AI Agent Evaluation · Chicago, IL

AI agent evaluation in Chicago.

Chicago's AI runs on documents with money attached: rate confirmations that bill loads, claims that pay providers, invoices that close books. Evaluation here is operational: field-level accuracy an ops manager can staff against, thresholds the floor co-signed, and drift alarms that fire before the error report does.

We build evaluation systems for document AI and operational agents: labeled sets from your real paperwork, threshold curves priced in your error costs, input and outcome drift monitors, and bake-off harnesses that put vendor claims under your own scoring rules.

How we work

Tell us the documents and what an error costs.

The technical reality of document-AI evaluation in Chicago.

Field-level measurement is the only honest kind: per-field precision and recall on labeled samples of your real documents, paired with confidence calibration so the threshold curve tells ops exactly what auto-posts, what routes to review, and what leaks. Document-level averages are how mis-billed loads hide inside good-looking numbers.

Thresholds get set by pricing errors, not by engineering intuition: leakage costs per field against review-touch costs, run through the curve, co-signed by ops with the queue forecast attached, and revisited quarterly against measured reality.

Drift gets caught on both sides: input fingerprints (layout clusters, OCR quality, source mix) alarm before accuracy degrades; outcome monitors (confidence distributions, correction rates) confirm and trigger eval-set refreshes. Corrections sampled back monthly keep the ground truth aging with the paperwork.

Vendor claims meet blind bake-offs on your withheld sample under your scoring rules, and the same harness becomes contract language: acceptance thresholds with teeth, re-run at renewal.

What an evaluation engagement includes.

The standard build, tuned for high-volume document operations.

Labeled document samples

Representative sets per document type across your carrier or payer variety, labeled with your team and maintained as the system of record.

Field-level accuracy harness

Per-field precision, recall, and confidence calibration, reported as threshold curves rather than averaged brag numbers.

Ops-priced threshold policy

Error costs and review costs run through the curve, co-signed by operations, with queue-volume forecasts staffing can plan on.

Drift monitors, both sides

Input-stream fingerprinting and outcome control bands, with monthly correction sampling that keeps the eval set current.

Vendor bake-off protocol

Blind comparisons on withheld samples under identical scoring, and acceptance criteria written into contracts as measured thresholds.

HIPAA-frame compatibility

For claims and EOB pipelines, evaluation runs inside BAA channels with audit logging intact and PHI handled per your existing controls.

Built for Chicago's operational stakes.

Chicago operations have been promised accuracy by software vendors for decades, which is why the credible move here is measurement under the buyer's own rules: your documents, your labels, your error costs. Evaluation built that way converts AI from a claim into a managed process, the same way the floor already manages quality everywhere else.

The deliverables are designed for the operating review, not the data-science deck: threshold curves, queue forecasts, leakage rates, and drift alarms that route to the people who own the consequence.

We work with Chicago teams remotely, in Central hours, with first harnesses typically standing in two to three weeks.

Questions from Chicago buyers.

How is extraction accuracy actually measured so ops can staff against it?: Field-level, per document type, with the costs of each error class attached. Document-level 'accuracy' is an averaging trick: a rate confirmation with nine fields right and the rate wrong is not 90 percent fine; it is a mis-billed load. The harness we build labels a representative sample of your real documents (a few hundred per type, drawn across carriers or payers so layout variety is in the set), scores each field for exact-match or tolerance-band correctness, and reports per-field precision and recall alongside the confidence scores the system emitted. That last pairing is what ops can staff against: it tells you, at any confidence threshold, what fraction of documents auto-post cleanly versus route to review, and what error rate slips through. The deliverable is a threshold curve, not a brag number: 'at 0.85 confidence, 78 percent auto-posts with 0.4 percent field-error leakage; review queue is 22 percent' is a sentence an operations manager can plan a shift around.
How do we set acceptance thresholds with our ops team instead of having engineering guess?: By pricing the two error directions and letting the queue do the arithmetic. The workshop we run with ops leadership: name the cost of a leaked error by field (a wrong reference number costs a phone call; a wrong rate costs real money and a relationship) and the cost of a review touch (minutes at loaded rates). With the threshold curve from the eval harness, those two prices make the decision mechanical: thresholds tighten on the fields whose leakage is expensive and relax where review touches cost more than the errors they catch. The output is a per-field, per-document-type threshold policy that ops co-signed, plus the queue-volume forecast it implies, so staffing is a plan rather than a surprise. We revisit the policy quarterly against measured leakage and queue data, because the right threshold drifts as the system and the document mix evolve, and a co-signed policy that never updates becomes folklore with a signature.
Our document mix changes: new carriers, new payer formats, seasonal shifts. How does evaluation catch drift?: With monitors on both the inputs and the outcomes, because drift announces itself in two different ways. Input-side: we fingerprint the incoming document stream (layout clusters, OCR quality scores, source mix) and alert when the distribution moves, a new carrier's format spiking, scan quality dropping from a particular office, a payer revising their EOB layout. That alert fires before accuracy visibly degrades, which is the point. Outcome-side: confidence-score distributions, review-queue rates, and human-correction rates are tracked per document type with control-band alerts; when corrections rise on a field, the eval set gets refreshed with the new examples and re-labeled, and the threshold policy gets re-run. The discipline that makes this work in Chicago operations is sampling the corrections back into the labeled set monthly, so the ground truth ages with the document mix instead of fossilizing at launch.
A vendor claims 99 percent accuracy on our documents. How do we verify before signing?: With a blind bake-off on your own labeled sample, which is exactly the artifact an evaluation engagement leaves you holding. The protocol: a few hundred of your real documents, labeled by your team (or by us with your team's sign-off), withheld from every vendor; each candidate system processes the same set; field-level scoring runs identically across them; and the comparison reports per-field accuracy, confidence calibration (does their 0.9 mean 90 percent), throughput, and failure behavior on the ugly tail (skewed scans, handwritten notes, multi-page exceptions). Vendor accuracy claims almost always describe their best document class under their own scoring rules; your freight mix or claims mix is the only benchmark that predicts your outcomes. The same harness then serves contract enforcement: acceptance criteria written as measured field-level thresholds on a maintained sample, re-run at renewal, which converts '99 percent' from a brochure adjective into a term with teeth.

