RAG systems people can verify
Retrieval design, document pipelines, grounding, citations and evaluation for products that need to answer from trusted knowledge.
- RAG architecture
- Evaluation harnesses
- Search and retrieval quality
Available for selected AI projects · Worldwide
The engineering between an AI prototype and production.
I help teams turn promising AI prototypes into products people can depend on. That usually means working across RAG, agents, evaluation, security, full-stack software and cloud delivery.
01 / SERVICES
Deep AI work.
Full-product context.
The model is only one part of the product. The data, interface, permissions and recovery paths need just as much care.
Agents that handle interruptions
Stateful workflows with tool use, human handoffs, permission boundaries and recovery paths for the moments a demo never shows.
- Agent state machines
- Human-in-the-loop flows
- Tool and permission design
The software around the model
Product discovery, Next.js interfaces, APIs, data models, AWS deployment and production monitoring built as one connected product.
- Product prototyping
- Full-stack implementation
- AWS delivery
02 / WORKING MODEL
Start with the
hardest unknown.
I test the riskiest decision first, then widen the work into implementation once there is evidence the approach can hold up.
Frame the outcome
Clarify the user decision, available evidence, risk, and what success looks like before choosing a model.
Prove the hard part
Prototype the riskiest workflow with representative data and an evaluation approach, not only a polished happy path.
Build the product system
Connect the model layer to the interface, services, permissions, state, observability, and recovery paths.
Launch and improve
Ship with measurable quality signals, document operational decisions, and refine from real usage.
03 / COMMON QUESTIONS
Before we start.
What kinds of AI projects are the best fit?
Products that need RAG, agentic workflows, LLM evaluation, secure AI integrations, or a senior engineer who can own both the AI layer and the surrounding full-stack system.
Can you improve an existing AI prototype?
Yes. I start by finding where it breaks with real data, ambiguous requests, tool failures, security constraints or heavier use.
Do you work only on the model layer?
No. My advantage is connecting AI architecture to product UX, backend services, data, cloud infrastructure, analytics, evaluation, and launch.
How does a project begin?
We start with the business outcome, current system, constraints, and riskiest assumptions. From there I recommend a focused discovery, prototype, implementation, or technical audit.
04 / NEXT STEP
Let’s work out what to tackle first.
Share what you are building, the constraints and where the current approach stops working.