One Intelligence Layer
for your agent network
GitMir turns your product and company systems into a trusted, reusable information model. Each agent retrieves the Intelligence its current step needs through MCP. The people supervising the workflow use the same source of truth.
ProductsIntelligence→MCP/Vibe→your agents
Your network stays yours. Runtime, orchestration, permissions, customer data and execution remain in your systems. GitMir supplies reusable Intelligence from the connected model.
Measured on 50 product questions against one 459,339-line repository, with the same model across every row. Production agent workflows should be measured on their own cases.
One request. Different agents
The same Intelligence
A customer upgrades to the Team plan. Every step needs a different slice of the product. Run the workflow and see what GitMir supplies without rebuilding the product picture each time.
Both steps call the same endpoint an agent would call. No model runs and nothing is stored; the timing is the assembly itself, not the round trip, and tokens are estimated at four characters each.
This runs against GitMir’s published Supabase Studio model. It does not claim to run that company’s support or billing workflow: your customer data and execution stay in your own systems.
One source of truth
Both sides of the workflow
The same Intelligence can feed the agents doing the work and the people checking what is happening. Execution and oversight stop building separate versions of product reality.
GitMir Intelligence
Trusted, versioned understanding of product and company logic, served at the level each request needs.
AI agents
Retrieve only the Intelligence needed for the current step instead of rebuilding the whole picture.
Operators & leaders
Use the same model to verify results, inspect changes and see what deserves attention.
Agent runtimes
Keep orchestration, workflow state, permissions, tools and execution where they already run.
GitMir Cloud Compute
Where you choose, tasks created from the same Intelligence can be executed inside the GitMir ecosystem.
MCP / API
Bring the same Intelligence into Claude, Codex, internal agents and connected systems.
Repositories & company sources
Connect the systems from which GitMir builds and updates the information model.
Keep your tools and workflow. Reuse the Intelligence instead of reconstructing it at every step.
Your agents can already retrieve information
Understanding it is the expensive part
Code, documents, databases, tickets and tools may already contain the facts. Each step still has to find, combine, interpret and verify enough of them before the model can reason reliably.
Each step rebuilds its own picture
The product understanding already exists
GitMir does not replace retrieval, databases or tools. It reduces the amount of semantic reconstruction required between the facts you already have and the question an agent must answer.
Add Intelligence
Do not rebuild your agent stack
GitMir has one job in the workflow: maintain and serve reusable Intelligence. Your operational systems remain responsible for live state and execution.
Context for the current step from the connected information model.
Agent runtime, orchestration, permissions, live data and execution.
The product rules and behaviour needed to understand the request.
Customer history, account state, support tools and authorized actions.
How plans, permissions, limits and dependencies are implemented in the product.
Current subscription, contract terms, balances and billing actions.
A shared, versioned reference different agents can query from different perspectives.
Workflow state, handoffs, agent identity, policies and execution permissions.
Requirement analysis, product impact and development tasks available with the product model.
The developer, coding agent or GitMir Cloud Compute process that performs the change.
The connection is standard
The Intelligence is the difference
MCP gives AI applications a standard way to reach data and tools. GitMir uses that connection to expose a product and company model that has already been interpreted into reusable Intelligence.
Claude · Codex · internal agents · your runtime
Ask the question that belongs to the current workflow step.
MCP / API
Transport the request and response into the tools you already use.
Intelligence Layer
Resolve the request against a trusted, versioned model of product and company logic.
Runtime · operational data · tools
Provide live state and perform the authorized action when the workflow requires it.
Use the same Intelligence
across the network
The use case changes. The source of product understanding does not.
Give support agents product rules, not a folder of documentation
Resolve what the product allows, what behaviour exists and which path a request belongs to before using customer-specific data.
Separate product logic from live billing state
GitMir supplies how plans, permissions and limits work; your billing systems supply the current subscription and authorized actions.
Let different agents ask the same product from different angles
Reuse one versioned model across handoffs instead of passing ever-growing prompt history from agent to agent.
Turn an incoming change into tasks already connected to the product
Analyze what exists, what is affected and what remains unclear; make the resulting development work available to agents through MCP.
Start from product meaning, then touch code where the task requires it
Give coding agents the business logic, affected areas and task context before they spend tokens reconstructing the system from implementation.
Supervise autonomous work from the same model the agents use
See which product logic changed, compare results with intent and inspect the version of Intelligence used without reading every underlying file.
Let GitMir Cloud Compute execute from the same Intelligence
Where you want GitMir to do more than supply context, connect generated tasks to execution inside the GitMir ecosystem.
Measure the Intelligence
before you measure the workflow
The published benchmark isolates one variable: the information available to the same model. Your production workflow should then be measured on its own cases, success criteria and total cost.
Repository: 459,339 lines · 50 natural-language questions · 3 runs each · 1,050 answers graded blind · temperature 0 · the same model across every row. This is a product-question benchmark, not a claim about support, billing, sales or other production workflows. See the benchmark conditions →
What does reconstructing context
cost your network?
Use your own workflow volume and AI spend. This is a scenario calculator, not a measured result.
A seat is a person. When the product runs on server-side agents, the paid seats are the people who set those agents up and operate them — the agents themselves consume credits, not seats. Credits are the meter for AI compute, and every seat includes 30,000 pooled credits a month.
combined economic value per month, less the GitMir plan only. Compute is not estimated, so this is a gross figure, not a net one.
COMPUTE IS NOT ESTIMATED, SO THIS IS NOT A NET FIGURE — ONLY THE GITMIR PLAN IS SUBTRACTED, AND RETURN ON COST IS NOT SHOWN.
A scenario based on your inputs, not a measured result. Human time represents capacity unless your spending changes.
INCLUDED: AI SPEND, HUMAN HANDLING, DIRECT ERROR COST, GITMIR PLAN. NOT INCLUDED: GITMIR COMPUTE, LOST FUTURE REVENUE, ONE-OFF FIRST PROCESSING, AND ANYTHING YOUR AGENTS SPEND OUTSIDE THESE CASES. NOTHING YOU TYPE LEAVES YOUR BROWSER.
Your agent network keeps running where it runs
GitMir does not need to become your orchestrator. Connect the information sources you choose, build the Intelligence layer, and expose it to approved agents and systems.
Stay in your infrastructure unless you explicitly choose GitMir Cloud Compute.
Stay under the controls of the systems that actually perform the work.
Can remain in the systems that own it; GitMir supplies the product and company Intelligence around it.
Expose the connected model to approved tools and agents through MCP / API.
Give the next agent step
enough Intelligence
Connect one product, expose its model through MCP and measure the same cases before and after. Keep the network you already built.