AI and LLM product development

From an AI feature to a product people can use

I help product teams build the application around an AI capability: the model integration, data flow, user interface, backend and the checks that make failures visible. My core stack is TypeScript, React, Next.js, Node.js and PostgreSQL, with OpenAI, Anthropic and Gemini integrations where the product needs them.

AI product work is not limited to calling a model. It includes deciding what input is allowed, how output is structured, how errors are handled, what users can verify and which observations constitute a useful result.

Relevant production work

A practical engagement

1. Describe the user task, existing stack and the decision the AI feature needs to support.
2. Define an evaluation set and failure cases before selecting a production approach.
3. Build a narrow end-to-end workflow with explicit interfaces and observable errors.
4. Review evidence and decide whether to extend the scope, change the approach or stop.

The exact delivery plan depends on your product and access constraints. I do not quote a universal timeline or promise that adding AI will improve conversion.

What to send with an enquiry

Include the product context, the workflow you want to improve, your stack, whether a dataset or prototype exists and the outcome you need. If you need a technical review rather than implementation, say so: the scope can begin with architecture and evaluation.

• How I think about multi-LLM evaluation
• Technical leadership and architecture support