Your team has the licenses, and on a small clean project the agents do what the demos promised. In the codebase that runs your business, the same agents drop to autocomplete: they guess at how things work and reviewing the output costs more than writing the code by hand.
The reason is that your codebase was written for people. What an agent needs to work safely, how the system fits together, what correct means for each module, and what must never change, lives in your engineers' heads and in old conversations, not in the repository. A new engineer absorbs that over months alongside the team. An agent only sees what is written down.
Two studies from June 2026 put numbers on it. Info-Tech Research Group found that 51% of practitioners say AI breaks on complex or legacy code, making it the single most common roadblock to AI in the development workflow. Software Improvement Group, benchmarking more than 400 billion lines of code across 30,000 systems, found that AI-generated code carries roughly double the security risk violations of human-written code, and that the gap grows as codebases get larger.
AI-ready is four things that either exist in your repository or do not.
A live map of your repositories, services, endpoints, datastores and the people who touch them, plus the knowledge that never made it into a document: how a flow actually works, why a decision was made, what is still an open question. Agents read it before they act, so a task starts with the territory. It is kept current automatically as your code changes.
Living specs for the modules that matter, so what an agent builds gets checked against intent rather than against plausibility.
Guardrails that define what an agent may touch, what it must not touch, and what happens when it tries, running in your CI rather than written in a wiki.
Verification wired into your pipeline, so AI-written changes arrive with proof attached.
Before an agent modifies anything shared, an endpoint, a response shape, a secret, a common package, it asks the Brain who depends on it. And each build adds back what it learned, so the map gets better.
Everything lives in your repository. The context, the specs and the guardrails are versioned next to your code and reviewed in the pull request like any other change.
Model and vendor agnostic. Works with the coding agents your team already uses, and keeps working when you switch.
Standard integration surface. MCP for tool and context access, your IDEs, your CI, your pull request flow.
Any stack. None of the four pieces are language-specific.
We map how operable your repository is by agents today, where the blockers are, and what enablement would take.
Your repository made AI-ready, and demonstrated: the engagement closes with features shipped by directed AI inside your codebase, suite green.
Someone operates and scales what was enabled, with your team.
Forward Deployed Engineering →So we close with features your users touch, built by directed AI inside your repository, passing your suite, reviewed by your engineers. Your team sees the method work on their own code.
What lands: the engineering brain over your repositories, live and queryable. Living specs for the modules that matter, versioned in the repository. Guardrails running in your CI. Verification wired into your pipeline. And the features shipped with all of it in place.
Your infrastructure. Your repository, your CI, your environments. Nothing about your code moves to a system of ours.
Guardrails apply to us too. The same boundaries that constrain your agents constrain our engineers, enforced in the pipeline.
Least access, ending when the engagement does.
Secrets stay out of the repository, through your secret manager and environment variables.
Enablement works on a system your team can already change safely. If your codebase has no test coverage, the first step is a safety net (Software QA Automation). If the platform itself is out of support, the first step is modernization (Legacy Software Modernization). The assessment tells you which of the three you are looking at, and the three compose: each one leaves the codebase in better shape for the next.
That is the symptom this addresses. The licenses you have start returning value once the context, specs and guardrails exist.
The assessment maps it precisely, and points to whether the first step is coverage, modernization or enablement.
No. Everything we install is versioned in your repository and is not tied to one agent or model.
Nowhere. Everything runs in your repository and your CI.
No. We plug into your IDEs, your CI and your pull request flow.
Review, mostly. The method gets demonstrated on your own code.
Some will. The guardrails and verification make that visible and cheap to catch rather than silent.
One working session: we look at your codebase together and tell you how operable it is by agents today, what blocks it, and what enablement would take.
An AI-ready codebase is also the seed of an engineering brain. If your company later grows a Company Brain, your repository is already a module of it.
Learn about the Brain →