Same AI.
Different Intelligence.
The model is only as good as the information it starts from. GitMir gives people, AI and systems trusted Intelligence at the level the question requires — with less reconstruction, less context and less uncertainty.
Every commit has a meaning.
Move through the commits to see the business logic change.
- Existing logic
- Added in this commit
- Removed in this commit
Links between areas
- Billing & subscription → Notifications (informs)
Billing & subscription
- Subscription status → Billing period (ends with)
- Refund unused days
- Cancel subscription → Refund unused days (triggers)
Billing & subscription ▸ Cancel subscription
- Keep paid access
- One-click cancel → Keep paid access
- Revoke at once
- One-click cancel → Revoke at once
Billing & subscription ▸ Subscription status
- Cancels at period end
- Active → Cancels at period end (on cancel)
- Cancels at period end → Cancelled (period ends)
- Active → Cancelled (on cancel)
Billing & subscription ▸ Renew subscription
- Cancelling? Skip the charge
- Cancelling? Skip the charge → Charge payment method (blocks)
Access & entitlements
- Keep until the period ends
- Keep until the period ends → Paid features (keeps)
- Revoke on cancel
- Revoke on cancel → Paid features (removes)
Notifications
- Paid access ends
Illustrative business logic. On your repository every analysed commit is laid over the real model the same way. Explore a real product
The variable is the Intelligence.
Same repository. Same questions. Same model. The benchmark changes only what the model knows before it reasons.
BENCHMARK 001 · 50 NATURAL-LANGUAGE QUESTIONS · 3 RUNS EACH · SAME MODEL, BLIND GRADING · SEE FULL METHODOLOGY →
Not a smarter model.
A better starting point.
Most systems start close to implementation and spend model effort reconstructing meaning. GitMir starts closer to the level of the question.
What could break if subscription cancellation changes?
An example, so you can see the answer without uploading anything of yours.
- Areas it reachesBilling & Subscription · Support · Usage · Project Settings · Organization
- RecordsSubscription · DiskAttributes · OrganizationUsage
- Actions · 9updateOrgSubscription · confirmPendingSubscriptionChange · sendDowngradeFeedback
- Endpoints · 8PUT /platform/organizations/{slug}/billing/subscription · POST /platform/feedback/downgrade
- Screens · 7cancel or downgrade the subscription · change the plan
- Customer pathsChange an organization's plan
More information
is not better Intelligence.
The useful question is not how much context you can provide. It is whether the information is accurate, sufficient, understandable and close enough to the real problem to reduce work.
Grounded in reality.
Intelligence should reflect what the system actually contains and does — not a plausible reconstruction.
Enough for the need.
Not everything. The information required to answer, verify, analyze or execute without another expensive investigation.
Closer to the question.
The less meaning people or AI have to reconstruct before reasoning begins, the less time, context and uncertainty the task requires.
More meaning. Less context.
Reduce the amount of irrelevant material between intent and useful understanding.
Something you can rely on.
Version, evidence, scope and measurable outcomes matter when Intelligence becomes a source of truth.
Understanding, not data.
Visual models, relationships and flows reduce the human time required to interpret correct information.
One Intelligence Layer.
Everything works from it.
Use it yourself. Give it to AI. Connect it to the systems you already use. Or let GitMir execute from the same trusted Intelligence.
Ask. Understand. Verify.
Get the level of product or company Intelligence needed without reconstructing the underlying complexity.
Better context before reasoning.
Claude, Codex and internal agents can work from sufficient Intelligence instead of rebuilding meaning from raw source.
Bring Intelligence into your stack.
Give existing tools and agents access without replacing the workflow your company already uses.
Product to company questions.
Change impact, requirements, market context, legal checks, strategic questions and proactive recommendations.
Execute from the same Intelligence.
Generated tasks can be handed off for execution without rebuilding product context for every task.
Visibility without watching everything.
See what changed, what was produced and what deserves attention while automated work continues.
Less supervision. More autonomy.
Let automated workflows use the same trusted understanding for recurring and autonomous work.
One reality across systems.
Connect internal software, enterprise tools and future applications to the same Intelligence source.
Keep your tools. Keep your workflow. Reuse the Intelligence instead of reconstructing it.
