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.
LLM integration means embedding large language models like GPT-4 or Claude into your business applications to power features like document Q&A, AI assistants, automated summarization, and intelligent search.
Yes. We integrate OpenAI GPT-4, Anthropic Claude, Google Gemini, and open-source LLMs into existing web and mobile applications using LangChain and direct API integrations.
RAG (Retrieval-Augmented Generation) grounds LLM answers in your own documents and data using vector databases like Pinecone or Weaviate. Yes, we build RAG-based chatbots and Q&A systems.
Yes. We build autonomous AI agents using LangChain Agents, LangGraph, and OpenAI Assistants that can use tools, browse the web, query databases, and complete multi-step tasks.
Who leads this work
AI Automation & Data Engineer
Turns business processes into AI workflows and builds the data underneath them — ingestion, pipelines, and the knowledge bases agents retrieve from.