AI agent evaluation in Minneapolis.

The technical reality of quality-system evaluation in Minneapolis.

Questions from Minneapolis buyers.

How do we verify appeals drafts are faithful to the case record before a specialist signs them?

Claim-level verification against the case file, with the error classes appeals work actually fears. The harness decomposes each draft into assertions (clinical facts cited, policy provisions referenced, dates and determinations described) and verifies each against the case record and policy library: supported, unsupported (asserted but absent from the record: the fabrication class that ends programs in front of regulators), contradicted (the draft misstates what the record shows), and omitted (record facts material to the appeal missing from the draft, which weakens cases silently). Required regulatory language gets checked deterministically. Calibrated judges carry the volume after agreement with specialist-graded samples is demonstrated, and results report per error class and per appeal type, with the strictest gates on external appeals and anything DOI-visible. The specialist's review stays in the workflow; what the evaluation changes is what that review costs: reading against a verified draft with flagged uncertainties is minutes, reading defensively against an unverified one is the half-hour the program was supposed to save.

Our complaint-coding consistency worries us before FDA inspections. Can evaluation make it inspectable?

Yes, by converting consistency from an impression into a measured, monitored property. The build: paraphrase suites (real complaint narratives reworded, reordered, translated from call-transcript style to email style) verify that equivalent narratives code identically, which catches the prompt sensitivity that makes model-assisted coding quietly arbitrary; a stratified sample (weighted toward MDR-reportability boundaries and severity edges) gets blind-graded by your quality engineers, producing agreement rates per code category against the human standard; and historical re-scoring runs detect where legacy coding diverged, surfacing trends inconsistency had hidden. The records this produces are inspection-shaped: documented methodology, versioned runs, agreement statistics, and the explicit boundary that reportability determinations remain human with the model as assembly support. Inspectors respond to exactly that structure: a tested system with preserved human judgment reads as quality management; an untested one with enthusiastic adoption reads as a finding waiting to be written.

Who grades product-content quality at retail scale, and how does that stay manageable?

Your merchandisers set the standard; calibrated judges enforce it at catalog scale; sampling keeps the loop honest. The setup: merchandising leads grade an initial set (200 to 300 items across categories, scored against a rubric we build with them: taxonomy correctness, claim accuracy against product data, brand-voice compliance, completeness), which costs each lead a few hours and produces the anchored standard. Judges tuned to that standard then score full catalog runs, with items failing thresholds queued for human review rather than shipping. The maintenance loop is risk-tuned sampling: heavier on new brands, regulated categories (supplements, children's products), and recently changed prompts; lighter on stable refreshes, with merchandiser re-grades of 30-to-50-item monthly samples keeping the judges anchored. The practical effect at Twin Cities retail volume: full-catalog quality coverage at the cost of a few merchandiser-hours monthly, with review attention concentrated exactly where category risk lives, and a quality dashboard that survives the question 'how do you know.'

What does inspection-ready mean for AI evaluation records in a quality-system culture?

It means the records look like the rest of your quality system: versioned, attributable, and retrievable on demand. The set we build to that standard: documented intended use per AI system (what it assists, what stays human, in language your quality manual already uses), evaluation protocols as controlled documents (methodology, sample designs, acceptance criteria, with revision history), run records linking every evaluation execution to the model, prompt, and eval-set versions involved, with results against criteria; change records showing that model or prompt updates triggered re-evaluation before redeployment; and CAPA-compatible issue records: when evaluation catches a failure, the investigation, correction, and effectiveness check get documented in the format your quality system already audits. None of this is exotic; it is your existing document-control discipline applied to a new system class. Teams with mature quality cultures (med-device especially) adopt it fastest, because the frame is familiar; the work is mapping evaluation artifacts into it, which we deliver as part of the build.