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| id | channel | draft_type | status | approval_status | approved_by | approved_at | subject | body | task_id |
|---|---|---|---|---|---|---|---|---|---|
| draft_410b3a19dc1cdae9 | seed_pipeline_outreach | draft | not_approved | Maggie Basta, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Maggie, Your thesis that AI agents will use the web far more than humans ever did, the bet behind First Round's Parallel seed, is exactly the gap Parcle fills on the memory and context side. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_20c5bdb2e5c46b57 | |||
| draft_c06b565c87920d33 | seed_pipeline_outreach | draft | not_approved | Bill Trenchard, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Bill, Your applied-AI, enterprise-infra portfolio, Vercel, Linear, Warp, is the company Parcle wants to keep: we're the memory and context layer those agent and developer tools are still missing. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_6dcec6d69262bf62 | |||
| draft_eadd45529f1b950d | seed_pipeline_outreach | draft | not_approved | Helen Min, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Helen, Your enterprise-software and fintech pre-seed thesis, and infra bets like Metronome, line up closely with the agent-memory layer we're building at Parcle. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_56c3974c4d5f9944 | |||
| draft_c0665078794e3c97 | seed_pipeline_outreach | draft | not_approved | Pat Matthews, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Pat, Active's thesis on backing technical founders building AI-native software and infrastructure is exactly the lane Parcle sits in. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_3f3aab3c829eeafd | |||
| draft_c5a7614594d8ff08 | seed_pipeline_outreach | draft | not_approved | Nichole Wischoff, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Nichole, Your bet on the automation of the real-world economy and unsexy B2B infrastructure is where agent memory quietly becomes load-bearing. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_232f243db1d89d68 | |||
| draft_2184d65525bbfaa6 | seed_pipeline_outreach | draft | not_approved | Sergio Monsalve, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Sergio, Roble's future-of-work thesis around AI human enablement is a natural home for memory that lets work agents actually hold context. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For generalist seed investors, the bet is fast early revenue plus a large category: agent memory becomes a core layer under enterprise AI. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_fcae6b9545d44d56 | |||
| draft_ede6d850a0491f9d | seed_pipeline_outreach | draft | not_approved | Jenny Fielding, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jenny, You've been first money into 300+ companies across money and work, and Parcle is the memory layer those agentic work and fintech tools end up needing. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For fintech and regulated-workflow investors, Parcle is traceable memory infrastructure behind agents that need current context, provenance, and less repeated data loading. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_f4188388a0f65bc2 | |||
| draft_6d197862f12b146b | seed_top10_outreach | draft | not_approved | Jason, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jason, Your a16z piece, "Your Data Agents Need Context," is one of the closest public matches to what we build: that context layer, as durable memory agents can rely on. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_f3e032e57e693519 | |||
| draft_0ad7acee02151091 | seed_top10_outreach | draft | not_approved | David, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi David, Your Sequoia writing on AI infrastructure maps closely to what we build: the memory layer that sits between enterprise systems and agent workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_9c8b165f87847e8f | |||
| draft_6f6756e81352023f | seed_top10_outreach | draft | not_approved | Denise, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Denise, Gradient has been investing at the front of AI since 2017, and Parcle goes after the piece agent products still lack: memory that makes them reliable. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_ac94a36814dff1fa | |||
| draft_6c6c9e7f0deef1ec | seed_top10_outreach | draft | not_approved | Sruthi, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Sruthi, Forum is a B2B AI studio backing agents at pre-seed, and Parcle is the memory layer those agents need to work with real business context. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_7b504befde15a208 | |||
| draft_ddfe466d33fb9f91 | seed_top10_outreach | draft | not_approved | Grace, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Grace, Amplify's Spiral thesis says AI teams need better data platforms, and Parcle is the context layer those agent teams keep rebuilding by hand. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_46f8305857a69a15 | |||
| draft_ed54ca84318a02db | seed_top10_outreach | draft | not_approved | Christopher, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Christopher, AIX backs founders building alongside working AI practitioners, and coming from NLP you'll know the problem firsthand: agents lose the thread the moment context leaves the window. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_8106d0a3cd638925 | |||
| draft_c1ee6140a25f15d4 | seed_top10_outreach | draft | not_approved | Lauri, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Lauri, Your Bessemer focus on data, AI, and developer infrastructure is a clean match for Parcle's memory layer for enterprise agent workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_87eee5fa8aa7e6d5 | |||
| draft_eea0a6dc01f26152 | seed_pipeline_outreach | draft | not_approved | Andrew, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Andrew, AI Fund co-builds AI companies from real customer need to working prototype, and that is where we come in once those agents ship: they start from zero on every task. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_5e64830cfcc404dc | |||
| draft_832dc1251694583e | seed_pipeline_outreach | draft | not_approved | Hauke, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Hauke, AI.FUND backs early applied AI in Europe, which is our lane too: the memory and context layer agents need inside enterprises. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_eff250666b934134 | |||
