AI Integration
Most AI features fail in production because they were vibe-checked, not eval-checked. I bring an engineering discipline to LLM apps: retrieval pipelines that don't hallucinate, eval harnesses that catch regressions before deploy, and observability so you know exactly why an answer was wrong. The result: AI features you can defend in front of customers.
- RAG pipelines
- Agent frameworks
- Evals & guardrails
- LLMOps
- Cost optimization
- Observability stack
Use case
Define what the AI should do, what success looks like, and what failure costs.
Eval first
Write 100+ test cases before writing any model code.
Build
Implement retrieval, prompting, and post-processing with full tracing.
Harden
Add guardrails, rate limits, fallbacks, and cost monitoring.
Ship
Deploy with feature flags, monitor evals in production, iterate weekly.
Which models do you use?
Whichever fits the use case. I've shipped on OpenAI, Anthropic, open-source (Llama, Mistral), and small custom models. Model choice is a cost/quality/latency tradeoff.
Can you guarantee no hallucinations?
No — and anyone who says they can is lying. I can reduce hallucinations to <1% and make every answer auditable so you catch the rest.
Sound like a fit for your project?
I take on a handful of new engagements each quarter. If your problem is hard, the timeline is real, and you want a thoughtful partner — let's talk.