Find your work in it.
The same Intelligence can answer a developer, a founder, a reviewer or an agent differently — without forcing any of them to reconstruct the same underlying reality.
Stop reconstructing the product before every task.
Ask how behavior works, trace business logic and see what a change can affect — without keeping the whole system in your head.
Get useful understanding before the first explanation meeting.
Move through an unfamiliar product from the level of behavior and rules, then go down to implementation only when you need it.
Put a requirement next to the product that already exists.
See what is already there, what would change and what still needs clarification before work is handed to engineering.
Keep visibility without reading every implementation detail.
See how product behavior evolves, what people and AI changed, and where attention is actually required.
Review the behavior that matters, not every file that moved.
See which product logic a change reaches and compare implementation with the intended requirement or expected behavior.
Know what a client request means before you price and promise it.
Put a brief against the existing product, expose scope and open questions, and hand the team work grounded in the same understanding.
Give coding agents enough product context to work from.
Create Intelligence-backed tasks and expose them through MCP so agents start closer to the real product meaning.
Give each agent step only the Intelligence it needs.
Connect product and company context to existing workflows so autonomous systems can operate with less repeated discovery.
Get the answer you need without becoming the analyst.
Validate outcomes, compare intent with what was built, and surface exceptions without spending attention on the underlying complexity.
See how much you can do with it.
From incoming requirements to development tasks and the business logic changed by each commit — keep your people and AI working with the same product understanding.
Analyze incoming documents
Understand what a new brief, specification or change request means for the product you already have.
Compare proposed requirements with the product
See what already exists, what needs to change and what needs clarification.
Create development tasks
Turn analyzed requirements into actionable work connected to the product.
Make tasks available through MCP
Let your agents and connected systems retrieve development tasks with the product context they need.
Compare business logic across analyzed commits
See how rules, conditions and product behavior changed — whether the code came from people or AI.
Understand the impact of a change
See which parts of the product a proposed change could affect.
Explore your product and ask questions
See how the product fits together and get answers without reconstructing it from files or waiting for its author.
Find areas that need attention
Surface product observations worth investigating before the next change.
Organize projects and repositories
Keep your company’s products or client projects in one structured workspace.
Switch perspectives
Explore the same product through developer, CTO and product questions.
Connect tools and agents to the product model
Bring product understanding into the workflows you already run through MCP.
Work from a shared, versioned model
Give colleagues access to the same product understanding and see which version it reflects.
Twelve blocks is what fits on a page. What matters is which of them do anything for your product — and that is answered on your product, not here.
12 OF 317 SHOWN ON THIS PAGE · 32 OF THEM WORK ONLY IN PART · CHECK ANY OF THEM ON YOUR OWN REPOSITORY.
Quality per unit of context.
A derived benchmark metric: how much scored answer quality each system produced for every 1,000 tokens of context supplied to the same model.
| Input | Quality / 1K tokens | Against GitMir |
|---|---|---|
| GitMir | 10.95 | — |
| Unblocked | 2.85 | 3.8× less |
| Augment Code | 2.34 | 4.7× less |
| Sourcegraph Cody | 1.81 | 6.0× less |
| Raw repository context | 0.95 | 11.6× less |
Measured Intelligence,
not a black-box promise.
GitMir is built as Intelligence infrastructure and a research system. We publish measurable outcomes and benchmark methodology while keeping the proprietary representation and interpretation methods private.
We publish the results.
We keep the method.
The important claim is not that GitMir has a secret technique. It is that the resulting Intelligence can be inspected, tested and compared against alternatives.
50 questions · blind grading · same model
Inspect finished models before using your code
Know which product state Intelligence reflects
Cloud · Private Source · Enterprise
Results public · internal method private
Its own plan.
Priced for the people doing the work.
Credits pay for building and updating models and are shared across GitMir products. Asking questions and MCP requests are not metered on any plan.
3,000 credits to start · one private repository
$29 per active developer / month · 3,000 pooled credits each
Private deployment, identity and procurement terms scoped to your company
Ask a real product.
Open a finished public Intelligence model, ask the questions you would ask about your own system, then decide whether the difference matters.