Independent AI reviews for engineering teams who ship fast and need to stay safe.
We look at the real thing, not the polished version.
Most teams know something is off. They just haven't had someone sit down and actually look. That's the job.
We read your actual code — not just your docs or your team's description of what they do
We look at real pull requests, real prompts, and real AI tool configs
We flag specific problems with specific fixes, not general recommendations
We write standards your team can actually follow, not a policy document no one reads
We check for secrets, credentials, and sensitive data in places teams forget to look
We map where your AI system can fail and what happens when it does
You get findings in plain language — no jargon, no slides full of frameworks
Speed is easy. Knowing what you shipped is harder.
If your developers use AI to write code:
- AI-generated code ships without a consistent review process
- Different developers use the same tools in completely different ways
- No one has checked whether credentials or sensitive data are leaking through prompts
- Code quality is unpredictable — sometimes great, sometimes a mess
If you've built AI features into your product:
- AI features work in testing but behave unexpectedly in production
- Data flows touch sensitive information with no clear audit trail
- Failure modes haven't been mapped — you find out when something breaks
- The team that built it is too close to it to spot the gaps
In both cases, the risk isn't that AI is being used. It's that no one has looked closely at how.
That's what we do.
Pick the one that fits your situation.
AI Coding-Tool Audit
The problem
Your developers use Copilot, Cursor, or Claude to write code every day. But there's no shared standard for how they use these tools — so code quality varies, reviews are inconsistent, and no one has checked whether secrets or sensitive data are slipping through.
What we do
We review how your team actually uses AI tools day-to-day. We look at real code, real workflows, and real review habits. We flag where AI-generated code is shipping without proper checks, where quality is inconsistent, and where sensitive data or credentials are at risk.
What you walk away with
A clear set of standards and guardrails — written for your team, not a generic template — so every developer uses AI the same safe, consistent way. No more guessing whether the code that just shipped was actually reviewed.
AI Systems Review
The problem
You've shipped AI features — models, integrations, data pipelines. They work well enough in testing, but you're not fully confident they'll hold up under real load, edge cases, or a security probe. You want a second set of eyes before something breaks in production.
What we do
We review your AI system with fresh eyes — the models, the integrations, the data flows, the failure modes. We look for where it's fragile, where it's insecure, and where it's likely to break in ways your team is too close to see.
What you walk away with
A prioritized list of what to fix, why it matters, and how to fix it — before it becomes a production incident. Concrete findings, not a slide deck full of abstract recommendations.
Three steps. No surprises.
Every engagement is the same structure — a conversation, a review, a clear set of findings. Simple on purpose.
We talk for 30 minutes
Free
You tell us what your team is doing with AI — the tools, the workflows, the parts that feel uncertain. We ask questions. By the end, we both know whether we can help and which review makes sense.
You get
A clear yes or no on whether this is the right fit
We do the review
1–3 weeks
We get access to what we need — code repos, tool configs, system diagrams, or a walkthrough of your AI features. We work through it methodically, looking for the things that are easy to miss when you're inside the system.
You get
A written report with specific findings, ranked by severity
You get a clear picture
Delivery call included
We walk you through what we found, what it means, and what to do about it. No vague recommendations — each finding comes with a concrete next step. You leave knowing exactly what to fix and in what order.
You get
Findings doc + standards or guardrails (depending on the review)
Engineering managers and tech leads who need a straight answer.
Not a framework. Not a workshop. Just someone who looks at what you've built, tells you what's wrong, and helps you fix it.
AI Coding-Tool Audit
You're a fit if:
- Your developers use Copilot, Cursor, Claude, or similar tools daily
- You don't have a shared standard for how AI tools should be used
- Code review doesn't consistently catch AI-generated issues
- You're not sure whether sensitive data is making it into prompts
- Quality varies a lot depending on who wrote the code
AI Systems Review
You're a fit if:
- You've shipped AI features — models, integrations, or data pipelines
- You want a second opinion before something breaks in production
- Your team built it and you need someone outside to pressure-test it
- You're not confident about the failure modes or security posture
- You're about to scale it and want to catch problems first
Not sure which one applies?
Some teams need both. Some need one. Book a call and we'll figure it out in the first 10 minutes. There's no pitch — just a conversation about what you're dealing with.
Why us
We're engineers, not consultants who learned about AI last year. We've built and reviewed these systems. We know what to look for because we've made the same mistakes.