I wire language models into real products and real workflows - content pipelines, translation, data extraction and classification, agent-driven automation over your own tools and data via MCP.

The difference between an AI feature that works and one that quietly embarrasses you is not the prompt. It is the plumbing around it: retries, idempotency, cost control, and knowing which parts of the job the model should never be trusted with. I use these tools every day in my own products, so I know where they hold up and where they fail.

Typical jobs: an automated content or translation pipeline, an internal tool that reads your data and answers questions about it, a support or classification flow, or an MCP server that lets Claude operate your own systems.