| draft_c6395334d62093a5 | seed_pipeline_outreach | draft | not_approved | Ragnar, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Ragnar, Your AI.FUND writing on Europe's head start in applied AI points at the same practical bottleneck we work on: enterprise context and memory. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_9e760ce88793b010 | |||
| draft_7509ec8ecab1de8e | seed_pipeline_outreach | draft | stale | Ming | 2026-06-21T05:08:57Z | Christopher, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Christopher, AIX backs founders building alongside working AI practitioners, and coming from NLP you'll know the problem firsthand: agents lose the thread the moment context leaves the window. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_bb3944e9c03eff71 | |
| draft_9de19510113d04ab | seed_pipeline_outreach | draft | stale | Ming | 2026-06-21T05:09:59Z | David, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi David, Amplify backs infrastructure and developer tools, and Parcle lives in that same builder-facing layer: durable memory and context for agents across enterprise systems. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_514dbea871d5dfb1 | |
| draft_6334bbaca53ca724 | seed_pipeline_outreach | draft | stale | Ming | 2026-06-21T05:10:54Z | Grace, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Grace, Amplify's Spiral thesis says AI teams need better data platforms, and Parcle is the context layer those agent teams keep rebuilding by hand. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_9ed348b439f3dcf2 | |
| draft_1af5a45c89a16363 | seed_pipeline_outreach | draft | not_approved | Lenny, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Lenny, Your Amplify focus on devtools, data, and agent tooling points right at the missing piece we build: the memory underneath reliable enterprise agents. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_59b5073c76d0a4a7 | |||
| draft_7b61a999cc6ec068 | seed_pipeline_outreach | draft | not_approved | Sarah, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Sarah, You've backed the data and developer-tools stack deeply at Amplify, which is the intersection Parcle is built for. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_a1d5e24b86264c28 | |||
| draft_6ee354f45a569fac | seed_pipeline_outreach | draft | stale | Ming | 2026-06-21T05:11:13Z | Lance, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Lance, Bessemer's note on your work bridging AI theory and deployment names the gap we close: persistent context for agents once they hit production. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_3c0dc4f1a6f32035 | |
| draft_d8b5535309856b78 | seed_pipeline_outreach | draft | not_approved | Lauri, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Lauri, Your Bessemer focus on data, AI, and developer infrastructure is a clean match for Parcle's memory layer for enterprise agent workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_34977005b3b5f976 | |||
| draft_23485844410d816b | seed_pipeline_outreach | draft | not_approved | Sarah, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Sarah, Conviction's "Software 3.0" thesis is the frame we use too: AI-native software needs a persistent memory and context layer to be genuinely useful. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_54853f2f3ce5e402 | |||
| draft_0ac55582dabe83e1 | seed_pipeline_outreach | draft | not_approved | Greg, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Greg, Costanoa has backed early enterprise software for years, and Parcle is the context layer those enterprise agents need before they can own real workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_82bad86460338848 | |||
| draft_a14a8bc20699f48c | seed_pipeline_outreach | draft | not_approved | Tony, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Tony, You back data, AI, and developer infrastructure founders at the earliest stages, which is squarely where Parcle sits: memory infrastructure for agentic software. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_17f2081713edf08b | |||
| draft_ecf6db2d6fbf7b3c | seed_pipeline_outreach | draft | not_approved | Jake, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jake, Cyberhill's work on ontologies, knowledge graphs, and trustworthy enterprise AI is close to our thesis: a lightweight, agent-friendly memory and context graph for enterprise workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_ecb7d51423d130be | |||
| draft_835c2e2e97f276f8 | seed_pipeline_outreach | draft | not_approved | Matt, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Matt, Your MAD landscape has shaped how a lot of us read the data and AI stack, and we think enterprise agent memory becomes a new box on it. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_4118d314c675afe1 | |||
| draft_405402bf56e963fa | seed_pipeline_outreach | draft | not_approved | Sruthi, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Sruthi, Forum is a B2B AI studio backing agents at pre-seed, and Parcle is the memory layer those agents need to work with real business context. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_7db7e4efb86733c3 | |||
| draft_b5475c977938ee50 | seed_pipeline_outreach | draft | not_approved | Ashu, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Ashu, Your "Context graphs" essay is the clearest writeup I've read of the exact problem we work on: agents need durable decision traces and real business context. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_33cd3e04bae7dffa | |||
| draft_1486714567b1eaae | seed_pipeline_outreach | draft | not_approved | Jaya, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jaya, Your context-graphs thesis with Ashu maps straight onto our product: agents need a living source of truth for context, decisions, and workflow memory. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_ca14ed0e4232f66b | |||
| draft_73b7ca347d3a10a8 | seed_pipeline_outreach | draft | not_approved | Joanne, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Joanne, Your writing on glue functions as a wedge for AI agents is close to what we build: the memory layer that helps agents understand those workflow edges. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_109cc6ab691fab8d | |||
