AI features fail in production for unglamorous reasons: unbounded costs, non-deterministic output, and no way to tell whether a change made things better or worse. I build them with the same discipline as any other subsystem.
What I Build
- LLM-backed product features — summarisation, classification, drafting, and conversational interfaces wired into your existing stack.
- Structured extraction — JSON Schema-constrained output with validation and fallbacks, so downstream code can trust what it receives.
- Semantic search and RAG — vector embeddings, chunking strategies, and retrieval pipelines tuned against your own corpus.
- Tool use and agents — model-driven workflows that call your APIs under explicit guardrails and permissions.
- Evaluation harnesses — test suites for prompts and outputs, so quality regressions surface in CI rather than in support tickets.
Cost And Safety
Every integration lands with token budgeting, prompt caching where it applies, streaming responses for perceived speed, graceful degradation when the provider is down, and logging that lets you audit what the model was asked and what it returned.
Typical Engagements
- Adding a first AI feature to an established product without destabilising it.
- Replacing a brittle prototype with a production-grade, evaluated implementation.
- Building semantic search over documentation, catalogues, or internal knowledge bases.
Frequently Asked Questions
How do you integrate with my internal team?
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I offer embedded developer support or autonomous project delivery models. I integrate with your Slack/Jira, participate in daily standups, and enforce your Git workflow standards.
Who owns the Intellectual Property (IP) and source code?
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You retain 100% ownership of all IP, source code, commit history, and architectural documentation upon creation.
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