We focus on the categories where evaluation is feasible — meaning we can prove the system works, not just hope it does. If your problem doesn't have a measurable success state, we'll tell you upfront.
Anyone can wire OpenAI to a frontend. The hard parts come later — model switching, eval pipelines, observability, cost control. Below is what we reach for first, and why. We swap when there's a real reason.
AI projects fail in week one or week ten. We front-load the failure modes — data audit, eval baseline, cost & latency targets — so by the time we ship, you know exactly what you're getting and what it costs to run.
Production AI without evals is hope wearing a deployment. Every Woyce build ships with eval pipelines, safety probes, and observability — the three things that turn a demo into a system you can trust.
Built a grounded, citation-first RAG assistant on a 12,000-document knowledge base. Hallucination rate under 2%, fully deployed in 10 weeks, in production with 85,000 monthly users.
Built a multi-step agent that enriches, scores, and routes inbound leads through five tools. Replaced a 4-hour SDR triage process with a 3-second one. Handles 1,200 leads daily.
Built a structured extraction pipeline for invoices, contracts, and purchase orders — typed JSON output, every time. Processes 8,000 documents monthly at 99.2% field-level accuracy.