| draft_eaf0662da61ecef6 | seed_pipeline_outreach | draft | stale | Ming | 2026-06-21T05:11:26Z | Tao, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Tao, Golden Seeds backs scalable early-stage companies, and Parcle's angle is practical enterprise AI infrastructure that earns its way into daily workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_a189911ee2eaaba7 | |
| draft_4fff4a9b873aad20 | seed_pipeline_outreach | draft | not_approved | Denise, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Denise, Gradient has been investing at the front of AI since 2017, and Parcle goes after the piece agent products still lack: memory that makes them reliable. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_d7e34cb4801534d4 | |||
| draft_1edf991f991c6810 | seed_pipeline_outreach | draft | not_approved | Jerry, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jerry, You've backed cloud infrastructure and SaaS for years at Greylock, and Parcle is infrastructure for the next layer, where agents need memory across tools. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_cefc2f24b314faf4 | |||
| draft_8237ce0bf1abb6d4 | seed_pipeline_outreach | draft | stale | Ming | 2026-06-21T05:11:42Z | Saam, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Saam, Your Greylock focus on data and ML infrastructure and applied AI is right where Parcle sits: memory infrastructure for enterprise agents. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_b06237e9156ca497 | |
| draft_11fc81f75673f861 | seed_pipeline_outreach | draft | not_approved | Shreya, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Shreya, Your Greylock focus on AI, infrastructure, and developer tooling fits what we build: a trustworthy memory layer for agentic enterprise software. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_9ff9200b15db4dc2 | |||
| draft_1c6c30a1d6fbfc37 | seed_pipeline_outreach | draft | not_approved | Jesse, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jesse, Your Heavybit thesis on AI moving developer tools from automation to delegation names the shift we build for: delegated agents need shared memory. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_e87401fbf577e7f3 | |||
| draft_396ba2178a35eb9f | seed_pipeline_outreach | draft | not_approved | Joseph, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Joseph, Heavybit backs practitioner-first devtools and AI coding, and Parcle is the context layer underneath agent builders' work. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_79ddc44188f399a2 | |||
| draft_e1348ffccf95a1d2 | seed_pipeline_outreach | draft | not_approved | Tom, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Tom, Your focus on developer tools, cloud infrastructure, and data services is a strong match for what we build: agent-memory infrastructure for the enterprise. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_262fab4a14452663 | |||
| draft_53aabe2c6ffc9868 | seed_pipeline_outreach | draft | stale | Ming | 2026-06-21T05:12:15Z | Bucky, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Bucky, Lightspeed's "infrastructure reimagined" thesis fits our bet that memory becomes core infrastructure for enterprise agents, not a nice-to-have. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_b4f8e6879c1c08a7 | |
| draft_2033a952be9114e4 | seed_pipeline_outreach | draft | not_approved | James, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi James, Your Lightspeed focus on AI and infrastructure connects directly to Parcle: the persistent context layer that sits above the model. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_1d92563894a089e3 | |||
| draft_ee6921ba30e90fb7 | seed_pipeline_outreach | draft | not_approved | Brandon, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Brandon, Lux has leaned into AI-teammate companies like Cognition, and Parcle goes after the unglamorous part that makes those teammates reliable: durable memory across their work. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_5b62f5f4df44352d | |||
| draft_07258135ece79be0 | seed_pipeline_outreach | draft | not_approved | Lan, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Lan, Lux backs data and ML infrastructure and companies like Applied Compute building in-house agent workforces, which is who we serve: durable memory for those enterprise agents. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_379677feca437b79 | |||
| draft_44af4aa304aa4662 | seed_pipeline_outreach | draft | not_approved | Akash, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Akash, MXV backs the next generation of B2B enterprise cloud companies, and the AI-native ones keep hitting the same wall: their agents can't remember anything across customer workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_f230d93ed6137bba | |||
| draft_3e0c2f13243dcf4f | seed_pipeline_outreach | draft | not_approved | Deedy, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Deedy, Your Menlo focus on early AI/ML, next-gen infrastructure, and enterprise software is the intersection Parcle is built for. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_4fd8bdbe118a4b9f | |||
| draft_ff57344b12b5d165 | seed_pipeline_outreach | draft | not_approved | Tim, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Tim, Your Menlo focus on AI/ML and the new data stack is why Parcle is relevant: agents need a memory layer on top of that stack. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_93454ff2a8f62eda | |||
| draft_65cf892de4c37c68 | seed_pipeline_outreach | draft | not_approved | Venky, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Venky, Your Menlo focus on AI, cybersecurity, and infrastructure fits what we build: an enterprise-grade context layer so agents run on governed memory. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_3ef660930b726c29 | |||
| draft_410ac304195b50ba | seed_pipeline_outreach | draft | not_approved | David, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi David, Your Sequoia writing on AI infrastructure maps closely to what we build: the memory layer that sits between enterprise systems and agent workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_821bbfdf820b6abb | |||
