Give every AI agent the business
intelligence to act correctly.
Connect product logic, customer state, subscriptions, policies and operational context into Intelligence your agent network can use before it acts.
ONE INTELLIGENCE LAYER · THE SAME ONE ENGINEERING USES · AVAILABLE THROUGH MCP
Your agents rebuild the same
picture on every single run.
An agent is handed a request and almost nothing else. Before it can act it has to assemble the customer, the plan, the rules and the current state out of whatever it can reach — and it does this again on the next run, and the one after that.
How it works now
- Customer request
- Support agent
- CRM retrieval
- Docs retrieval
- Subscription retrieval
- Policy retrieval
- The model reconstructs reality
- Action
- A person checks it
How it works with GitMir
- Your systems
- GitMir Intelligence
- Agent
- Verified action
The reconstruction happens once and is shared. Every agent in the network starts from the same understanding instead of assembling its own.
The agent acts on what
is true right now.
Support
The agent understands:
- the customer
- their subscription
- what the product can do
- previous interactions
- what they are entitled to
- the current state
- which actions are allowed
Billing
The agent understands:
- the invoice
- the subscription
- the limits
- the business rules
- the contract state
- which adjustments are allowed
Sales
The agent understands:
- the account
- what the product can do
- current usage
- the commercial state
- which upsells are actually valid
Customer success
The agent understands:
- where the customer is in their life with the product
- adoption
- churn signals
- the state of the product
- what to recommend
When several agents work one case, each one reconstructs the same context independently. Shared Intelligence removes that repetition — which is why the saving grows with the number of agents, not just with the number of runs.
Token savings are the smallest
part of the case.
Cheaper runs are easy to show and rarely what decides a budget. These are the numbers an agent operation is actually judged on — measure them before and after.
Cost per completed outcome
What one finished ticket, invoice or conversation costs end to end — not what a token costs.
Agent accuracy
The share of runs that end in the right action the first time.
Human escalation rate
How often a person still has to step in and check the work.
Context per workflow
How much has to be retrieved and reconstructed before the agent can act.
Incorrect actions
Refunds issued wrongly, entitlements misread, promises the product cannot keep.
Revenue leakage
Discounts, credits and cancellations an agent had no basis to grant.
Automation rate
The share of work that completes without a person in the loop.
Latency
How long a customer waits while the agent reassembles what it should already know.
What is reconstruction costing
your agent network?
Put in your own numbers. Everything is computed in your browser, and the two factors it applies are named on screen — including the fact that they were measured on engineering work, not on agent operations.
per month · $723,600 a year, if the improvements you entered hold on workflows your agents already run.
THIS IS A SCENARIO YOU BUILT, NOT A RESULT WE MEASURED. GITMIR APPLIES NO IMPROVEMENT OF ITS OWN HERE — BOTH PERCENTAGES ARE YOURS. THE 63% AND 64% BESIDE THEM COME FROM BENCHMARK 001, WHICH MEASURED ENGINEERING QUESTIONS ABOUT A CODEBASE AND NOT SUPPORT, BILLING OR SALES AGENTS — THEY ARE A REFERENCE POINT, NOT A FORECAST. NOTHING YOU TYPE LEAVES YOUR BROWSER.
See the difference before you
connect your own systems.
The same Intelligence layer is published in full on three real products. Open one, then decide whether it belongs in front of your agents.