Generative AI and LLM implementation
We build the retrieval and evaluation layers that decide whether an LLM product survives contact with real users. Serving the DC metro, Baltimore, and remote across the US.
Where LLM projects fail
Most generative AI projects treat the model as the product. The model is actually one component in a system that also has to retrieve the right context, evaluate its own output, and fail safely when it's wrong. Skip retrieval-augmented generation (RAG) and evaluation, ship the model alone: it looks fine in a demo and falls apart on the first question nobody rehearsed.
We build that system end to end: prompt design, RAG pipelines, vector search, tool use. Deployment targets are AWS, Azure, GCP, or self-hosted infrastructure. Model choice is one design decision among several: OpenAI, Anthropic Claude, Google Gemini, or an open-source model served through Ollama or vLLM. The evaluation set is what tells you whether a decision was right.
How it runs
An engagement is scoped in four phases.
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Scope
An engagement starts with the retrieval problem first: what "correct" means for this use case, what data actually answers it, and how a wrong answer gets caught before a user sees it.
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Build
The retrieval pipeline, the prompt and tool-use layer, and an evaluation set get built together, so a model swap always ships with a way to measure whether it helped. The evaluation set starts with known failure modes and grows from what production traffic actually surfaces.
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Evaluate
Every material change, including a new model or a prompt edit, runs against the evaluation set before it ships. Only a change that can be measured ships.
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Operate
Guardrails and logging are built in before launch, ahead of any incident. Monitoring watches for the same failure modes the evaluation set was built to catch. What gets handed off is something the team can run and change.
What you get
Two things ship at the end of an engagement.
A retrieval and evaluation pipeline
A RAG pipeline built for your data, plus an evaluation set that defines what "correct" means for this use case. You'll be able to tell whether the next change helped or just changed things. The evaluation set ships and gets maintained alongside the system.
A model layer that isn't locked in
Prompt and tool-use logic built to swap models without rewriting the layers beneath it.
What this is not
Not a foundation-model research shop. We build products on existing models. We don't train new ones.
Not staff augmentation. The engineer who designs the retrieval pipeline is the one who builds and evaluates it. There's no hand-off to a separate delivery team.
Who does the work
26 years in production software · federal delivery since 2014 (NOAA, HHS, FDA, VA, CMS, IRS)
As Chief Architect at a healthcare technology company, the firm's principal built multi-agent engineering pipelines with role-based governance, pre-flight validation, and automatic rollback, taking an internal delivery cycle from three months to two weeks. More on his record.
Related reading: The AI Speed Trap — And How I'm Building My Way Out
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