| draft_607a9a62aab8a41f | seed_pipeline_outreach | draft | not_approved | Julien, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Julien, Your Sequoia work on AI agents for supply chains and AI-native finance lines up with what we build: the shared memory those enterprise agents need. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_1521bdf3cdfad2d4 | |||
| draft_9f814d39b4c3504e | seed_pipeline_outreach | draft | not_approved | Stephanie, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Stephanie, Your early-stage AI work at Sequoia treats infrastructure as the thing that matters, and we treat agent memory the same way: as enterprise infrastructure. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_25568d0fd779e09c | |||
| draft_b3f587504bd39b80 | seed_pipeline_outreach | draft | not_approved | Frederik, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Frederik, Speedinvest's AI and Infra team backs agent platforms, and Parcle is the persistent context layer those platforms keep needing. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_8c5d1e3c1e6df7d3 | |||
| draft_cdcd27c892659234 | seed_pipeline_outreach | draft | not_approved | David, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi David, TenOneTen's roots go back to foundational data infrastructure like Common Crawl, and your recent writing on AI agents points at the same gap we close: agents working against messy business systems with no memory. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_bf1fd1ca9a296f85 | |||
| draft_0c79be34f946f206 | seed_pipeline_outreach | draft | not_approved | Tomasz, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Tomasz, Your writing on data and AI infrastructure fusing, and on context retrieval for agents, is almost exactly the market frame behind Parcle's memory layer. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_b61de2a04f3a91c8 | |||
| draft_4ef6e7df1c0a3937 | seed_pipeline_outreach | draft | not_approved | Josh, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Josh, Version One is good at backing new categories early, and we think agent memory becomes one: infrastructure for software that remembers. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_09af3ffca1445f9c | |||
| draft_56f9a43646dc479a | seed_pipeline_outreach | draft | not_approved | Ben, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Ben, Vertex's writing on AI reshaping every industry lines up with where we focus: the infrastructure that lets enterprise agents work from real operating context. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_048162257d6f3c92 | |||
| draft_8417b981a05b691d | seed_pipeline_outreach | draft | not_approved | Chris, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Chris, Wing backs AI-first infrastructure with a production and governance lens, which is the bar we build Parcle's memory layer to clear. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_639f3b8e0f51bb2d | |||
| draft_b9c842895b3baea7 | seed_pipeline_outreach | draft | not_approved | Peter, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Peter, Your "State of AI in the Enterprise" point, that the bottleneck is now governed, production-grade systems, is exactly what we build toward: a context layer that makes enterprise agents governable and useful. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_5a46bebdd8d876a5 | |||
| draft_c6def66367a44e40 | seed_pipeline_outreach | draft | not_approved | Jessica, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jessica, Work-Bench's Fund IV targets AI/ML, infrastructure, and enterprise applications, the exact stack Parcle's memory layer plugs into. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_2d8f61cc5cf10afe | |||
| draft_6195af698a558a80 | seed_pipeline_outreach | draft | not_approved | Jonathan, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jonathan, Work-Bench led Artian's seed for reliable autonomous agents at enterprise scale, and reliability is exactly what we're after: agents that remember instead of starting over. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_e52158a0109d01f4 | |||
| draft_94182c21ea1cf09f | seed_pipeline_outreach | draft | not_approved | Kelley, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Kelley, Your Work-Bench focus on security and cloud-native infrastructure is the bar Parcle builds to: a context layer enterprise agents can use without losing governance. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_7cd241d3536c17e6 | |||
| draft_65fc7d4b8fc20b7e | seed_pipeline_outreach | draft | not_approved | Brianne, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Brianne, Worklife writes the first check into technical founders changing how work gets done, which is exactly who we build for: agent memory for work inside companies. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_b238b1d1017d5521 | |||
| draft_0f641e2b591448fb | seed_pipeline_outreach | draft | not_approved | Jocelyn, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jocelyn, Zetta backs founders putting ML and AI into cloud infrastructure and data systems, which is right where Parcle sits: memory infrastructure for enterprise agents. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_e0af76310fc99eb3 | |||
| draft_dc9e829cebb17e01 | seed_pipeline_outreach | draft | not_approved | Jason, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jason, Your a16z piece, "Your Data Agents Need Context," is one of the closest public matches to what we build: that context layer, as durable memory agents can rely on. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_27d4eac80d975cf4 | |||
| draft_5683abf6856b99a6 | seed_pipeline_outreach | draft | not_approved | Jennifer, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Jennifer, Your a16z context-layer thesis with Jason lands on the same point we build around: enterprise agents need business definitions, data context, and workflow memory to be useful. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_53838a8baa2bb264 | |||
| draft_c0daa7ef3c9807d1 | seed_pipeline_outreach | draft | not_approved | Matt, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Matt, Your a16z work on AI and data systems connects directly to Parcle: the context and memory layer that sits above enterprise data. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_265a9a52e8ecd851 | |||
