Every organization runs on knowledge that never gets written down — the decision made in a meeting, the fix found at 2am, the workaround only one person knows. It lives in conversations, tickets, and people's heads, and it walks out the door when they do. DocBrain captures that knowledge where it's created, connects it across the tools you already use, and keeps it accurate. So expertise survives turnover, new people ramp in weeks instead of months, and your teams stop re-solving problems they already solved.
The hidden cost
It isn't a documentation problem. The knowledge that runs your organization was never written down in the first place — it lives in conversations, tickets, and the heads of your most experienced people. Every day, some of it disappears.
The engineer who designed the system, the operator who knows the workaround, the lead who remembers why. When they go, decades of hard-won context walk out with them — and no wiki ever captured it.
Onboarding runs on interruptions. Every new hire re-asks questions someone already answered, because the answers were never captured anywhere they can find them.
A team hits an issue another team fixed last quarter, and burns days rediscovering the answer — because the knowledge was fragmented across tools nobody thinks to search.
And it's getting more expensive. People change roles faster, teams work across time zones, and AI now produces work faster than any human can absorb the reasoning behind it. The gap between what your organization does and what it remembers is widening every quarter.
Why the old way fails
You've already bought places to store knowledge. The problem is what they can capture — and what they can't.
Existing tools index what was written down. The knowledge that actually runs your organization never was. That's the layer DocBrain builds.
How It Works
Knowledge captured where it's created, connected into one memory, preserved with its context, and kept accurate over time — capturing knowledge at the source instead of documenting after the fact.
Knowledge is captured the moment it's created — from the conversations, tickets, incidents, and work systems your teams already use. Nothing to write. No wiki to maintain.
Every fragment is linked into a single connected memory — by topic, by who knows it, by what depends on what. Not a pile of documents; a graph you can ask.
Decisions, fixes, and the reasoning behind them are kept with their provenance — so the why survives long after the person who knew it has moved on.
Freshness is tracked continuously. When something goes stale or two sources contradict, DocBrain flags it — so you can trust what the memory tells you.
Other tools organize the knowledge you already wrote down. DocBrain captures the knowledge you didn't — the decisions, fixes, and expertise that only ever lived in a conversation or someone's head — and turns it into memory your whole organization can draw on.
Result: a living memory grounded in real work, kept current, without asking anyone to stop and write docs.
On demand · docbrain generate
docbrain generate drafts a runbook, a postmortem, an onboarding guide, or a reference — grounded in your organization's own memory: your incidents, tickets, decisions, and past work, with per-claim provenance. A general AI model writes fluent, generic prose. generate writes what is true for you, or tells you honestly what it can't answer. Run it from your terminal, or wire it into your pipeline so documents update themselves as work happens.
stdout is the markdown (pipe-clean). Diagnostics go to stderr. Swap --source notes.md for --source-url to ground in a Confluence page, Jira issue, Slack thread, or GitHub PR.
Drop --out and the exit code becomes a gate: generate exits non-zero on error-severity quality violations, so a sub-bar doc fails the build. If a named source can't be fetched, the whole run hard-fails — never a doc from a partial set.
Reads your real PRs, runbooks, incidents, and threads through your connectors. Every section is attributed to the source it came from — reviewers verify, they don't trust.
When the knowledge isn't there, it emits needs_input — the open questions — instead of fabricating. You fix gaps on purpose, not mid-incident.
Secret/PII redaction, hostname scrub, injection-quarantine, and a 0–100 quality score run on every draft. The exit code is your CI signal — "good enough to publish" becomes machine-checkable.
Access Everywhere
One memory, many ways in. Ask from chat during an incident, from your editor while you work, from the terminal, or from the web dashboard — everyone drawing on the same shared memory.
git log --oneline production..mainkubectl rollout undo deployment/payment-processorcurl -sf https://api.example.com/health/paymentsCoding agents · Claude Code & Cursor
Engineers now solve their hardest problems inside agent sessions — and that knowledge dies when the terminal closes. DocBrain's MCP tools give your agent a write path: it checks whether the org already knows the fix, drafts a capture, and asks you before filing it. Three lines in your CLAUDE.md:
Nothing uploads automatically — the agent proposes, you approve, and the capture lands in the normal review queue. The client is MIT-licensed: read the full guide.
