You're moving quickly. Claude or Cursor wrote half your codebase. You don't have a CTO yet. You don't want a 12-week consulting engagement. You want someone senior to look at the thing before it ships and tell you what's going to break.
Vibe-coded apps and AI-assisted MVPs work — until they don't. The most common ways AI-assisted code breaks in production are predictable, but you have to know to look for them.
API keys, .env files, database credentials sitting in your git history. Almost every AI-assisted repo we look at has at least one. By the time you notice, it's been crawled.
Authentication that works in dev but has obvious bypasses, missing CSRF, weak password handling, or sessions that never expire. The AI generated something that compiles. It doesn't mean it secures.
No token limits, no caching, no rate limits, no model fallback. One viral moment and your OpenAI bill has four extra zeros. Your Stripe trial users have already cost you $4K.
System prompts that can be exfiltrated. User-controlled inputs that can override behavior. RAG retrieval that returns other users' data. Not theoretical — happens within a week of going live.
A senior engineer reads your codebase against a 30-point checklist before you ship. Categorized findings (blocker / serious / minor / nice-to-have) with file/line references and remediation guidance. One 60-minute walkthrough call. Optional 2-week Slack follow-up window.
30-min call + questionnaire. What is this, who uses it, what's the worst case if something breaks.
Senior engineer reads the codebase against the checklist. Auth, secrets, validation, prompt injection surface, eval coverage, cost guardrails, error handling, dependencies.
Severity-categorized findings report with file/line refs and remediation guidance. Pre-launch checklist confirming what was checked.
60-min findings call. We walk you through what to fix, in what order, and how. Optional 2-week Slack window for clarifying questions.
Most startups don't need a 12-week engagement. They need a specific thing reviewed by someone senior, fast.
Make sure your AI-generated privacy policy actually matches what your app does. (It usually doesn't.) Specific to AI products.
PromptsFind the prompts that will leak data, get jailbroken, or hallucinate disastrously. Includes hardening recommendations.
EvalsBuild 20–50 test cases so model swaps don't silently break your product. We pick the eval framework that fits your stack.
CostModel selection, caching strategy, rate limits, fallbacks. Often pays for itself in the first month, sometimes the first week.
TrustAI usage disclosure page, plain-language model card, security overview. The artifacts your enterprise prospects will ask for.
SubscriptionSlack + email access to a senior engineer when you're stuck. Office-hours response, often faster. Cancel anytime.
Three tiers. Cancel anytime. The point isn't to replace your team — it's to give you a senior reviewer on call when something feels wrong and you want a second pair of eyes.
A few things we deliberately don't offer, so you can save yourself a call.
Book a ship review, or sign up for the monthly hotline. If you're not sure which you need, the call is free.