Independent consultant - data engineering to LLM & AI engineering
Data foundations.
Production AI.
I help teams move from data platforms to production LLM and AI systems - reliably, securely, and measurably. Most AI projects fail on the data layer, not the model layer. That is the part I know best.
- [ RAG pipelines ]
- [ Evals and observability ]
- [ Data quality for AI ]
- [ LLMOps ]
- [ Vector databases ]
- [ Streaming data platforms ]
01 / Who I help
Built for teams between demo and production
Engineering teams shipping their first LLM feature
You have a working demo. You need retrieval that does not hallucinate, evals that catch regressions, and an architecture your team can own.
Data teams extending their platform into the AI era
Your pipelines, warehouse, and governance are solid. You want to expose them to LLM workloads without rebuilding everything from scratch.
Founders who need a data-to-AI roadmap
You know AI is table stakes for your product. You need a realistic architecture and sequencing plan, not a slide deck.
02 / Core offers
Engagements that fit real teams
LLM application architecture
System design reviews for LLM-powered products: retrieval design, model selection, cost and latency budgets, failure modes.
RAG and evaluation pipelines
Production-grade retrieval pipelines with automated evaluation: retrieval quality metrics, regression suites, and CI gates.
Data platform to AI enablement
Extending existing data platforms for AI workloads: quality gates, semantic layers, and pipelines that feed LLM systems reliably.
Strategy and roadmaps
Focused engagements to define what to build first, what to measure, and what to safely defer.
Full details on the services page.
03 / Latest writing
How I think about this work
Evals are unit tests for non-deterministic code
If a prompt change can silently regress your product, you do not have an engineering process - you have a demo. A practical starting point for LLM evaluation suites.
Most LLM projects fail on the data layer, not the model layer
Teams debug prompts when they should be auditing retrieval. The failure mode I see most in stalled LLM projects is a data problem wearing a model costume.
Next step
Have data, need AI?
Tell me what you are building and where it is stuck. If I can help, we will figure out the fastest path from prototype to production.
Book a callUsually replies within 1-2 business days