| draft_bac4926b5e43493f | seed_pipeline_outreach | draft | not_approved | Ed, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Ed, boldstart writes the first check into technical founders, and your autonomous enterprise thesis needs the piece we build: memory agents can actually run on. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For AI infrastructure and devtools investors, the bet is that agent memory becomes core infrastructure, not a feature: the layer agents read from and write back to as they work. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_3839a85f34687b15 | |||
| draft_ecca365fd6990806 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Sales agents reset too often. Parcle is focused on durable shared context that can make digital workers feel less generic and more useful. | task_83f547218b0a54bd | ||||
| draft_a88bfb0ee95a359c | outreach_email | draft | not_approved | Winston and Gabriel - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Harvey's legal workflows are a strong example of why agents need governed memory. Parcle is building persistent context with provenance so teams do not rebuild matter history and document context every run. Open to a short infrastructure conversation? | task_f5544b410a430190 | ||||
| draft_58de0203e8011b47 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Professional-services agents need memory that is traceable and permissioned. That is the angle I wanted to compare. | task_1224012a596b0a27 | ||||
| draft_8a8ecaaea67ece4d | outreach_email | draft | not_approved | Jesse and Ashwin - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Decagon's AI concierge is a strong Parcle fit. We are building persistent context for agents so customer history prior resolutions product knowledge and escalation state survive across channels. Worth comparing notes? | task_3734abafab45bbf2 | ||||
| draft_aa49fbe1f62e78a4 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Support agents need durable memory to move from deflection to real customer experience. That is the Parcle wedge. | task_a58db7f64a0c14d0 | ||||
| draft_66f781599aa03e0b | outreach_email | draft | not_approved | Bret and Clay - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Sierra's public agent-memory framing is very aligned with Parcle. We are building a governed memory layer for agents so customer context signals and prior actions are reusable and inspectable. Worth a short infrastructure conversation? | task_e440d7216d51babc | ||||
| draft_88971bfa8619144f | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. The public Sierra product already points at the core Parcle thesis: better customer agents need durable memory and real-time context. | task_ea88d9a9cb6451f9 | ||||
| draft_deee73f90300ec5b | outreach_email | draft | not_approved | Munjal - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Hippocratic AI's patient-facing agents show why memory and governance matter. Parcle is building persistent agent context with provenance and handoff history. If your team is exploring memory infrastructure for longitudinal workflows I would be glad to compare notes. | task_566652bcd3107aab | ||||
| draft_b72a389b3c61cde0 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Healthcare agents need continuity without losing control. That is the specific Parcle angle. | task_d8979c30c116ab47 | ||||
| draft_13b11b514e8d3b73 | outreach_email | draft | not_approved | Scott - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Devin's direction makes persistent engineering context central. Parcle is building memory for agents so repo history decisions team norms and prior fixes carry across runs. Worth comparing notes on agent memory for software teams? | task_8ed817132ec3537c | ||||
| draft_4259114d8c101349 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Coding agents waste a lot of time reacquiring context. Parcle is focused on making that context durable and reusable. | task_93724e1735a09b72 | ||||
| draft_1a09495876777b84 | outreach_email | draft | not_approved | John - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Norm's supervisory AI thesis is close to Parcle's memory layer work. We are building persistent governed context for agents with provenance and auditability. Would be useful to compare notes on memory for regulated workflows? | task_ad12db41f80e9996 | ||||
| draft_f583d876aad5b3d8 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Compliance agents need memory that can be inspected and trusted. That is exactly the Parcle angle. | task_c91002e1f2023694 | ||||
| draft_7aa42c6b2803dcef | outreach_email | draft | not_approved | Nicolas - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Rillet's AI-native ERP and agent marketplace point at a big context problem: finance agents need memory of policies prior closes exceptions and approvals. Parcle is building governed agent memory for exactly this kind of recurring workflow. Worth a quick compare notes chat? | task_1c1af57649fe70ce | ||||
| draft_de8cdbef42369c02 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Accounting agents become more valuable when they remember decisions across periods instead of treating each close as new. | task_9c89f394846f5684 | ||||
| draft_bd55dd558d4a4337 | outreach_email | draft | not_approved | Nami - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. ClaimGlide's prior-auth workflow is a strong agent-memory use case. Parcle is building persistent context so agents can remember payer patterns prior cases approvals and practice-specific workflows. Open to a short founder chat? | task_59ad50740e324b28 | ||||
| draft_aa5d26c53e1463d5 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Prior-auth automation gets stronger when the agent remembers what worked for similar cases and keeps the evidence trail. | task_f0f4187e8307c37f | ||||
| draft_488b5fbd57d21aa1 | outreach_email | draft | not_approved | Felix and Niclas - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Lunavo's carrier operations agents are a strong Parcle fit. We are building persistent memory for agents so customer rules dispatcher feedback exception history and document context carry across workflows. Worth comparing notes? | task_e827f6d3cf0b2723 | ||||
| draft_3ffcdc085425d418 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Logistics ops has exactly the kind of tribal context agents need to remember. Parcle can help make that memory durable and inspectable. | task_8b5145f552a64004 | ||||
