1,500 screens. More AI changes. The same number of reviewers
Run supported GitMir intelligence and local AI workloads inside your infrastructure. Give agents product context and let people inspect the business logic that changes.
The bottleneck has moved to understanding the change
In a product spanning 1,500 web screens and 750 mobile screens, no single person can keep every rule and connection in their head. An agent cannot reliably recover the whole relevant picture just because it can read the repository.
Agents generate work. People then reconstruct what it affects, check the assumptions, request fixes, and decide whether it is safe to release. When generated change outpaces that process, review becomes the constraint on delivery.
These screen counts describe a workload profile drawn from a discovery conversation; they are not a published customer deployment or a measured GitMir success story.
Put understanding and visual review inside the perimeter
GitMir combines product intelligence, visual inspection of business logic, and supported local model deployment. Agents receive compact context relevant to a change. Reviewers examine affected behavior and connections instead of relying on memory or reading implementation diffs alone.
A private deployment can be configured around approved internal repositories, model endpoints, and agent runtimes. Its actual data boundary must include every component in the workflow: using a cloud coding agent elsewhere would change that boundary.
Better context can reduce avoidable mistakes and the work needed to correct them. Visual review can make remaining checks easier to complete. Together, these capabilities support greater autonomy within the approval rules your organization defines.
Review a business workflow, not 2,250 screens one by one
Example: change an enterprise approval policy. The policy affects role permissions, approval states, web and mobile actions, notifications, and audit records. Give implementation tasks the relevant product context, then inspect the changed business behavior and run the validation required for the affected workflows.
Visual inspection complements code checks and regression testing; it does not prove every untouched screen is correct.
Illustrative workflow based on a prospect conversation
Change: Allow a partial refund after dispatch
- Eligibility rule — changed: partial refund after dispatch
- Refund amount — new
- Inventory, customer notice, mobile status, finance reconciliation — affected
- Acceptance tests — added
What the reviewer sees
- The rule that changed
- Eligibility: a refund was allowed only before dispatch; now a partial refund is allowed after dispatch.
- Refund amount
- New: undelivered items, minus shipping already paid.
- Connected paths
- Inventory, customer notice, mobile order status and finance reconciliation read the refund amount.
- Tests supporting acceptance
- Checks for eligibility, amount and each connected path run before the change is accepted.
Visual inspection complements code checks and regression testing; it does not prove every untouched screen is correct.
Measure the review queue and the release outcome
Track reviewer hours, waiting time, correction rounds, release frequency, and defects escaping approval. Start with one well-defined workflow and keep its quality requirements fixed.
Illustrative economics
Modelled target
Recovering eight review hours across each of 40 changes yields 320 hours. At $90/hour, that is $28,800 of review capacity before software, hardware, and operating costs. Validate the time reduction before expanding the deployment.
Assumptions
- 40 accepted changes in the period
- 8 review hours recovered per change
- $90 fully loaded hourly cost
- Software, hardware and operating costs not included
Scenario — your numbers, not measured savings
Review capacity in your numbers
- Review hours freed per month
- 320 h
- Cash-equivalent capacity per month
- $28,800
- After GitMir's monthly cost
- $28,800
- Months to recover the initial fee
- —
Hours = changes × (minutes today − minutes with GitMir) ÷ 60. Capacity = hours × hourly cost. This is reviewer time that can go to other work, not cash saved. Your numbers stay in this page.
Evidence and its limits
Measure our review bottleneck
Bring one complex workflow and your infrastructure requirements. Establish the context, review, and deployment scope for a measurable pilot.