How teams use Omnibrain
Real workflows, with results you can measure.
Plans built on the code, not assumptions
The problem
Sprint planning happens in a meeting room with sticky notes and best guesses. "How long will auth take?" depends on who answers. Nobody remembers the shared type that needs updating. Dependencies show up mid-sprint.
With Omnibrain
The planning agent reads your codebase before generating a single task. It knows which modules are involved, what the dependencies are, where the complexity lives, and what conventions to follow. You get goal trees and tasks that reference real files, not vague tickets disconnected from the code.
- → Clarifying questions that pull code mid-conversation. The agent reads before it asks
- → Pulls context from every relevant repository automatically
- → Tasks reference actual files and architectural constraints
- → Estimates on every task. Commitments, not vibes
- → Plans track themselves. Progress rolls up from merged PRs
Connect business goals to engineering output Enterprise
The problem
Leadership sets quarterly goals. Engineering interprets them into projects. Months later, the question is always the same: "Did we actually build what we said we would?" Nobody knows, because no system connects goals to merged code.
Managers track progress through standups, Jira filters, and gut feel. When the board asks for engineering ROI, the answer is a spreadsheet someone spent a weekend building.
With Omnibrain
Strategic planning starts with a conversation. Leadership defines goals. The system clarifies scope, pulls context from every relevant repo, and generates a goal → task tree with estimates grounded in the code.
When PRs merge, Omnibrain automatically ingests the changes, summarizes what was done, and matches it to goals and tasks with a confidence score. No manual tagging. No Jira labels. Omnibrain computes the link between "what we planned" and "what we shipped" from the code itself.
For engineering managers
- Set goals once, track them automatically
- Tasks generated from the actual codebase, not guesswork
- Goal pace shows what's stalling, with a predicted completion date
- Quarterly reports write themselves, since the data is already connected
For upper management
- Real-time view into how engineering effort maps to business goals
- Plan delivery, on-time rate, and estimate accuracy for each team and engineer
- Evidence-based capacity planning for next quarter
- No more "trust me, we're making progress"
See what your team is actually building Enterprise
The problem
Engineering managers know what tickets are assigned. They don't know what's actually shipping, whether it aligns with the plan, or which engineers are carrying the hardest problems. Performance conversations rely on memory and anecdote.
With Omnibrain
Omnibrain summarizes every merged PR and matches it to the goal, task, or issue it serves. You get a factual, code-based record of what each engineer delivered against the plan, not a self-reported status update.
- Led Connect API migration, 12 PRs in payment-service
- Implemented webhook signature verification, 4 PRs
- Unblocked mobile team with shared type updates
- Optimized 3 high-traffic query paths
- Reduced p95 latency from 340ms to 87ms on /api/orders
This is not surveillance. No keystrokes tracked. No hours logged. Just a clear record of shipped work and how it connects to goals your team agreed on together.
- → Factual: built from merged code, not self-reporting
- → Contextual: shows what the work accomplished, not just that it happened
- → Team-level and individual views
- → Exportable for reviews, planning, and board reporting
Ship faster with the full architecture in view
The problem
Engineers spend a large share of every week reading code they didn't write. They switch between repos, trace dependencies by hand, and ask teammates "who owns this module?"
With Omnibrain
Every engineer can pull up the full architecture of any repo they touch. Chat answers questions in seconds. Reviews catch cross-repo issues before they merge, and planning writes tasks grounded in real code.
Impact
- ↓ LLM token costs, since context is filtered before it reaches the model
- ↓ PR review time drops, since breaking changes get caught automatically
- ↓ Onboarding from weeks to days
- ↑ Senior engineers freed from "how does X work?" interruptions
Make every AI tool in your stack smarter
The problem
Copilots see the file you're editing. Maybe a few imports. They don't know your types, your conventions, how your modules connect, or what that function three repos away expects. So they guess. And you fix their guesses.
With Omnibrain
Your copilot, whichever one you use, gets the exact context it needs before it writes anything. Types, related functions, architecture patterns, cross-repo dependencies. All of it streams through one API.
function processOrder(data) {
// copilot guesses at types
// doesn't know validation rules
// misses the event system
const order = { ...data };
save(order);
}function processOrder(input: CreateOrderInput): Order {
const validated = validateOrder(input); // from shared-libs/
const order = OrderFactory.create(validated); // factory pattern
await orderRepo.save(order); // repository pattern
eventBus.emit('order.created', order); // event system
return order;
}Works with: GitHub Copilot · Cursor · Cody · Continue · Aider, plus any tool that accepts context over HTTP or MCP
Code review that understands consequences
The problem
Your current review process catches syntax issues and obvious bugs. It misses the type you renamed that breaks 4 downstream repos, the new endpoint without rate limiting, the query that bypasses your repository pattern. It only sees the diff, not the architecture.
With Omnibrain
Omnibrain reviews every PR against the full knowledge graph. It traces changed functions through their consumers and checks new code against the patterns your team already uses. Security flags come from what your codebase actually does, not a generic rule set. It also tags every PR with the goal it serves, so review and delivery tracking happen in the same pass.
You changed a shared type. Omnibrain finds every consumer across every repo and lists exactly what breaks.
A new endpoint ships without auth middleware, rate limiting, or input validation. Omnibrain spots it by comparing against your existing endpoints.
Raw SQL in a handler when your team uses repositories. Direct state mutation when your app uses events. Flagged with links to the correct pattern.
Functions that grow past maintainable size. Omnibrain suggests where to split them, based on the logical boundaries in the code.
Ships as a single PR comment via the GitHub App, with error, warning, and info severities, updated on every push. Nothing to configure after install.
New engineers productive in days
The problem
New hires spend their first 2-4 weeks reading code, asking questions, sitting in "architecture overview" meetings, and still not understanding how the pieces connect. Senior engineers lose hours explaining the same things over and over.
With Omnibrain
On day one, they ask "What powers the checkout flow?" and get an instant answer with file paths and a dependency map. "How does auth work across services?" returns a full trace across repos. "What's the pattern for adding a new API endpoint?" pulls examples from your actual codebase.
No stale wiki pages or outdated diagrams. No waiting for someone to free up and explain.
I need to add a new payment method. Where do I start?
Payment methods are added through the provider pattern:
- Create provider in
api/src/payments/providers/
→ See stripe.ts and paypal.ts for the interface - Register in
api/src/payments/registry.ts:15
→ Add to PROVIDER_MAP with your provider key - Add types to
shared-libs/src/types/payment.ts
→ Extend PaymentMethod union type - Frontend selector:
frontend/src/components/PaymentSelect.tsx
→ Providers auto-discovered from API /payments/methods
Tests to update: api/tests/payments/checkout.test.ts (3 assertions), frontend/tests/PaymentSelect.test.tsx
See Omnibrain work on a real repo
Book a demo. We skip the slide deck and run it on actual code.