AI is the surface, not a button
The core interaction is a model doing real work — answering from your data, taking actions, guiding a workflow — not a summarize toggle bolted onto an existing screen.
I'm a forward-deployed full-stack engineer. I embed with your team, start from the user problem, and build copilots, RAG, and agents — with the evals, guardrails, and full-stack system that make them safe in production.
What “AI-native” means here
The core interaction is a model doing real work — answering from your data, taking actions, guiding a workflow — not a summarize toggle bolted onto an existing screen.
Retrieval, structured outputs, and citations keep responses tied to your content and rules, so users can trust what they see.
Eval sets, guardrails, and cost / latency budgets are part of the build, not a cleanup pass after launch.
Capabilities
Interfaces
Streaming responses, tool-calling, citations, conversation state, and retry / stop controls built into your product surface.
Retrieval
Ingestion and chunking, embeddings, hybrid (keyword + vector) search, re-ranking, and retrieval evals so answers stay grounded.
Agents
Multi-step tool use, human-in-the-loop approvals, retries, timeouts, and predictable fallbacks when a step fails.
Production
Prompt and version management, eval harnesses, moderation and guardrails, observability, and cost / latency tuning.
Integration
Claude and OpenAI APIs, structured outputs, function calling, MCP servers, and background jobs for long-running work.
Full-stack
Auth, data models, payments, dashboards, mobile and web clients — the product still has to work when the model is not the answer.
Forward-deployed delivery
AI projects fail in the gap between a demo and a product. Being embedded — close to the users, the data, and the decisions — is how that gap closes.
01
I join your Slack, your standup, and your board. You are not chasing status updates or translating between teams.
02
The first week goes to the actual problem and real data — not a spec doc. We find where AI earns its place and where it does not.
03
A usable slice in week one, put in front of real users, then iterated weekly against feedback and eval results.
04
Discovery, build, deploy, measure, adjust. I stay on it through production hardening, not just the demo.
AI case studies
Small, real, open-source AI-native builds — each with a live demo and a published eval table. Client work stays private; this is how you see the engineering.
Chat over a document set with hybrid retrieval, inline citations, and a published eval table (accuracy, p95 latency, cost per query). Live demo + open source.
A streaming assistant inside a dashboard with real tool calls — query data, draft a record, summarize a view — and human confirmation before writes.
A small Model Context Protocol server exposing a real data source to Claude, with auth and scoped tools.
Engagements
Fixed-scope options to keep the first step small. Longer builds and embedded / retainer work are quoted after a scoping call.
1–2 weeks
Add one AI capability to an existing product — a copilot, a smart search, an assisted workflow — wired to real data with basic evals and guardrails.
3–6 weeks
A working AI-native app from problem to deployed release: retrieval pipeline, chat or agent interface, auth, and the dashboard around it.
3–5 days
You have an AI demo that works on a good day. I map what it takes to make it safe and affordable for real users, with a prioritized plan.
Before you commit
Bring the product goal, the data you have, and where you are stuck. You leave with an honest read on whether AI fits, what to build first, and a realistic route to production.
Start a conversation24h
A practical next step within a day, not a discovery maze.
Fit
If AI is not the right answer for a surface, I say so.
Plan
Priorities, risks, evals, and the fastest useful release.