Applied AI engineering
I put language models into products where a wrong answer costs something.
Retrieval pipelines that cite their sources, checks that are not the model, and evals that tell you whether a change helped. Twenty years of shipping software, the last several building production AI systems, and the author of Agentic Workflows.
How I can help
Fixed prices with a written scope: what is in, what is out, and what you own at the end. Inside the scope the price does not move.
Grounding and eval review
I read your RAG or LLM feature end to end and tell you where it can show an answer nobody checked. You get a written report, a golden set to measure against, and the fixes ranked by risk.
Build a retrieval pipeline
Hybrid search, reranking and a citation check written in code, with a corpus you can trust and tests that never call the model. The pipeline degrades step by step instead of failing.
AI-assisted delivery for your team
Coding agents that plan before they build, with independent review and a merge gate a model can't talk its way past. Set up in your repository and taught to your engineers.
Recent work
All in production. Each one is something I designed and built.
Howard's Lantern: a question box that cites the law
A driving-law exam-prep app whose ask box answers a learner's question in two sentences from their own state's manual or vehicle code, with the section underneath.
- 172,486 law rows across all 51 states, fetched verbatim, dated and hashed
- BM25 and vector search fused by rank, a Haiku rephraser and reranker, then a check that refuses any figure not in a cited law
- A 190-case eval against the live model, run as a gate on pull requests
PalabraEdu: real-time education SaaS
A five-language multiplayer learning platform on Cloudflare Workers and Durable Objects, with payments, notifications and teacher dashboards.
- Built in six weekends
- 110,000 lines, 6,200+ automated tests
Quorum
An open-source Claude Code plugin that makes a coding agent write a falsifiable plan, then runs six independent review lenses, a judge, and a CI guard before a pull request opens.
Multi-tenant RAG platform on AWS
Hybrid Pinecone retrieval, Cohere reranking, Bedrock model routing, query decomposition and streamed answers, with the whole stack in AWS CDK.
On-device document intelligence
Commercial PII-redaction and RAG authoring apps for macOS and Windows: DistilBERT on ONNX, searchable PDFs, audit trails, signed offline licensing.
Books
The methods behind the work, written down so your team can use them too.
Agentic Workflows: Building a RAG Pipeline That Cites Its Sources
Build the Howard's Lantern pipeline by hand, one stage at a time, in TypeScript, and compare each stage with the production code.
On Amazon
The 25x Developer
Building real applications with AI agents, quickly, without giving up the testing that keeps them working.
On AmazonAbout
I've built software for twenty years: web, mobile and data platforms, a fleet-inspection app at Zonar that I moved from Angular to React in 60 days, and cargo systems for U.S. Customs and Border Protection. Since 2024 I've built production AI systems as a contractor with Pilot & Pixel, and for my own products. I studied applied mathematics at George Washington University and work from Frostburg, Maryland.
Tell me what you're building
A short note on the product, what the model does in it, and what goes wrong when it's wrong. I'll reply within two working days with whether I can help and what it would take.
Or reach me on LinkedIn.