| draft_c1bf9db630641753 | outreach_email | draft | not_approved | Felipe and David - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Denki's audit-agent workflow maps well to Parcle. We are building persistent governed memory for agents so controls evidence walkthrough notes and prior exceptions stay available with provenance. Worth a quick compare notes call? | task_98db21b48312b3a5 | ||||
| draft_46d05446652bf7d7 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Audit agents need memory that can be defended. That is the Parcle wedge. | task_8cbb64a4b9d35ec6 | ||||
| draft_89dcd56fa786214b | outreach_email | draft | not_approved | Anas and Pierre - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Bravi's front-office agents are a strong Parcle fit. We are building persistent memory for agents so lead context product specs quote history and follow-up state carry across calls chats and internal workflows. Open to comparing notes? | task_5b53c958a05c1e15 | ||||
| draft_8ddac3ae0761b47e | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Home-service AI agents win when they remember the customer and the company workflow. That is what Parcle is building. | task_f865f6b27d1ad114 | ||||
| draft_f40472fed8b2f679 | outreach_email | draft | not_approved | Lucas and Linus - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Brickanta's construction agents are a great example of where Parcle can help. We are building persistent context for agents so project docs standards templates decisions and prior estimates stay available across workflows. Worth a quick chat? | task_95062299374480e3 | ||||
| draft_7e1ebc2a46055478 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Construction agents have to carry huge project context without losing provenance. That is the memory layer Parcle is building. | task_383c2f38f6f22108 | ||||
| draft_e2b4fffeed0d2ad9 | outreach_email | draft | not_approved | Dmitry and Elvira - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Fastshot's multi-agent app builder is a strong Parcle use case. We are building persistent memory for agents so product decisions design preferences and implementation context carry across planning coding and deployment. Worth comparing notes? | task_242c78e49b6ddf72 | ||||
| draft_1d9028e5f17926ec | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. App-building agents need durable project memory to avoid brittle one-off scaffolds. That is where Parcle may help. | task_99c88b9330e6c575 | ||||
| draft_c52e306b9ce6e83d | outreach_email | draft | not_approved | Andres and Akshay - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. item's agentic CRM direction overlaps deeply with Parcle. We are building persistent read-write memory for agents so customer history source records follow-ups and decisions carry across tools. Would love to compare notes founder to founder. | task_cbb99134e6a0feb1 | ||||
| draft_f05952473e638cb9 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Agentic CRM lives or dies on durable customer memory. Parcle may be useful as infrastructure or at least as a strong design conversation. | task_ef4d6d3dff6e2949 | ||||
| draft_ca6ffa9ea70ba4a7 | seed_top10_outreach | draft | not_approved | Alex, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Alex, The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For generalist seed investors, the bet is fast early revenue plus a large category: agent memory becomes a core layer under enterprise AI. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_2e3e2d38c40310a8 | |||
| draft_989da9145417af6b | seed_top10_outreach | draft | not_approved | Brett, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Brett, The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For generalist seed investors, the bet is fast early revenue plus a large category: agent memory becomes a core layer under enterprise AI. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_dc4805f57ddd30db | |||
| draft_df0889bcda27d454 | seed_top10_outreach | draft | not_approved | Sara, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Sara, The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For generalist seed investors, the bet is fast early revenue plus a large category: agent memory becomes a core layer under enterprise AI. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_5dc5b3064e10d035 | |||
| draft_e3e9b9a66433d4fc | seed_pipeline_outreach | draft | not_approved | Ingo, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Ingo, Your AI.FUND piece on applied AI as Europe's edge points right at what we build: the memory that lets enterprise agents work from real, current business context. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For enterprise SaaS investors, this is about agent adoption inside companies: agents cannot own real workflows without current company memory. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_fb0ce1ed072aa205 | |||
| draft_138ab887285c3aea | seed_pipeline_outreach | draft | not_approved | James, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi James, Your case that AI agents are becoming the operating system of the enterprise is the world we build for, and that operating system needs memory and governed context to run in production. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For enterprise SaaS investors, this is about agent adoption inside companies: agents cannot own real workflows without current company memory. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_bdd554654e9529a9 | |||
| draft_39d4bfeb95dc494c | seed_pipeline_outreach | draft | not_approved | Michael, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Michael, Bee's writing on the AI supercycle and inception-stage bets fits where we are: agents need memory before enterprises trust them with real workflows. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For enterprise SaaS investors, this is about agent adoption inside companies: agents cannot own real workflows without current company memory. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_fc9f036080873d8a | |||
| draft_2bc7b9c27da618fd | seed_pipeline_outreach | draft | not_approved | Ariel, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Ariel, Bessemer has been early to call data infrastructure the next big AI-native opportunity, and Parcle is building right at that line for vertical enterprise agents. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For fintech and regulated-workflow investors, Parcle is traceable memory infrastructure behind agents that need current context, provenance, and less repeated data loading. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_330763d26cb59279 | |||
