THE AI DATA ACCURACY AUDIT
One week. Thirty questions. Your real number.
A fixed-price benchmark of how accurately your AI agents answer real business questions, with every failure explained and a plan to fix it. You keep the eval suite either way.
AI Data Accuracy Audit
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Raw-schema accuracy score
Why measure first
Every vendor in this space wants to sell you a build. We won't scope one until we've measured, because half of what we learn in audits changes the plan, and occasionally the measurement says you don't need us. A small, fixed measurement beats a big guess.
What we need from you
- A read-only replica or masked snapshot (we sign NDAs before anything else; we never receive write or production access)
- 60 to 90 minutes with the people who ask the questions
- 30 minutes with whoever can confirm ground truth
What lands on your desk Friday
Executive page
the score, in one glance
Methodology
exactly how we tested, so the result survives scrutiny
Question-by-question table
AI's answer vs. verified answer, pass/fail
Failure taxonomy
the pattern behind the misses
Fix roadmap
prioritized, estimated, tool-agnostic
Eval suite handover
how your team reruns it forever
If your stack turns out to be one we can't benchmark, you pay nothing.
After the audit
Three doors, all fine: your team runs the fix plan themselves (it's written to be handed over) · we scope the Foundation from the findings · or the score was healthy and you keep the proof. About the only wrong move is not measuring.
Questions we get.
It already does, confidently, all day. That's the problem: published evaluations score it under 50% on real business questions, because your definitions and ground truth were never written down where it can see them. We don't compete with the agent. We build the layer of verified definitions it needs, and prove it with a before/after score. Think you're already accurate? The audit is the cheapest way to be sure.
Good. They'll own everything we hand over: documented, version-controlled code. Two questions first: has anyone measured your current accuracy? And what falls off their roadmap during the six to eight weeks this takes to build from scratch? We've done it repeatedly, so it's fixed scope. The audit gives your team the measurement either way.
A read-only replica or masked snapshot, your choice. No write access, no production, NDA first. Some clients run the eval suite themselves and share only outputs.
Then $2,500 bought you proof your agent is trustworthy, a claim almost no company can make. You keep the suite to re-verify whenever models or definitions change.
Find out what your AI actually scores.
One week. Thirty questions. A number instead of a hope.