A working AI system built for a specific task, not a demo. That's typically a retrieval-augmented generation pipeline grounded in your own documents or database, an agent workflow built in LangGraph or n8n that handles a multi-step process end to end, or an automation like lead scoring that replaces a manual triage step. Every pipeline ships with an evaluation harness — a set of test cases with known-good outputs — because an AI system that hasn't been measured against real cases is a system nobody can trust in production.
Work starts with the operational task, not the model: what decision or action needs to happen, what data it depends on, and where a human still needs to be in the loop. From there I build the retrieval or agent architecture around that task, set up the vector store and retrieval tuning if the pipeline needs one, and build the evaluation harness alongside the pipeline rather than after it, so quality gets checked as the system develops instead of at the end.
Teams that want an AI system that runs unattended in production — not a proof of concept that works in a demo and falls apart on real inputs. This includes businesses that want to automate a specific manual process, teams building a retrieval chatbot over their own knowledge base, and anyone who's had an AI pilot that never made it past the demo stage and wants to know why.
Tell me what's breaking — or what you're building.