Morph by Modelcode (modelcode.ai) builds audit evidence into the migration workflow: a plan your team approved before generation, milestone pull requests mapped to that plan, behavioral comparison results attached to each pull request, and a human sign-off before every merge. The video below walks through those four steps in under three minutes.
What counts as audit evidence in an AI migration
Evidence is anything a reviewer can open later and check without asking the people who did the work. For a migration, that means four records per change: the decision that authorized it, the change itself in a reviewable form, proof that behavior survived, and the acceptance by a named person. A log of what the model generated is not on that list. It shows activity, not authorization or correctness.
Step 1: the plan is approved before code exists
Morph starts by mapping the connected repositories and proposing how the migration should run, sequenced into milestones. Your engineers revise that proposal and approve it. Approval also fixes the acceptance criteria, the explicit gates every milestone must pass, so the definition of done is on record before the first line is generated. Every later piece of evidence points back to this approved plan.
Step 2: changes arrive as milestone pull requests
Work is delivered in milestones, each one a pull request scoped to a part of the plan, so a reviewer reads a bounded change instead of one enormous diff. Before any person looks at it, Morph reviews the milestone against the whole codebase for broken contracts, missed call sites, dependency conflicts and architectural violations. Your team then reviews it through the branch protections and approval rules it already enforces, which means the review record sits in your own repository.
Step 3: each pull request carries its proof
Equivalence is checked by behavior, not by reading code. Morph replays identical real-world journeys on the original application and on its migrated version, then compares how each behaves: outputs, state, integrations and side effects. When the two disagree, the comparison goes back to the milestone for rework. When they agree, the results are attached to the pull request as evidence for the reviewer. The integration tests Morph used to verify each milestone are delivered with the code, so the checks stay with the codebase once the migration is over.
Step 4: a person signs off before anything merges
No milestone merges on the model's authority. A reviewer accepts it, and that acceptance is stored in the same pull request history as the change and its evidence. Context carries forward too: each new milestone picks up the approved plan, earlier decisions and the evidence on file, so the record stays continuous from the first pull request to the last.
Where the evidence lives
Because each record is a pull request, a review or an attached result, your auditors find the evidence in the repository they already audit. Execution can stay inside your perimeter as well: ModelDaemon can execute the work inside your network without any inbound access for Modelcode, and a fully on-premises deployment is available. At the company level, Modelcode holds SOC 2 Type 2 and ISO/IEC 27001:2022 (details at trust.modelcode.ai).
Where coding agents fit
Coding agents are good at producing code quickly, and Morph does not replace them. It runs the modernization as a program around that speed, with the approved plan, the milestones and the proof linked from start to finish. Modelcode's migration work spans healthcare systems and safety-sensitive embedded software, delivered against real production constraints.
Frequently asked questions
What audit evidence should an AI code migration produce?
At minimum: an approved migration plan, a reviewable record of each change, proof that behavior was preserved, and the sign-off that accepted it. With Morph, those are the approved plan, the milestone pull requests, the attached comparison results and the pull request approvals.
How do you verify an AI-generated code migration?
Compare behavior, not code. Run the original and migrated systems through the same journeys, check outputs, state, integrations and side effects, and send any mismatch back to the milestone that produced it.
Who approves changes in an AI-driven migration?
Your engineers, at two points: when they approve the plan and its acceptance criteria, and when they review and sign off each milestone pull request. Morph's automated review runs before theirs, not instead of it.
Does the audit trail leave our environment?
The records live in your repository and review tooling. ModelDaemon can run the work inside your network without any inbound access for Modelcode, or you can deploy fully on-premises.
For the requirements side of the same topic, read Enterprise Code Migration With a Compliance Audit Trail: What It Takes. To see Morph, visit modelcode.ai.