Ready for accuracy numbers ops can staff against?

Tell us the document types, the volumes, and what an error costs. We reply within one business day with a scope and a fixed price.

AI agent evaluation in Chicago.

The technical reality of document-AI evaluation in Chicago.

Vendor claims meet blind bake-offs on your withheld sample under your scoring rules, and the same harness becomes contract language: acceptance thresholds with teeth, re-run at renewal.

What an evaluation engagement includes.

The standard build, tuned for high-volume document operations.

Labeled document samples

Representative sets per document type across your carrier or payer variety, labeled with your team and maintained as the system of record.

Field-level accuracy harness

Per-field precision, recall, and confidence calibration, reported as threshold curves rather than averaged brag numbers.

Ops-priced threshold policy

Error costs and review costs run through the curve, co-signed by operations, with queue-volume forecasts staffing can plan on.

Drift monitors, both sides

Input-stream fingerprinting and outcome control bands, with monthly correction sampling that keeps the eval set current.

Vendor bake-off protocol

Blind comparisons on withheld samples under identical scoring, and acceptance criteria written into contracts as measured thresholds.

HIPAA-frame compatibility

For claims and EOB pipelines, evaluation runs inside BAA channels with audit logging intact and PHI handled per your existing controls.

Built for Chicago's operational stakes.

The deliverables are designed for the operating review, not the data-science deck: threshold curves, queue forecasts, leakage rates, and drift alarms that route to the people who own the consequence.

We work with Chicago teams remotely, in Central hours, with first harnesses typically standing in two to three weeks.

Questions from Chicago buyers.

How is extraction accuracy actually measured so ops can staff against it?

Field-level, per document type, with the costs of each error class attached. Document-level 'accuracy' is an averaging trick: a rate confirmation with nine fields right and the rate wrong is not 90 percent fine; it is a mis-billed load. The harness we build labels a representative sample of your real documents (a few hundred per type, drawn across carriers or payers so layout variety is in the set), scores each field for exact-match or tolerance-band correctness, and reports per-field precision and recall alongside the confidence scores the system emitted. That last pairing is what ops can staff against: it tells you, at any confidence threshold, what fraction of documents auto-post cleanly versus route to review, and what error rate slips through. The deliverable is a threshold curve, not a brag number: 'at 0.85 confidence, 78 percent auto-posts with 0.4 percent field-error leakage; review queue is 22 percent' is a sentence an operations manager can plan a shift around.

How do we set acceptance thresholds with our ops team instead of having engineering guess?

By pricing the two error directions and letting the queue do the arithmetic. The workshop we run with ops leadership: name the cost of a leaked error by field (a wrong reference number costs a phone call; a wrong rate costs real money and a relationship) and the cost of a review touch (minutes at loaded rates). With the threshold curve from the eval harness, those two prices make the decision mechanical: thresholds tighten on the fields whose leakage is expensive and relax where review touches cost more than the errors they catch. The output is a per-field, per-document-type threshold policy that ops co-signed, plus the queue-volume forecast it implies, so staffing is a plan rather than a surprise. We revisit the policy quarterly against measured leakage and queue data, because the right threshold drifts as the system and the document mix evolve, and a co-signed policy that never updates becomes folklore with a signature.

Our document mix changes: new carriers, new payer formats, seasonal shifts. How does evaluation catch drift?

With monitors on both the inputs and the outcomes, because drift announces itself in two different ways. Input-side: we fingerprint the incoming document stream (layout clusters, OCR quality scores, source mix) and alert when the distribution moves, a new carrier's format spiking, scan quality dropping from a particular office, a payer revising their EOB layout. That alert fires before accuracy visibly degrades, which is the point. Outcome-side: confidence-score distributions, review-queue rates, and human-correction rates are tracked per document type with control-band alerts; when corrections rise on a field, the eval set gets refreshed with the new examples and re-labeled, and the threshold policy gets re-run. The discipline that makes this work in Chicago operations is sampling the corrections back into the labeled set monthly, so the ground truth ages with the document mix instead of fossilizing at launch.

A vendor claims 99 percent accuracy on our documents. How do we verify before signing?

With a blind bake-off on your own labeled sample, which is exactly the artifact an evaluation engagement leaves you holding. The protocol: a few hundred of your real documents, labeled by your team (or by us with your team's sign-off), withheld from every vendor; each candidate system processes the same set; field-level scoring runs identically across them; and the comparison reports per-field accuracy, confidence calibration (does their 0.9 mean 90 percent), throughput, and failure behavior on the ugly tail (skewed scans, handwritten notes, multi-page exceptions). Vendor accuracy claims almost always describe their best document class under their own scoring rules; your freight mix or claims mix is the only benchmark that predicts your outcomes. The same harness then serves contract enforcement: acceptance criteria written as measured field-level thresholds on a maintained sample, re-run at renewal, which converts '99 percent' from a brochure adjective into a term with teeth.

Ready for accuracy numbers ops can staff against?

Tell us the document types, the volumes, and what an error costs. We reply within one business day with a scope and a fixed price.