Proof of Concept

A written report covering: data quality assessment with specific examples, reproducible model evaluation with accuracy numbers, infrastructure and scaling findings, security review, and a prioritized task list with time estimates.

Quick wins separated from architectural fixes – so you know what to do this week vs what to budget for next quarter.

Yes. Many audits happen before any AI is built – checking whether your data is good enough, whether your architecture can handle what you’re planning, and whether AI is actually the right answer for the workflow.

Sometimes the recommendation is “don’t build AI for this – here’s a simpler approach.” Better to know before you spend.

Common patterns: data labeling errors that no amount of GPU compute can fix, prototypes built fast with no scaling plan, missing security basics (exposed API keys, no rate limits, no prompt injection protection), inefficient model choices that quietly drive up monthly bills, and architecture decisions that look fine at low load and break at scale. The audit catches issues that look small until they hit production.

Standard NDA before any access. Data stays in your environment whenever possible; when we need samples, we use your secure transfer of choice. We don’t reuse client code or data for anything outside your engagement. Same protections we use on long-term builds for Stevens Industries and Internet Archive.

The audit report is yours – take it to any vendor or hand it to your in-house team. Many clients do exactly that for at least some of the fixes. We’ll also tell you upfront if a different team is better suited for the build (e.g., niche domain expertise we don’t have). Independence is the point of an audit.