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Organizational Memory 2.0 - Your enterprise just started thinking.

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AI models start from zero every session, burning over 50% of enterprise token budgets on context retrieval. OM2 is the organizational memory that continuously learns your business across 50+ connectors, retiring outdated facts as new ones arrive. Your existing AI becomes 9x cheaper and 64% faster, with answers preferred 84.5% of the time. Pair OM2 with Optimized Routing for 51x cost savings. Works in Claude, ChatGPT, Gemini, Perplexity, or custom agents via MCP or API.

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Hi Product Hunt! 👋

We're live with Organizational Memory 2.0 today.

Every time you ask AI about a customer or a project, it re-reads your entire company from scratch: Slack, email, CRM, Jira, Google Docs, just to answer one question. More than half of enterprise token spend isn't going toward the answer. It's going toward the search for it.

OM2 fixes that. Connect your tools once through MCP, across 50+ connectors, and OM2 starts learning the shape of your business in the background: your customers, your projects, the people behind them. It's a neural graph, so it keeps rewiring itself as new information comes in and retires facts once they go stale. The first time you ask a question, it figures out where to look. After that, it already knows.

We benchmarked Claude running on OM2 against Claude using off-the-shelf connectors: same models, same tasks, only the context changed. Result: 9x cheaper, 64% faster, and preferred on quality 84.5% of the time. Pair OM2 with our Optimized Routing and the cost savings climb to 51x.

OM2 works with whatever AI you're already using: Claude, ChatGPT, Gemini, Perplexity, or your own custom agents, via MCP or API.

Coworker starts learning the moment you connect it. Stop paying rent on your context. Start owning it.


Read more about OM2 in our launch blog post! 👇

Nigel

Maker

Dhruv from the Coworker team. I'll be around all day if anyone has questions on the benchmarks or how the MCP setup works in Claude or Cursor.

The thing that surprised me most building this out: the biggest gains weren't on the flashy stuff, they were on Jira, GitHub and Slack lookups, where agents normally rebuild the same query every single time. That's where the 89% cost drop came from.

Full methodology is in the if you want to poke at the numbers.