| draft_5bbe7050213577ef | seed_pipeline_outreach | draft | not_approved | Matt, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Matt, Your focus on AI, developer tools, and cloud infrastructure at Menlo is the stack Parcle is built for: memory and context infrastructure for agents. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For fintech and regulated-workflow investors, Parcle is traceable memory infrastructure behind agents that need current context, provenance, and less repeated data loading. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_702a404540503626 | |||
| draft_02d1566eb6a292fe | seed_pipeline_outreach | draft | not_approved | Martin, Introducing Parcle Real-Time Agent Memory - $5M Seed Raise | Hi Martin, You've spent years on cloud and AI infrastructure at a16z, and Parcle is the agent-memory layer that stack now needs. The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Here's a quick summary about Parcle: - Parcle is building the memory/context layer for AI agents. - The model layer is getting better fast, but agents still break when they do not have business context across docs, email, Slack, CRM, databases, calendars, and prior agent work. - Parcle connects that persistent memory to agents in real-time, allowing agents to retrieve relevant context when needed and saving 70% in token consumption. Early benchmarks: - 70% lower token usage and 50% faster task completion in Claude Code/Codex workflows. - Memory evals include 97% BirdBench, 93.6% LoCoMo, and 95.6% LongMemEval. For fintech and regulated-workflow investors, Parcle is traceable memory infrastructure behind agents that need current context, provenance, and less repeated data loading. We're raising a $5M SAFE with a 20% discount to the next priced round; $1.5M is committed and $3.5M remains open. I think there could be a real fit here if the team agrees after review. If there's enough internal interest, happy to jump on a call to discuss. Book a meeting: https://calendly.com/ming-parcle/30min Read our deck: https://investors.parcle.ai/P4rcPCweqd Regards, Ming | task_991d7914b246353d | |||
| draft_5151e08b56060eff | outreach_email | draft | not_approved | Andrew - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Cofounder 2 is exactly the kind of product where shared memory becomes the product surface. Parcle is building a read-write context layer for agent teams so engineering sales marketing and ops agents can carry company history customers and decisions across runs. Worth a short founder-to-founder compare notes call? | task_f39f245f56a92bbe | ||||
| draft_e2a944e1f736c6eb | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Cofounder is already selling the world Parcle cares about: agents doing real company work. If you are pressure-testing memory across departments I can share what we are seeing from our own CRM and agent workflow stack. | task_a5388ab77c92d84f | ||||
| draft_6b6ed7857e6510fb | outreach_email | draft | not_approved | Fryderyk - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Viktor feels closest to the future Parcle is building toward: an AI hire that knows the company and does real work. Parcle is focused on persistent governed memory across agents and tools. I would love to compare notes on how you are handling company context memory and approvals inside Slack and Teams. | task_3df0c233eea9f3ee | ||||
| draft_7d1afc2d84b9d9a2 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. The specific angle is not another workflow builder. It is durable shared context for AI employees so the coworker can remember the company without leaking control. | task_c588d672e25d2ec5 | ||||
| draft_90c249085307fa15 | outreach_email | draft | not_approved | Devi - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Yutori's AI chief-of-staff direction maps closely to Parcle. We are building a governed memory layer for agents so task history preferences approvals and context survive across sessions. If you are exploring how web agents remember users safely I would love to compare notes. | task_f80031799c1c750f | ||||
| draft_732bb7b6a3375688 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Personal web agents will live or die on memory quality. Parcle may be useful as infrastructure or at least as a strong technical conversation. | task_4b65d619cc3810a2 | ||||
| draft_46ca155d05c2b0d7 | outreach_email | draft | not_approved | Connor and Rajiv - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Caddy's promise that context and voice stay intact is exactly where Parcle can help. We are building a persistent memory layer for agents so user preferences prior work approvals and recurring workflows carry across apps. Open to comparing notes founder to founder? | task_ef47499d1d4b941b | ||||
| draft_118a386df680bb9d | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Caddy and Parcle are attacking the same context switching problem from different layers. I think there may be a useful infrastructure conversation around durable memory. | task_f7308f33c481273a | ||||
| draft_b472ffc8585d6d3e | outreach_email | draft | not_approved | Chloe - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Bond's AI Chief of Staff is the kind of product where memory quality becomes trust. Parcle is building persistent governed context for agents across tools like Slack GitHub Notion and Salesforce. Would be useful to compare how you are handling company memory and provenance? | task_1a95a03ab5b11a34 | ||||
| draft_837432426a1495f1 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. The CEO brief use case is one of our strongest Parcle wedges: agents need to know what changed and why without making the founder chase context. | task_dcd040e1c4204e56 | ||||
| draft_ec146224b1f65d92 | outreach_email | draft | not_approved | Rohan and Anton - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Supafax's inbox and calendar agent is a strong fit for Parcle. We are building durable memory for agents so preferences relationships follow-up state and approval history survive across email workflows. Worth a quick compare notes call? | task_b572cbb6a471b75d | ||||
