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 OM2 is the organizational memory that continuously learns your business across 50+ connectors, retiring outdated facts as new ones arrive.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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Coworker AI
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! 👇 https://coworker.ai/blog/introducing-om2
Nigel
@kohnigel what happens when the AI makes a wrong decision in a critical workflow ?
Coworker AI
@dipanshu_kushwaha5 that's what we're solving for with OM2 - it's significant'y more accurate than just plugging in regular connectors in Claude/ChatGPT. we've also built in approval workflows to the agents/decisions that matter and won't be actioned unless you approve it
Coworker AI
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 blog post if you want to poke at the numbers.
The thing for me that is exciting is that you can run a benchmark report of OM2 against your existing Claude infrastructure using MCPs vs using just OM2. Proving out that 9x is crucial and easy to do.
Coworker AI
@michael_saeta1 we'll work alongside you and run a benchmark report so you can understand how effective OM2 can be for your org
incredible work! cant wait to read more in the blog
Coworker AI
@hilarie_zombek yes!!
Super exciting launch for @Coworker AI!
Coworker AI
@teddy_butz1 thanks to you!
Macaly
context retrieval burning half the token budget is very real 😅 memory layer is the right fix. selling to enterprise or bottom up?