An AI feature is only as good as what it's grounded in.
I connect Claude, GPT, and other models to your actual data and systems — retrieval, function calling, and structured output — so answers are sourced from your business, not guessed from general training data.
Press "Run comparison" to see how the model answers with no access to your data.
This side pulls from your actual documents before answering.
where it breaks
Three ways an AI feature quietly fails
None of these show up in a demo with three test prompts. All of them show up in production.
Confident, wrong answers
The model fills gaps with plausible-sounding fabrication. Fix: ground every factual claim in retrieved source content and have the model cite it, not recall it.
Right data, wrong moment
Relevant context exists but never reaches the model. Fix: retrieval and prompt assembly tuned to the actual query pattern, not a fixed static prompt.
Slow, expensive at scale
Unbounded context windows and no caching turn a cheap demo into an expensive product. Fix: right-sized retrieval, caching, and model selection per task.
what I build
End-to-end AI feature engineering
From a first API call to a production system with retrieval, tools, and evaluation.
LLM API integration
Wiring Claude, GPT, or Gemini into your product with proper streaming, error handling, rate limiting, and cost controls from day one.
Retrieval-augmented generation
Chunking, embedding, and indexing your documents so answers are retrieved and cited from your own content, not recalled from training data.
AI agents & function calling
Agents that call real tools — your database, your APIs, your internal systems — with scoped permissions and human-in-the-loop confirmation where it matters.
Embeddings & vector search
Vector store selection and indexing pipeline design so retrieval stays fast and relevant as your document set grows.
Chat & copilot UI integration
Streaming chat interfaces, inline copilots, and structured-output features built into your existing product, not bolted on as a separate widget.
Prompt evaluation & fine-tuning
Test suites that catch regressions when a prompt or model version changes, plus fine-tuning when a general-purpose model genuinely isn't enough.
engagement
How an AI integration project runs
Grounding and evaluation come before scaling up — not after something goes wrong in front of a customer.
Define what the model is and isn't allowed to answer from
Deciding upfront which questions must be grounded in your data versus which are fine as general knowledge — this shapes the entire retrieval design.
Build the retrieval or tool layer
Chunking and indexing your content, or defining the tools an agent can call, with the same rigor as designing a public API.
Wire up the model with guardrails
Prompt structure, output schema, and fallback behavior for when retrieval comes back empty or a tool call fails.
Evaluate against real questions
A test set of actual queries your users will ask, checked for accuracy and citation correctness — not just a handful of happy-path prompts.
Ship with monitoring and a feedback loop
Deployed with logging on what was retrieved and answered, so weak spots in your knowledge base surface instead of staying invisible.
stack
Models and infrastructure I work with
Models & APIs
Vector stores
Frameworks
questions
Frequently asked
Which model should we actually use — Claude, GPT, or something else?+
It depends on the task more than brand: long-document reasoning, coding, and structured tool use each favor different models and even different sizes of the same model family. I typically prototype against two candidates on your real use case before recommending one, rather than defaulting to whichever is trending.
What's the difference between RAG and fine-tuning, and which do we need?+
RAG retrieves relevant information at answer time, so it stays current as your documents change and every answer is traceable to a source. Fine-tuning bakes patterns into the model itself and is better suited to teaching a specific style or format than teaching facts. Most "make it know our data" requests are RAG, not fine-tuning.
How do you keep an AI agent from taking a destructive action by mistake?+
Every tool the agent can call is scoped explicitly, with a dry-run or human-confirmation step required for anything destructive or costly — refunds, deletions, sending communications. The agent proposes; a defined boundary decides what happens without a human in the loop.
How do you measure whether the AI feature is actually accurate?
A test set built from real questions your users are likely to ask, run against the system and checked for correctness and citation accuracy before launch, then re-run whenever the underlying model, prompt, or documents change.
Can you add AI features to an existing app, or does it need to be built in from scratch?
Almost always the former. Most projects are adding a grounded chat feature, a copilot panel, or an agent workflow to a product that already exists, integrated into the current codebase rather than rebuilt around it.
What does a typical AI integration project cost and take?
A single grounded feature — one data source, one clear use case — usually takes two to three weeks. Multi-tool agents or larger knowledge bases take longer. I'll give a fixed-scope estimate after a short discovery call.
Ship an AI feature you can defend when someone asks "where did that answer come from?"
Tell me what you want the model to know and do — I'll tell you honestly what it takes to make it reliable.