| draft_c46393753fbf86bb | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Inbox agents need more than retrieval: they need memory of how the user handles people and recurring situations. That is exactly Parcle's wedge. | task_fbb675342e7364f7 | ||||
| draft_0eeb10cf3976970e | outreach_email | draft | not_approved | Sam and Anushka - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Logical's desktop agent pitch is all about carrying context across apps. Parcle is building a persistent memory layer for agents so user intent workflow history and approvals can survive across sessions without brittle prompt handoffs. Would love to compare notes. | task_22f85a0aed257a67 | ||||
| draft_8d6fd63b476e5556 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Proactive desktop agents are only as good as the memory they can safely keep. Parcle may be relevant to the context layer behind Logical. | task_cd277a7ce95b7ac4 | ||||
| draft_b41c43ea8c163708 | outreach_email | draft | not_approved | Pranav and Farhan - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Meteor's browser-agent direction is a strong fit for Parcle. We are building durable memory for agents so preferences prior workflows approvals and task outcomes persist across sessions. Worth a short founder-to-founder chat? | task_53a90375896c772c | ||||
| draft_a87b9d66b77c6080 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Browser agents can do more when they remember the user and the workflow. Parcle is focused on making that memory governed and reusable. | task_ac1e1d537fb6fb12 | ||||
| draft_10231b56a14aa532 | outreach_email | draft | not_approved | Nizar and Adam - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Orchid's iMessage EA is exactly the kind of agent that needs durable personal memory. Parcle is building governed memory for agents so preferences relationships approvals and recurring tasks carry across sessions. Open to compare notes? | task_4be777ccbfc6d216 | ||||
| draft_5d79e56d0457d0d6 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. The approval loop is a natural fit for Parcle: remember what the user wants while keeping a record of what was approved. | task_33042605dcf505e0 | ||||
| draft_742d6906befe2e24 | outreach_email | draft | not_approved | Allen - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Friday's assistant depends on remembering how a person handles people and recurring email patterns. Parcle is building a memory layer for agents that keeps preferences relationship context and approval history durable. Worth comparing notes? | task_95ec466d9b0a0536 | ||||
| draft_467d5cca0ed26e2a | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Inbox automation is a high-signal Parcle use case. We are focused on memory that helps agents act like they know the user without guessing. | task_045e8efb012712b9 | ||||
| draft_3d0e051ae7705b4d | outreach_email | draft | not_approved | Braden - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Epicenter's local-first shared memory direction is very close to Parcle's worldview. We are building persistent agent memory with an emphasis on retrieval provenance and write-back across tools. Would be useful to compare notes on memory schema and agent access patterns? | task_6f35a14c94a0ee69 | ||||
| draft_42821967212fec2e | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. This is less a vendor pitch and more a memory-architecture conversation. I think Parcle and Epicenter may have useful lessons for each other. | task_4e6f5d5d48dba386 | ||||
| draft_c8071dddb7bee736 | outreach_email | draft | not_approved | Andrew - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Serno's multi-agent expert workflow hits a Parcle pain point: agents need shared memory of what they already debated decided and learned about the user. Parcle is building that persistent context layer. Open to a quick compare notes call? | task_ceedd849c27ca356 | ||||
| draft_4ac947666c82a8b1 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Decision tools get much more valuable when they remember prior tradeoffs and user context. That is the Parcle wedge. | task_ddf7edd528f957bb | ||||
| draft_1f3f5fdee568d003 | outreach_email | draft | not_approved | Dara - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Delphi's digital mind product is a direct memory problem: it has to scale a person's expertise while staying current and faithful. Parcle is building a governed memory layer for agents and expert workflows. Would love to compare notes on memory provenance and updates. | task_7a7172bb6c6f8a74 | ||||
| draft_ea8cb03ad8848114 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Digital minds need trust in what they remember and when that memory changed. That is where Parcle may be useful. | task_058a41dcfad318b1 | ||||
| draft_4d4e09a028dafaf4 | outreach_email | draft | not_approved | Chris and Sam - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Granola is becoming the memory layer for meetings. Parcle is building persistent context for agents across tools so meeting notes can turn into follow-ups CRM updates and remembered decisions. Worth comparing notes? | task_7e165703527d86dd | ||||
| draft_b431e787fe59458b | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. The bridge from meeting memory to agent action is a strong Parcle use case. I would like to share what we are building. | task_e846ea47267f63c8 | ||||
| draft_f93b340413b41905 | outreach_email | draft | not_approved | Jaspar - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. Artisan's AI employee model is a sharp fit for Parcle. We are building persistent memory for agents so account history prospect context objections and prior follow-ups carry across campaigns. Worth a short founder-to-founder chat? | task_f09feefc7a05d54b | ||||
| draft_4872ad9afd562796 | followup_email | draft | not_approved | Quick follow-up: The problem we’re solving at Parcle is simple: We stop AI agents forgetting. AI BDRs are only useful when they remember accounts and do not reset every sequence. That is a core Parcle problem. | task_cb9750e45a07109f | ||||
| draft_0b20f48510284ee3 | outreach_email | draft | not_approved | Prabhav and Hasan - The problem we’re solving at Parcle is simple: We stop AI agents forgetting. 11x's digital worker model is exactly where Parcle can help: persistent memory for account context objections prior outreach and CRM signals across agents. Worth comparing notes on making GTM workers more context-aware? | task_dce6f3dd518543c2 |