Model Performance Lab

Model Performance Lab: practical plan

Benchmark design

Dataset

Build a permissioned, representative sample across invoice layouts, languages, scan quality, currencies and edge cases. Redact personal and financial identifiers when they are not needed. Keep a held-out test set.

Gold labels

Have trained reviewers label supplier, invoice number, date, line items, tax and total. Resolve disagreements and document uncertainty rules.

Metrics

• Exact and field-level accuracy
• Numeric tolerance and currency correctness
• Completeness and false extraction rate
• p50/p95 latency and timeout rate
• Cost per correctly processed invoice
• Performance by document type and scan quality

Protocol

Use identical prompts, preprocessing, retry policy and hardware/provider settings. Run repeated trials if outputs are stochastic. Record model/version and date.

Decision

Choose based on business error cost and human review burden, not aggregate accuracy alone. Keep a manual fallback for low confidence.

Monitoring

Re-run benchmark after model updates, track drift and sample live output with approval. Protect source documents and avoid retaining unnecessary content.

Measurement and review

Record the baseline, source, date range, denominator and owner. Use one primary measure plus quality guardrails; compare like-for-like cohorts and disclose missing data. Treat estimates as scenarios, not observed outcomes.

Risks and controls

Check relevance, consent, disclosure, workload and downside before scaling. Keep an evidence trail, obtain human review for material commitments, and pause if trust, quality or compliance signals deteriorate.

Next steps

1. Confirm the goal, audience and available capacity.
2. Gather the minimum evidence for a first test.
3. Assign an owner, budget and review date.
4. Run a small pilot, record outcomes and decide whether to iterate, expand or stop.