Migration: Legacy Codebase to AI-Ready
Last verified: June 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.
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."
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.