Service
I put LLM features into products that already have users — retrieval pipelines, streaming interfaces, evaluation, and the cost and failure handling that demos skip.
The gap between an AI demo and an AI feature is almost entirely in the parts nobody screenshots: what happens when the model returns malformed JSON, what a user sees during the eight seconds before the first token, what the feature costs at a thousand users instead of ten, and how you know a prompt change made things better rather than just different.
That is the work I do. I have shipped an AI content studio, a multi-provider resume optimizer, and a production RAG pipeline — and in each one the interesting engineering was in the failure handling and the retrieval quality, not the prompt.
I start by asking what the feature is allowed to get wrong. An internal document search that occasionally surfaces a mediocre result is fine. A customer-facing summary that invents a refund policy is not. The answer sets everything downstream: whether you need retrieval at all, how hard the grounding has to be, whether a human reviews the output, and how much you should spend per call.
For anything answering questions over your own content, retrieval quality dominates model choice. Most disappointing RAG systems I have looked at are not held back by the LLM — they are retrieving the wrong chunks, because the documents were split on a fixed character count that cut tables and headings in half. Chunking and metadata are where the wins are, and they are cheap to iterate on.
I also design for provider portability from the start. On HireOS that meant a bring-your-own-key model spanning five providers — OpenAI, Anthropic, Gemini, Groq, and Mistral — so users pay their own inference costs and no single vendor decision is load-bearing. Even when you standardize on one model, keeping the boundary clean means a migration is an afternoon rather than a quarter.
CodexGenAI generates multi-slide carousels and scores post-publish sentiment, built solo on Next.js and Supabase. HireOS tailors resumes against job descriptions across five AI providers with Cloudflare Workers as the edge inference proxy. The RAG knowledge base project runs an n8n ingestion pipeline into Supabase pgvector with Hugging Face embeddings and returns source citations alongside every answer.
A first AI feature in an existing product is usually a two-to-six week engagement depending on how much retrieval infrastructure it needs. If you are not yet sure the feature is viable, I would rather start with a short paid spike that proves or kills it on your real data than write a proposal for something neither of us can evaluate yet.
A first AI feature in an existing product is usually a two-to-six week engagement, depending on how much retrieval infrastructure it needs. If you are not yet sure the feature is viable, I would rather start with a short paid spike that proves or kills it on your real data than write a proposal for something neither of us can evaluate yet.
OpenAI, Anthropic Claude, Google Gemini, Groq, and Mistral, plus open models via Hugging Face where self-hosting makes sense. I keep the model behind a thin internal interface so a provider swap is a configuration change rather than a refactor — quality and pricing in this category move monthly, and you should not be locked to whichever model was best the month you shipped.
Often not. I start by asking what the feature is allowed to get wrong. An internal document search that occasionally surfaces a mediocre result is fine; a customer-facing summary that invents a refund policy is not. That answer decides whether you need retrieval at all, how hard the grounding has to be, whether a human reviews the output, and how much you should spend per call. Plenty of features that get specified as RAG are better served by a good prompt and a narrow data scope.
Cost controls are part of the build, not an afterthought. That means batching work that does not need to be live, caching what repeats, computing expensive scores as scheduled jobs rather than on every page load, and picking the cheapest model that passes the quality bar for each individual call rather than routing everything to the largest one.
Not to zero — anyone promising that is selling something. What you can do is make wrong output rare, visible, and contained: strict output contracts validated before the user sees anything, retries that feed the validation error back into the prompt, grounding answers in retrieved source text with citations, and designing the interface so an uncertain answer degrades into "I do not know" rather than a confident invention.
An AI content studio for creators and teams: generate multi-slide carousels, schedule posts across platforms, and track engagement with sentiment analysis.
Read the case study →An AI resume-optimization platform — tailors resumes to target roles, renders clean PDFs, and ships with a Chrome extension.
Read the case study →A retrieval-augmented document Q&A system with vector embeddings and semantic search over a custom knowledge base.
Read the case study →A 15-minute call is usually enough to scope it. If I am not the right fit, I will tell you on the call.