Shared context for AI and teams
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Including team chats, GitHub, Slack, Notion, and AI agents
When agents work, Doco tells them what to keep in mind for their task at hand
Important decisions and chats are collected and shared
Seamlessly. Decisions happen in Slack threads, pull requests, Notion pages and chats with agents, then scatter. Doco collects them as they happen into one workspace, each as a record with its history.
Without being asked. Every brief opens with your workspace's constitution, which tells agents to record each decision as it is made: the question, the choice, and the alternatives that lost and why. Rewrite it in plain words.
MayaSMS codes get phished. Let's use passkeys.
Claude CodePasskeys it is. I'm recording the decision.
DecisionSecurity decisions
Switch providers at will, without losing any context or memory. Your team's context lives in Doco, not in one vendor's chat history, and every agent reads it through the same MCP server. Export it anytime.
One memory, whichever model is plugged in
Nodes and edges, like a brain. Every decision, rule and reference is a node with a history, and typed edges say what supports, constrains or replaces what, so retrieval follows the connections, not just the words.
Just the perfect amount of context, exactly when it is needed. Right before each prompt and each edit, an agent asks Doco what applies to the task and gets it in tiers that fit its budget.
Prompt
>Add passkey sign-in to invites
Doco's brief4,000 tokens
File edit
±app/routes/invite.tsx
Doco's brief4,000 tokens
Agents forget. Doco notices. When an agent ships code without Doco, or keeps reading but stops writing, you and the person it works for hear about it, with the message that sets it right.
Every spike, a record written
Codex reads, but hasn't written in 7 days.
Doco told you and Maya, with the fix.
4th
of 39 systems on EnterpriseRAG-Bench
Still unpublished
| #1 | codos.ai | 92.64 |
| #2 | Flexor.ai (+ GPT-6.1 Sol) | 91.48 |
| #3 | CDL (Causal Dynamics Lab) | 90.91 |
| #4 | Doco | 88.46 |
| #5 | AHOY Labs | 88.26 |
| ⋮ | ||
| #22 | OpenAI File Search | 61.03 |
| #26 | Amazon Q (Kendra) | 48.96 |
| #27 | Azure AI Search | 48.42 |
| #30 | Vertex AI Search | 41.87 |
EnterpriseRAG-Bench is Onyx's open benchmark of company knowledge: 500 questions over about 500,000 documents. Each answer scores its completeness when it is correct and zero when it is not. Doco's 88.46 is still unpublished: it comes from a pilot run on 10% of the dataset. The other scores are from the public leaderboard on October 29, 2026.