How to build an LLM evaluation harness before production.
Build an LLM evaluation harness from real cases: acceptance criteria, labelled sets, per-field scoring, cost and latency, rerun on every change.
Related service: Production AI
Guides
Engineering guides for systems that have to hold up.
Practical notes on the parts of integration, document processing and production AI that decide whether a system can be trusted: evaluation, failure handling and review.
Idempotent integrations: retries, duplicate events and failed handoffs.
Build idempotent integrations that survive retries: idempotency keys, timeouts as unknown outcomes, transient versus permanent failures, a trace per record.
Related service: System integration
Confidence thresholds in document extraction: when a field goes to review.
Set document extraction confidence thresholds per field: calibrate scores against checked answers, route uncertain fields to review, sample what you accept.
Related service: Document processing
Bring one concrete workflow
Describe the work as it happens today: who does it, how often, and what breaks. You will hear back from a founder.
Discuss your workflow