AI context engine
AI should never work blind.
Grounded context for every agent — every answer cited.
↓ how the context engine works
How ContextBrain works
From your stack to a grounded solution — without the guessing.
ContextBrain fetches the exact context each task needs, feeds any agent over MCP, and ships a solution you can trust — every claim cited.
Closed learning loop
Every agent run, chat correction, and code change feeds back into project memory. Humans approve — they don't type from scratch.
How the loop closesHybrid retrieval
Semantic + keyword + entity match, fused with RRF, then re-ranked by a cross-encoder and weighted by freshness.
Read the pipelinePublic eval numbers
Recall 0%, tier-correctness 77.5%. Updated on every release.
See benchmarksWhy teams pick ContextBrain
One brain, every surface, every role.
Fourteen capabilities that work together — not fourteen tabs you forget to open.
Citations, not hallucinations
Every answer points back to the exact code, doc, or ticket line it came from.
Learn moreOne graph, every source
Code + docs + designs + meetings + tickets + PR threads — searchable as one.
Learn moreRole-aware from day one
PMs, devs, QAs, designers, and clients each see what they should — no fewer, no more.
Learn moreDoc–code drift detection
The moment your spec and code diverge, ContextBrain flags it.
Learn moreAgent pipeline → auto-PR
Read spec → write code → run tests → security scan → open PR. End-to-end.
Learn moreClaude Code in seconds
One curl | bash line installs the MCP shim in your editor.
Learn moreGrounded chat with citations
Ask anything across code, docs, tickets, designs — every reply links back to its source.
Learn moreClient-shareable chat links
Mint a 7-day read-only link. Your client self-serves status updates without a login.
Learn moreDrop-in embed for your PM tool
One <script> tag puts ContextBrain chat inside Jira, Linear, or your own dashboard.
Learn moreBring your own keys
OpenAI / Voyage for embeddings · Anthropic for agents · swap any time.
Learn morePostgres-native
pgvector + recursive CTE — no separate vector DB or Neo4j to babysit.
Learn moreAudit-grade trust
Every action logged, exportable to CSV, PII auto-redacted.
Learn moreArchitecture map + impact analysis
Visualize 100s of modules; see what breaks before you change it.
Learn moreSelf-host or SaaS
One Docker compose for the whole stack. Or let us run it. Same code.
Learn moreBefore vs. after
Without context vs. with context.
Five scenarios. Same teams. Two completely different days.
2–4 weeks reading code blind, pinging seniors on Slack, re-asking the same questions weekly.
Day-1 access to a chat that cites every code path, doc, and decision. Onboarding velocity 2× per published benchmarks.
Caught in QA or production — 10–100× the cost of catching it pre-merge.
Drift detector flags every spec doc that updates after a related PR merged. Same-day alert to PM + Tech Lead.
Weekly status emails. Meetings to recap meetings. Repeated 'what's the status of X?' questions.
One link. Client opens /chat/<token>, asks, gets a cited answer. Audience-scoped — they only see what's been marked client-shareable.
Generic Copilot suggestions that hallucinate function names.
Claude Code with MCP grounding — every suggestion cites your actual code, docs, and past decisions.
Yet another tab. Yet another login. Context lives in a separate app.
Drop a <script> tag into Jira / Linear / your own dashboard. Chat opens in-place, scoped to whichever project the user is already looking at.
Surfaces
Same context. Every surface your team works in.
The bow on the box.
Spin up a free workspace and start asking your codebase real questions in under five minutes.