Your agents write the code. We review it.
For engineering teams shipping with Claude Code, Cursor, or Copilot who need senior engineers to verify what's actually going out the door.
Code volume is up 5–10x. Review quality hasn't kept pace. Junior engineers can't effectively review agent-written code — they don't know what's been glossed over, what patterns are subtly wrong, what security surface has quietly opened up. AI-generated code looks confident. It compiles. The tests pass. That's the trap.
The failure modes are real, specific, and invisible until they aren't. Hallucinated API contracts. Missing error handling in exactly the paths that matter. Security holes dressed up as clean abstractions. Architecture decisions made by accident, one PR at a time, that will cost you six months to unwind.
This is not a problem junior engineers can solve. They can read the diff. They can't tell you what's wrong with it.
Ongoing AI Code Review
Senior engineers embedded as reviewers in your AI-assisted development pipeline. We review PRs with the same depth we'd apply to our own production systems — architecture fit, security surface, edge cases the agent skipped, patterns that compound badly at scale.
Not a linter. Not a checklist. A person who understands what agents get wrong and why — and can explain it to your team in terms that make them better, not just fix the immediate diff.
AI Code Audit
A one-time deep read of a codebase built or heavily modified with agent tools. The use case: your team has been shipping fast with Cursor or Claude Code for three, six, twelve months. Someone senior needs to read what's actually there.
We look at architecture as it emerged (not as it was planned), security surface, dependency decisions, test coverage gaps, and the specific patterns that will calcify badly if left alone. You get a prioritized findings list and a remediation roadmap — not a vague report, specific evidence.
Full NDA · scoped read-only access · data deleted post-delivery
Architecture for AI-Augmented Teams
Setting the system design guardrails before you scale. Most teams let agents make architecture decisions by default — because no one had time to make them explicitly. This is how you end up with five different patterns for the same problem and a data model that made sense for week one.
We work with your engineering leads to define what should be constrained, what should be left open, and where human judgment needs to stay in the loop. Applied at the start, this prevents the drift that audits have to clean up later.
How this relates to Eloquent Lens
Eloquent Lens is a cold read of what's already there — a full codebase audit delivered in 2 business days. This service is the ongoing layer: eyes on what's coming in daily. The two work well together. Lens gives you the baseline; ongoing review keeps it from eroding.