Ground Truth
Can your AI trust the data it's acting on?
- Source and pipeline review: what feeds the system, how stale, how clean
- Retrieval quality sampling on your real queries
- Permissioning and data-leak surface
You have an AI system in production — built in-house, by an agency, or over a long weekend with AI coding tools. In ten business days we tell you, with traces and numbers, whether it's trustworthy, what it really costs per task, and exactly what to fix first. By some industry estimates, more than 80% of AI projects fail — almost always quietly. This is how you find out early.
Every system we touch is checked the same way — the same three layers we use to build our own systems. You get a red / yellow / green score per layer, with the evidence behind every mark.
Can your AI trust the data it's acting on?
Does the system do what everyone thinks it does?
Would you know if it broke tomorrow?
Sample Scorecard · Day 10
Ground Truth
Needs workTwo stale sources feeding retrieval
Working Agents
HealthyTool errors handled; 3 edge cases found
Flight Recorder
MissingNo evals, no drift alerts, no baseline
Cost per task
$0.42 → $0.11
after top 3 fixes
Fix plan
7 items, each priced
the plan is the quote
Illustrative sample — your report scores your actual system.
An agency shipped it and moved on. The developer who built it left. The vendor answers on their timeline, not yours. The audit is how orphaned systems come in from the cold: we map what exists, score it honestly, and if you want, take over the keeping-it-alive part with AI Operations.
Every item in the fix plan comes with a fixed price attached. There is no follow-up proposal, no discovery phase, no “let's scope that.” You pick the items worth fixing, we start. And if the honest answer is that a $5 prompt change beats a $50K rebuild — that's what the report will say.
$7,500 fixed, credited toward operations if you stay. Read-only access is all we need to start.