AI Production Audit: find out why your AI breaks before your customers do.
A focused review of your AI system's accuracy on real data, reliability, cost, and security, from the engineer who built production AI at two Y Combinator startups. You get a written report, a test set you keep, and a prioritized fix plan.
Your AI works in the demo. Does it work on Tuesday at 3pm?
Most AI systems are judged on a handful of hand-picked examples. Production is thousands of messy inputs, provider outages, and a bill that grows faster than usage. The audit measures what's actually happening.
- Your AI feature is live, but customers report wrong or inconsistent answers.
- You're about to launch and aren't sure it will hold up under real traffic.
- Your model or GPU bill is growing faster than revenue.
- An enterprise customer or investor is asking hard questions about reliability and data handling.
- The engineer who built it has left, and nobody fully understands it.
Six areas where production AI fails.
Accuracy on real data
We build a test set from your real inputs and measure how often the system is right, where it fails, and why.
Reliability
Fallbacks, retries, timeouts, and what happens when a model provider slows down or goes offline.
Cost
Cost per request, model choice, caching, batching, and GPU utilization, with concrete ways to bring it down.
Security & data handling
Access control, sensitive data exposure, prompt injection risks, and readiness for HIPAA or enterprise security reviews.
Architecture & scale
Whether the design will hold up at the next order of magnitude, and what to change first if it won't.
Monitoring
Whether you'd know about a quality drop before customers tell you, and what to track if not.
Clear answers, not a slide deck.
Everything is written for both your engineers and your leadership: what's wrong, how much it matters, and what to do about it.
- Written audit report with findings ranked by business impact.
- A test set and measurement script built from your data, yours to keep and rerun after every change.
- Prioritized fix plan with effort estimates for each item.
- Cost breakdown with specific savings opportunities.
- Walkthrough call with your team to go through every finding.
- Optional follow-on: we can implement the fixes, or your team can run with the plan.
Two weeks from kickoff to answers.
Scope
A free 30-minute call to understand the system and agree on a fixed price.
Access
Read-only access to code, logs, and sample data, under NDA. We can work inside your environment.
Measure
We build the test set, run it, review the architecture, and trace costs.
Report
Written report, fix plan, and a walkthrough with your team.
We've fixed these problems in our own systems.
Reliability at scale
Circuit breakers, checkpointing, and real-time monitoring across a pipeline processing millions of videos.
GPU cost efficiency
8× H100 inference tuned to 80–90% utilization, with Modal for overflow capacity.
Redundancy & compliance
Redundant LLM pipelines with fallback providers, on HIPAA-compliant infrastructure.
Audit questions
How much does it cost?
It's a fixed price, quoted after a free 30-minute call once we understand the size of the system. No hourly billing and no surprises.
Will you see our sensitive data?
Only what's needed, under NDA, with read-only access. For regulated data we can work entirely inside your environment.
Do we have to hire you to fix things afterwards?
No. The report and fix plan are written so your own team can act on them. If you'd like us to implement the fixes, we'll scope that separately.
What kinds of AI systems do you audit?
LLM features and agents, voice agents, document processing, search and retrieval systems, computer vision and video pipelines, and self-hosted model deployments.
Know exactly where your AI stands.
Book a free 30-minute call. We'll tell you whether an audit is worth it for your system.