The knowledge layer between scattered company data and AI agents.
Stop stuffing your agent's prompt with noisy RAG chunks.
Brain OS turns Slack threads, emails, tickets and docs into atomic, attributable facts — reconciled when things change, served to your AI agents with provenance on every claim. Not a search box. Not a chunked index. A durable memory layer your agents load at startup.
What it does
Not chunks. Every fact is a self-contained proposition with a source, a quote, a confidence, a timestamp — the unit format the agent-memory literature has converged on.
When a fact changes, the old one is marked stale with a validTo and supersededBy. When two sources disagree, both are flagged disputed. Your agent never speaks from out-of-date state.
Pull the live skill file at agent startup, or query the brain by API. Per-agent scoping. Every claim the agent makes can cite its source.
Why not just RAG?
| Chunked RAG | Enterprise search (Copilot) | Brain OS | |
|---|---|---|---|
| Storage unit | Document chunks + embeddings | Whole documents | Atomic, attributable facts |
| When facts change | Silently re-retrieves whatever's in the index | Silently re-summarizes | Supersedes old fact, flags conflicts as disputed |
| Provenance | "Trust me" : chunk → answer | Citations on the answer | Source + quote + confidence + timestamp on every fact |
| Built for | Human-readable answers | Employees searching from a UI | Agents loading durable, attributable context |
| Deployment | Roll your own | SaaS-only, per-seat licensing | Self-host on one VM, BYO LLM (Claude or vLLM) |
Get started in 3 steps
- Go to Ingest →1Ingest a fragment of company knowledge
Paste any Slack thread, email, ticket, or doc. The model extracts atomic units with their source, evidence quote, and confidence.
- Open Map →2Watch reconciliation happen
Ingest a second source that updates or contradicts the first. Old facts get superseded; conflicts get flagged disputed. The Map shows the resulting entity graph.
- Get the skill file →3Load it into your agent
Export SKILLS.md and load it as your Claude or GPT agent's memory — or query the brain by API. Every answer the agent gives can cite the underlying fact.
Explore the rest
Export an executable SKILLS.md file — the version of your company an AI agent loads.
Connect a Slack workspace so brainOS can listen to channels and auto-answer threads.
If you're serving your own model on an AMD MI300X (or any vLLM endpoint), live throughput stats live here.
Text, file uploads, and image ingestion (screenshots of whiteboards, slides, diagrams).
Paste a thrashing agent transcript. BrainOS extracts the loop as a durable gotcha and adds it to SKILLS.md so the next agent skips it.
Start with one Slack thread your agent currently has no idea about. Paste it into Ingest, then ingest a second message that updates it. The reconciliation view will show the old fact superseded, the new one fresh, and the provenance preserved on both — that's the loop your agent needs.