ContextBrain
- CATEGORYAI context infrastructure
- PROTOCOLModel Context Protocol (MCP)
- DEPLOYSaaS or on-prem white-label
- BEST FOREnterprise agentic engineering
The missing context layer of the AI coding stack — the operating system for software delivery. ContextBrain continuously builds deep project intelligence from your repos, tickets, docs, schema, and history, then briefs the coding agents your team already uses over the open Model Context Protocol.
Every agent starts every task with amnesia.
Agentic coding is now the default, not an experiment. But each agent reads files in isolation, re-explores, guesses, and re-introduces problems the org already solved — because it can’t see the context a senior engineer carries in their head. Blind automation isn’t a model problem; it’s a context problem, and it burns budget. Pair it with the right AI agents and that waste compounds.
Re-learns from scratch
The agent rediscovers context on every run, burning tokens to re-learn what the org already knows.
Budgets exhausted early
AI coding spend runs ahead of plan when every task pays to re-explore the same codebase.
No layer underneath
The market races ahead on agents while the context layer that would make them safe is missing.
The eight signals a senior engineer carries in their head.
ContextBrain assembles everything an experienced engineer knows without thinking — and makes it briefable to an agent. It isn’t code search, and it isn’t a coding assistant. It’s the intelligence layer beneath both.
Dependency map
The real graph of how the code fits together — not files read in isolation.
Project memory
Past decisions, incidents, and patterns — the landmines a codebase learned the hard way.
Standards
Style, linting, and conventions the team already expects every change to follow.
Domain rules
The business logic and constraints that aren’t written in any single file.
Schema & API contracts
The data shapes and interfaces a change has to respect to stay safe.
Tickets & history
Why the work exists and what came before it, pulled from the trackers you run.
QA & tests
Coverage, regressions, and risk clusters that tell an agent where it’s walking.
Docs & design
The written and drawn intent behind the system, kept beside the code.
Connect, index, and brief — in one flow.
A turn-key path from your existing stack to context-aware agents. No new workflow to learn, no codebase to migrate.
Connect
Link repos, trackers, docs, and CI in one step. Change-hooks install automatically, so context stays live without manual re-syncs.
Index
Code, tickets, and documents are made searchable by meaning — semantic, keyword, and entity search, fused into one retrieval layer.
Build context
For a given task, ContextBrain assembles a task-specific Context Pack — only the relevant code, schema, memory, and risks, and nothing else.
Brief the agents
Agents receive the pack over MCP, then code, test, review, and open PRs with full project awareness from the first message.
A task brief you can actually trust.
For any task, ContextBrain assembles a task-specific pack of only the relevant code, schema, memory, and risks — nothing else. It’s scored, signed, and budgeted, assembled in seconds.
- Relevant code files
- DB tables
- Related tickets
- Project-memory flags
- API contracts
- Prior QA tests
- Deployment risks
- Coding guidelines
- Confidence score
How sure the pack is about what it assembled, surfaced up front.
- Freshness score
How current the included knowledge is — stale signals are down-ranked, not hidden.
- Token budget
Cost-aware by design — the pack is bounded so agents stop paying to rediscover the codebase.
- Hash-signed delivery
Tamper-evident on arrival and assembled in seconds, so the brief you hand an agent is the brief it runs.
One brief, a team of specialists.
ContextBrain orchestrates a multi-agent software-delivery lifecycle — PM task → Context Pack → Code → QA → Security → PR merged. Each agent is briefed with the right pack, so review happens on every change. It complements our AI/ML engineering bench rather than replacing your team.
- 01 · AGENT
Coding agent
Writes against the full Context Pack instead of guessing from files in isolation.
- 02 · AGENT
QA agent
Generates and runs tests after every change — not only at the end of a sprint.
- 03 · AGENT
Security agent
SAST plus a dependency audit before merge, so risk is caught while it’s cheap to fix.
- 04 · AGENT
Architecture agent
Flags pattern and standards violations against the conventions the team already keeps.
- 05 · AGENT
Docs agent
Keeps READMEs, API docs, and changelogs current as the code moves underneath them.
- 06 · AGENT
Release agent
Drafts release notes and runs pre-deploy risk analysis before anything ships.
Knowledge that compounds — under human review.
ContextBrain remembers in tiers. When an agent finds a landmine or a fix, it can promote that finding to shared memory so the next agent inherits it — but nothing enters shared memory unchecked. Stale knowledge is auto-down-ranked, and superseded decisions are chained, not lost.
