We contract measurable problems.
Bring us one expensive recurring workflow. We measure what it costs today, agree what it should cost, and put the success criteria into the engagement — before anything is built. Fragmented processes, decisions and company knowledge become explicit, executable intelligence.
Four steps. Numbers first.
Baseline
We measure what the workflow costs now — hours, rework, errors, supervision. If it cannot be measured, we say so.
Target
We agree the number that would make the engagement obviously worth paying for — and how it will be verified.
Implement
We model the workflow, its rules and its context, and put AI to work inside that structure.
Verify
The result is measured against the agreed baseline. The metric decides — not the demo.
If we cannot find a measurable target that creates materially more value than we cost, we will tell you not to buy. That rule is what makes the contract model possible.
Every number is labeled.
VERIFIED
Measured on a real workload, with the measurement method stated. The only class of number we will contract against.
ILLUSTRATIVE
A worked example that shows the mechanism. Never presented as a promise about your workload.
TARGET
A goal we agree together and then verify. Written into the engagement, not into the marketing.
The product is new. The engineering experience behind it is not.
GitMir is built by the team behind MIR DIGITAL — an engineering organization with 25+ years of building and operating production systems across hundreds of projects. We built GitMir inside our own operation first, because we were paying for this problem ourselves.
Judgment
Knowing which workflow is worth fixing, what to baseline and where the hidden dependencies live.
Production discipline
Systems that had to survive real operations — integrations, data, failure modes, verification.
Skin in the game
We run GitMir on our own company every day. The technology is operational leverage, not a demo.
Private models for proprietary workflows.
Standard workflows are covered by our products. Proprietary ones get a private model: your processes, rules and constraints as an explicit reasoning structure — deployed in your environment, owned by you, and never used to train anything.
Bring us one workflow worth fixing.
Its current cost, the people it consumes, and the outcome that would make the engagement obviously worth it.