AI engineering
LLM integrations, AI product features, agents, and the infrastructure that keeps model behaviour reliable.
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AI is useful when it is part of a system, not a demo. I integrate language models into products with explicit boundaries: structured outputs, validated tool calls, retries, permissions, observability, and a clear path when the model is wrong.
That includes document processing, retrieval and search, classification, assistant-style interfaces, and tool-using agents that need to touch real data safely.
What this can include
- LLM API integration and provider abstraction
- AI product features inside existing applications
- Tool-using agents with bounded permissions
- Structured extraction and document workflows
- Retrieval and intelligent search
- Observable latency, cost, and failure behaviour
Example engagements
- Add an assistant to an existing SaaS product
- Extract structured data from documents reliably
- Connect a model to internal tools with guardrails
- Replace a manual review queue with a model-assisted one
Examples describe the shape of the work, not past client engagements.
Related case studies
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