Keep the product understood
through every change
Bring a requirement, client brief, bug or commit. GitMir keeps the relevant product Intelligence available from impact analysis to development tasks and review — whether people or AI write the code.
ProductsIntelligence→Vibe→Cloud Compute→MeetAI
The expensive part repeats before the code starts
Teams spend time reconstructing what the product already does, what the request touches and whether the result still matches the intended behaviour.
MEASURED ACROSS THE PROJECTS WE HAVE RUN. EACH FIGURE CARRIES THE DEFINITION IT WAS TAKEN UNDER — WHAT YOUR WEEK COMES TO IS MEASURED ON YOUR WEEK.
Put one change
next to the product
Follow the same change from the incoming question to what it reaches. Billing is not one file or one function: the change crosses billing, the organization, usage, partner onboarding and project settings.
What must an AI agent know before changing billing?
An example, so you can see the answer without uploading anything of yours.
- Areas it reachesBilling & Subscription · Organization · Usage · Partner Onboarding · Project Settings
- RecordsSubscription · Invoice · PaymentMethod · CustomerProfile · Entitlement · ProjectAddon
- Actions · 33updateOrgSubscription · topUpCredits · redeemCode
- Endpoints · 27PUT /platform/organizations/{slug}/billing/subscription · POST .../billing/credits/top-up
- Screens · 33payment methods · organization billing and usage
- Customer pathsChange an organization's plan
A READY-MADE EXAMPLE ON GITMIR'S PUBLIC SUPABASE STUDIO MODEL — 459,339 LINES OF SOURCE. WITH YOUR OWN PROJECT THE SAME WORKSPACE HOLDS YOUR REPOSITORIES, INCOMING DOCUMENTS, DEVELOPMENT TASKS AND PROCESSED COMMITS.
The same Intelligence
stays with the change
The handoffs change. The product reality should not. GitMir keeps the understanding reusable as work moves from a requirement to implementation and review.
Incoming requirement
Brief, specification, bug, customer request or internal change.
Existing product
See what already exists before deciding what the request means.
Impact
Make affected behaviour, dependencies and open decisions visible.
Development tasks
Create work connected to the same product understanding.
Implementation
People or coding agents retrieve the task and relevant Intelligence.
Commit review
See how business rules and product behaviour actually changed.
One shared, versioned product model underneath the work — available to developers directly and to agents and connected systems through MCP.
Use it where engineering
already spends time
Get up to speed without relying on the person who knows it best
Explore product behaviour and ask questions without reconstructing the system from files or interrupting the same senior developer again.
Know what a client brief or specification means before you commit
Compare incoming requirements with what exists, identify changes and make open decisions explicit before scope hardens into implementation.
Prepare work with the product context already attached
Create development tasks from analyzed requirements. Your team can use them directly and agents or connected systems can retrieve them through MCP.
See what people or AI actually changed in the product
Follow changes in business rules and product behaviour across processed commits instead of validating only the code diff in isolation.
Stop rebuilding product understanding project by project
Give each product or client project a shared model that people, AI and connected tools can use as the work continues.
The code is not the first thing
your team has to understand
Before a change can be written or reviewed, somebody has to reconstruct enough of the product to know what the request means and what the implementation should preserve.
Understanding is rebuilt around each change
The change starts next to an existing product model
GitMir does not replace your IDE, issue tracker, code review or coding agent. It reduces the repeated reconstruction between an engineering question and the product understanding needed to answer it.
Measure the understanding around the code,
not only the code generation
Two different measurements, kept apart: the work around a change, and the cost and quality of getting a product answer.
BENCHMARK: ONE 459,339-LINE REPOSITORY · 50 NATURAL-LANGUAGE QUESTIONS · 3 RUNS EACH · 1,050 ANSWERS GRADED BLIND · TEMPERATURE 0 · SAME MODEL ACROSS EVERY ROW. THESE NUMBERS DESCRIBE THE MEASURED WORKLOAD, NOT EVERY POSSIBLE ENGINEERING TASK. · SEE BENCHMARK CONDITIONS →
Put your own engineering week
into the equation
The useful number is not a generic “10× productivity” claim. It is how much of your own team’s time goes into understanding, clarification and rework — and how much of that you can realistically recover.
EXAMPLE INPUTS — EDIT FOR YOUR TEAM
ADDITIONAL CREDITS ARE NOT ENTERED, SO THIS IS NOT A FULL NET FIGURE — ONLY THE PLAN IS SUBTRACTED.
An estimate based on your inputs. Recovered time is additional capacity; it becomes cash savings only when your spending changes.
Put the Intelligence where
the engineering work already happens
Developers can work with GitMir directly. Coding agents and connected systems can retrieve product models and development tasks through MCP. Your existing tools still do the jobs they already do.
Developers & reviewers
Ask the product, explore behaviour, inspect impact and review business-logic changes without switching to a different source of truth.
Claude, Codex & coding agents
Retrieve task-specific product Intelligence instead of reconstructing the product from source on each prompt.
MCP
Expose product understanding and development tasks inside the agent and tool workflows you already run.
Your runtime or GitMir Cloud Compute
Keep execution where you want it. Intelligence is the common layer underneath the work, not a forced replacement for your stack.
Bring the next change into focus
before the work starts
Connect a repository, bring a real requirement and see the same product understanding carried from impact analysis to implementation and review.