GenAI engineering that survives contact with real users.
LLM pipelines, prompt architecture, evals and guardrails — built for production traffic, not a weekend hackathon.
Get Started with GenAI Engineering
Free 30-min strategy call. I'll review your project and respond within 24 hours.
50+ founders consulted last month
Anyone can wire up an OpenAI API call. Shipping a GenAI feature that’s reliable, cost-aware, and safe under real user load is a different job — that’s what I do.
What you get
Every engagement is built around measurable outcomes — not just deliverables.
Prompt & pipeline architecture
Structured prompt chains, function calling, and orchestration that don’t break on edge cases.
Evals & guardrails
Automated evaluation suites and safety guardrails so quality doesn’t silently regress.
Cost-aware design
Model routing and caching strategies that keep token costs sane at scale.
Multi-model integration
OpenAI, Anthropic (Claude), and open-source models — chosen per task, not by default.
Beyond the demo
A GenAI demo is easy. A GenAI feature that handles edge cases, controls cost, and doesn’t hallucinate in front of a paying customer is not. I build the evaluation harness, the guardrails and the monitoring alongside the feature itself — so quality doesn’t silently regress after launch.
What’s included
- Prompt and pipeline architecture, including function calling and orchestration
- RAG systems with vector databases (Pinecone, pgvector, Weaviate)
- Automated evaluation suites and safety guardrails
- Cost-aware model routing and caching
From kickoff to results
A clear, transparent process — no surprises.
Use-case scoping
Define the exact job the GenAI feature must do, and where it’s allowed to fail safely.
Prototype & eval
Build a working prototype with an evaluation harness from day one, not as an afterthought.
Production hardening
Add guardrails, monitoring, fallback models, and cost controls before launch.
Iterate on real usage
Use production logs and evals to keep improving prompt quality after ship.
01Which LLM providers do you work with?
OpenAI, Anthropic (Claude), and open-source models via providers like Together or self-hosted where it makes sense.
02Can you build RAG systems?
Yes — retrieval-augmented generation with vector databases (Pinecone, pgvector, Weaviate) is a core part of the work.
03How do you handle hallucination risk?
Grounded retrieval, structured outputs, automated evals, and human-review checkpoints for anything customer-facing.
04Do you build the whole product or just the AI layer?
Either — I can own the full feature end-to-end, or plug into your existing engineering team as the GenAI specialist.
Ready to get started?
Book a free 30-minute strategy call. No pitch, no pressure — just honest advice on where to focus.