Migration: Legacy Codebase to AI-Ready
Last verified: October 2026· engagement
We restructure a messy monorepo so agents work well in it — module boundaries, type coverage, doc generation, test scaffolding, and spec generation from existing code.
- Outcome
- A phased restructure that agents can work in
- Timeline
- 3–6 months
- Pricing
- $150–500k+
- Buyer
- CTO at mid-market ($50M+ ARR)
The problem
Your monorepo grew organically for a decade. Agents thrash in it — fuzzy module boundaries, thin tests, no specs — so the AI-coding productivity everyone else is getting passes you by.
What we do
- Establish clean module boundaries and explicit interfaces, phase by phase.
- Backfill type coverage and a fast, trustworthy test suite.
- Generate docs and specs from existing code so agents start oriented.
- Sequence the migration so each phase ships value without a big-bang rewrite.
What you get
Built on our open source
openclawOS — An OS-like architecture for AI assistants — a kernel-based design with process-isolated apps.
How a migration this size gets sequenced
The premise is that you do not stop shipping. Every restructure of a long-lived codebase that requires a feature freeze either does not finish or finishes into a product that has moved on, so the work is sequenced as a series of independently valuable phases, each of which leaves the repository better than it found it and none of which requires the next one to be funded.
The order is dictated by what makes the following step safe. Characterisation tests come first around the modules we intend to move, because they capture what the system currently does — not what it was supposed to do — and that is the only safety net a refactor of undocumented behaviour can have. Then the boundaries: extract the seams, make dependencies explicit, break the cycles that mean any change might touch anything. Then types at the interfaces that get crossed most, which converts a class of runtime surprise into a build error. Generated documentation and specs come last, because they are only worth writing once the structure has stopped moving.
Agents are used inside the migration as well as being its purpose. A great deal of this work — backfilling characterisation tests, mechanical extraction, adding annotations across hundreds of files — is exactly the shape agents are good at once there is a test signal to check against, which is why the test phase comes first. The measure of progress we care about is not a percentage of files touched but whether the surfaces you most want to delegate have become delegable, and we would rather finish three modules properly than start twelve.
When this is not the right engagement
Restructuring a long-lived codebase is a serious commitment and not always the right one. It is a poor fit when:
- The codebase is being decommissioned or replaced within the year. Restructuring something on its way out is rarely the best use of the budget.
- There is no room in the roadmap for any refactoring at all. This work is incremental, but it is not free.
- You are hoping an agent will do the restructure unsupervised. It will help substantially, after the characterisation tests exist, and not before.
Common questions about this engagement
Do you work under NDA?
Yes — we sign your mutual NDA before any data or repo access. For audits we prefer read-only access to start; for builds we work in a clean repo under your ownership.
Will we own what you ship?
Always. You own the code, the runbooks, the dashboards. We are explicitly set up to hand off and transition out, not to create dependency.
Vendor-agnostic — what does that mean in practice?
We integrate with what you already run — OpenAI, Anthropic, open-weight models on your cloud, your CI/CD, your observability stack. If a hosted vendor solves it, we will not reinvent it; if a self-hosted tool is the right answer, we will not pretend the hosted one is.
How is this different from a Big Four consulting deck?
We are the engineers doing the work, not analysts handing recommendations to a different team. The deliverable is working software in your repo, not a slide deck.
Can you work with our in-house AI team instead of replacing them?
Yes — most of our engagements pair with an internal owner and ramp them up to run the system after we leave. Many of our best engagements start with "we hired an AI team, help us get them productive."
Do we have to stop shipping features?
No, and any version of this work that requires a freeze is one we would advise against. Every freeze-dependent restructure either does not finish or finishes into a product that has moved on. The work is sequenced as independently valuable phases: characterisation tests first, then boundaries, then types at the interfaces that are crossed most. Each phase leaves the repository better than it found it and none requires the next to be funded.
Can agents do the migration themselves?
They do a great deal of it, after the characterisation tests exist and not before. Backfilling tests, mechanical extraction and adding annotations across hundreds of files is exactly the shape agents are good at once there is a signal to check against — which is why the test phase comes first rather than as a tidy-up. Unsupervised restructuring of undocumented behaviour without that safety net is how a migration produces silent breakage.
Guides for this work
Is your codebase ready for AI agents?
Agents don't fail randomly on a repo — they fail predictably on the same things that slow down human engineers: fuzzy boundaries, missing tests, undocumented conventions. Readiness is just those weaknesses paid down deliberately.
Agentic codingWhat is agentic coding?
Agentic coding is letting an AI coding agent plan, edit across files, run tools and tests, and iterate in a loop toward a goal — powerful for the first 80% of a change, and quietly expensive on the last 20% if you skip standards and review.
Let’s scope it on a call
Thirty minutes with an engineer. We’ll tell you straight whether this is the right first move for your team.