Service · Audit
AI production audit
What AI actually runs in your company — sanctioned or shadow —, what data it touches, what will break before it breaks, and what it returns measured in money and hours. For systems about to launch or already live, in two to four weeks.
What it includes
Four deliverables, none optional.
Real AI inventory — shadow AI included
Every model, agent and integration in use, sanctioned or not, with the data it touches and who operates it. Most companies can name two or three tools and miss a dozen: you cannot govern what you have not found.
Failure modes of what is already live
Agentic systems fail quietly: drift, degraded output, invisible retries. We map the weak points of what runs today and who sees it first when it fails — your team or your customer.
Measured return against baseline
We instrument the workflows AI touches and measure cycle time, error rate and cost per task against a real baseline. Survey-reported gains are not ROI; the number you take to the board has to survive scrutiny.
Immediate governance fixes
The short list of what to fix now: accesses, data boundaries, traces — and the route to full applied governance if the stack calls for it.
Who it is for — and who it is not
Two moments, one check.
About to launch
Your pilot worked and production is next. Auditing before launch finds what will break under real users, real volume and real data — while fixing it is still cheap.
Already in production
The AI runs and nobody will swear to exactly what it does. We enter through the observable: what runs, what it touches, what it returns — and what to fix first.
It is not for you if…
You want a compliance stamp for the annual report. This is engineering: actionable deliverables, not certificates.
Frequently Asked Questions
Before auditing your AI.
What is an AI production audit?
It is the review of the AI that already runs in a company — not the one in the slide deck. It surfaces every model, agent and integration in use, sanctioned or shadow; maps what data each one touches; finds the failure modes of what is live; and measures what it returns against a real baseline, in money and hours.
How do you audit an AI agent before it goes to production?
By defining its limits before its capabilities: what it may do alone, what requires human approval, which data it can reach and what traces it leaves. Then testing against edge cases and adversarial inputs, verifying the guardrails hold in the execution path, and instrumenting it so its behavior is observable from day one.
What does an AI systems audit deliver?
A complete inventory of AI in use (sanctioned and shadow) with the data it touches; a prioritized list of what will break before it breaks, with failure modes and weak points; the measured return of the main workflows against baseline; and the immediate governance fixes. All in a document that survives board scrutiny.
How long does an AI audit take and what does it cost?
A typical audit runs two to four weeks depending on stack size. Pricing is scoped after a free Diagnostic over WhatsApp: you get an exact number after we see what runs in your company, not before.
Why audit shadow AI and not just the official tools?
Because in our field observations only around 35% of organizations officially provide AI tools to staff; in the rest, the AI people use runs outside official channels — shadow AI, ungoverned, with your data. Auditing only the official layer means reviewing the small part of the problem.
Next Step
What you do not know is running is what bites first.
Free Diagnostic over WhatsApp: tell us what AI runs in your company and we will tell you where the audit enters.
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