Live Intelligence
Most knowledge tools answer from a snapshot. An index tells you what was true the last time it crawled, not what's happening this minute.
DocBrain reaches into your live systems at the moment you ask. “What's the status of PROJ‑412?” pulls the ticket from Jira as it stands right now, then combines it with your indexed runbooks into one cited answer. Old knowledge and live state, in a single response.
DocBrain can read your tools, never write to them. Any tool that could change a system is dropped at discovery. Enforced, not promised.
Connected per‑user over OAuth. Your answer is scoped to what you can see in the source. Tokens refresh on their own, and nobody sees data they shouldn't.
Jira, Confluence, and Slack out of the box, plus anything that speaks the Model Context Protocol. Add a tool, and DocBrain starts answering from it.
Write operations
OAuth scope
At answer time
“Is the payments outage from yesterday resolved, and what was the fix?”
PROJ‑412 (“Payments 502s after deploy”) moved to Done at 09:14 UTC. The fix was a rollback of payment-processor plus an idempotency-key guard, per the linked runbook.
One answer. Indexed history reconciled with live source-system state.
Any AI assistant can look something up for you. The difference is what happens after the answer. It forgets. DocBrain keeps it, connects it, and gets better every time someone asks.
An assistant asks your tools a question. DocBrain turns every question your organization has ever asked into a memory that gets smarter.
4-Layer Memory System
Four memory layers work together so every question makes the system sharper. The 100th question about a topic is faster and better-answered than the first.
Cache hit rate
Avg response
Binary (Rust)
Multi-turn conversation context. Resolves "it", "that service", "the same thing."
Every Q&A ever asked. Semantic caching, feedback learning. Gets cheaper as it learns.
Entity relationships via graph traversal. "Who owns the payment service?" gets resolved, not searched.
Adapts retrieval from feedback. Discovers: "deployment questions → search DevOps first."
Capabilities
First-class knowledge units with provenance. Auto-captured from PRs, conversations, and CI pipelines.
Merged PRs and deployments auto-analyzed by LLM. Decisions, caveats, procedures extracted as fragments.
Three-layer scoring: structural, style rules, and LLM-assessed semantic quality. Composite score 0-100.
Hybrid search, intent classification, cited answers with freshness. Low confidence asks questions instead of hallucinating.
Entity relationships with BFS/DFS traversal. Blast radius analysis, expertise routing.
Clusters unanswered questions, detects gaps, auto-drafts missing documentation.
docbrain generate drafts a runbook, postmortem, or reference grounded in your own runbooks, incidents, tickets, and PRs — with per-claim provenance, or an honest list of what it can't answer instead of confident fiction. Same redaction, injection-quarantine, and quality gates as every DocBrain doc. Returns the markdown — you decide what to publish. Wire it into CI to update docs on merge.
Anthropic, OpenAI, Bedrock, Ollama (local), Gemini, Azure, and 8 more. Swap via config.
GitHub/GitLab/OIDC SSO. Four roles. Space-level isolation. API keys Argon2-hashed.
Mirrors real Confluence / Slack / GitHub / Jira permissions at query time. Restricted page in source = filtered out for users who can't read it. Three modes, side-channel-safe, audit-logged.
25MB binary. <500ms cold start. Under 100MB memory. Self-hosted, your data stays yours.
Plug in any knowledge source in any language. 3 endpoints, DocBrain handles the rest.
10 tools for Claude Code, Cursor, and any MCP editor. Capture decisions at commit time.
Pull live Jira tickets, GitHub PR state, or any MCP-enabled system at answer time. Per-user OAuth with transparent refresh. Answers cite live data, not stale chunks. See how →
Confluence, Slack, Teams, GitHub, GitLab, Jira, PagerDuty, OpsGenie, Zendesk, and more.
Security & enterprise readiness
DocBrain is designed for the teams whose job is to say no. It runs entirely inside your infrastructure, reads your systems without ever writing to them, and shows its work on every answer.
Runs entirely in your infrastructure from Docker images and Helm charts you pin and control. Point it at a local model and nothing ever leaves your network. Air-gap capable.
When DocBrain connects to a live system, any tool that could create, edit, or delete is dropped at discovery. It can read to answer a question. It cannot change anything. Enforced, not promised.
Source-system ACL mirrors the real permissions of Confluence, Slack, GitHub, and Jira at query time; per-user OAuth scopes live results to what each person can already see. Side-channel-safe, audit-logged.