Organization memory
Global rules that apply across every project — the standards the whole org holds itself to.
Project memory
Decisions, incidents, and patterns specific to a codebase — the fixes it learned the hard way.
Session memory
Ephemeral per-task notes, used while the work is live and discarded after it’s done.
A governed promotion workflow gates everything: an agent’s finding only joins shared memory after human review, so the project’s collective knowledge stays trustworthy as it grows.
Built for a multi-level org.
Every level of the team sees the view it needs — from sprint-level PR status to a developer’s personal context-pack builder.
- Agent-activity timeline
- PR status per sprint
- Context-health score
- Release-readiness
- AI cost & token consumption
- Architecture risk alerts
- AI-confidence per feature
- Dependency-impact analysis
- Standards-violation detection
- Personal context-pack builder
- Semantic codebase search
- Session history
- Memory of past decisions & bugs
- AI-generated coverage
- Failing-test clusters
- Release-risk indicators
- Regression history
Governed and compliant by design.
Built for compliance-driven organizations from day one — the controls a strict security review expects, not an afterthought bolted on later.
Encryption
AES-256-GCM at rest, signed sessions, and keys-only audit. Secrets never hit the logs.
Access
RBAC plus multi-tenant isolation, with cross-tenant access verified on every reference.
Audit
Every state-changing action is logged, with retention controls you set.
Governance
A scanner flags or hard-blocks risky content before it can enter shared memory.
Data control
GDPR right-to-be-forgotten, per-repo deletion, and your keys with your models.
No LLM training
A standing commitment that customer data won’t be used to train LLMs.
Connects to the stack you already run.
No rip-and-replace — ContextBrain indexes in place over MCP, and change-hooks install automatically so context stays live as your work moves.
GitHub, GitLab
Jira, Linear
Slack, Teams
Gmail, Outlook, Calendar
Google Docs, Office 365, Figma, Markdown
TMS, EchoAce, and more
Hosted by us, or inside your perimeter.
Two paths to the same context layer — pick the one your security posture and timeline call for. Both support bring-your-own-model and bring-your-own-keys, so you control AI spend and the data path either way.
The fastest path
We host, you connect, and onboarding takes one step. Best for a quick pilot with zero infrastructure to stand up — live in days.
Inside your walls
Runs inside your own infrastructure, under your brand — nothing leaves your perimeter. It fits a strict security review and gives you full control of AI spend and the data path.
Working blind, versus briefed.
The difference a context layer makes, side by side. These are outcomes, not guarantees — any before/after numbers in a pilot stay illustrative until measured on your own data.
Relearns decisions & standards every run
Inherits them — solved problems stay solved
Pays to rediscover the codebase each task
Budgeted packs cut wasted tokens — more output per dollar
QA, security & standards checked at the end
Checked on every change — faster and safer
Lost between runs
Compounds — every run makes the next smarter
Explore ContextBrain on its own site.
Want to scope a pilot? Talk to us about a rapid POC or browse the rest of the product portfolio.
The questions engineering leaders ask first.
Build-vs-buy, lock-in, and the data path — answered straight.
Is ContextBrain a code-search tool or a coding assistant?
Neither. It’s the layer underneath both — the context infrastructure that feeds project intelligence to the coding agents your engineers already use, over the open Model Context Protocol.
Does it replace the agents we already run?
No. It briefs them. ContextBrain plugs into Claude Code, Cursor, Codex, and other MCP-aware agents — no rip-and-replace of your existing stack.
Can we run it on our own infrastructure?
Yes. ContextBrain offers an on-prem, private deployment that runs inside your own perimeter under your brand, alongside a managed SaaS option for a faster pilot.
Can we use our own models and keys?
Yes. Both deployment models support bring-your-own-model and bring-your-own-keys, so you control AI spend and the data path either way.
How does it keep AI cost down?
Context Packs are token-budgeted and cost-aware. Instead of an agent paying to rediscover the codebase on every run, it inherits a bounded, scored brief — so more of each AI dollar turns into output.
Will our data be used to train LLMs?
No. There’s a standing commitment that customer data won’t train LLMs, backed by AES-256 encryption, RBAC, full audit, and human-reviewed memory promotion.
Stop paying AI to work blind.
Scope one legacy product line, deploy on-prem white-label with your own keys, and measure rework, AI cost per PR, and standards-catch rate over 90 days — you define success up front and own the numbers.