SSO via GitHub, GitLab, or any OIDC provider. Four-tier RBAC, space isolation between teams, API keys hashed with Argon2, and every privileged action audit-logged.
Every answer is cited to its source. When the knowledge isn't there, DocBrain asks a question instead of inventing one. The design goal is "roughly 0% confidently wrong," not "always right."
Eleven analyzed attack vectors and an operator security checklist, open today. Because DocBrain runs inside your boundary, you can audit the boundary — not just the codebase.
Deploy with Docker Compose or Helm. PostgreSQL, OpenSearch, and Redis run inside your network; the AI model runs wherever you choose — a local model for zero egress, your own cloud account (Bedrock, Azure, Vertex) for your KMS and audit trail, or a provider API for the fastest setup. You decide where your data goes, source by source.
The client source — the CLI and the IDE MCP server — is open in the repo (MIT), so you can audit what runs in your environment and what leaves it. The server source is not published yet — we missed our original H1 2026 target, and we won't post a new date until we're certain of it. The rest of what determines your risk is open too: the deployment you control, the threat model, and the read-only, zero-egress boundary. How DocBrain earns trust →
Use cases
Anywhere critical knowledge lives in people's heads and conversations instead of a system you can search — engineering, operations, support, and beyond.
New people answer their own questions from day one, drawing on everything the team already figured out. Ramp in weeks, not months — without pulling a senior person off their work.
During an incident, ask what broke last time and what fixed it. Answers combine past incidents with live system state, cited — so the fix doesn't depend on who happens to be online.
Every "why did we do it this way?" has an answer: the decision, the context that shaped it, and who made it — kept with provenance instead of lost in a two-year-old thread.
Capture what your most experienced people know before they leave. When they do, the memory stays — and the next person inherits their context, not a blank page.
Front-line teams resolve issues from the whole organization's history instead of escalating the same question twice. The answer someone found once is available to everyone after.
Stop re-solving what another team already solved. One connected memory spans every tool and team, so a fix found in one corner of the organization is findable from every other.
In practice
One worked example, from an engineering team. Knowledge captured where the work happens — not after, not in a doc sprint. At the source, as it happens.
9:15 AM · PR Merged
DocBrain's CI capture extracts 3 knowledge fragments: a decision about retry logic, a caveat about idempotency keys, and a procedure for manual refund overrides. All auto-indexed with 92% confidence.
11:30 AM · Slack Thread
DocBrain answers with fragments captured 2 hours ago, plus links to the original PR. The support engineer types /docbrain capture to save more context.
2:00 PM · Auto-Composition
DocBrain detects semantic similarity across PR fragments, Slack capture, and older fragments. Composes "Payment Refund Procedures," scores at 78/100, routes for SME review.
3:30 PM · Published
Quality rules catch 2 violations, auto-fixed. Final score: 91/100. Published to "Payments" space. Total engineer effort: zero.
The old way: schedule a doc sprint, assign writers, review in 2 weeks, stale in 2 months.
With DocBrain: captured at 9:15 AM, part of your memory by 3:30 PM. Kept accurate from then on.
Recorded, not staged
Install → ingest → a cited answer → generate turning raw on-call notes into a runbook that cites its sources. Shipped images, 100% local models, sped up only where the title cards say so.
Independent Assessment
No cherry-picked benchmarks. We opened Rovo, asked it to do a fair comparison, and published the full response — including where it wins.
DocBrain's 'capture knowledge that was never written down' is solving a problem I fundamentally can't.
— Rovo (Atlassian's AI assistant), responding to a direct comparison prompt
Full response published unedited. Rovo's assessment, not ours. Read the full transcript →
FAQ
bash scripts/setup.sh and it brings up PostgreSQL, OpenSearch, Redis, migrations, and sample docs. Add your LLM API key and go.Security & Access
Docker, an API key, and three commands. That's it.
License & community
DocBrain is licensed under BSL 1.1 and free for production use. The client tooling — the CLI and the IDE MCP server — is open source (MIT) in the repo, built and tested in public CI. The server source is not published yet — we missed our original H1 2026 target, and rather than quietly moving the date, we're telling you plainly: no new date until we can hit it. What's public today: client source, production Docker images, Helm charts, full configuration, and the threat model to run it yourself.