AI integration
This is for a product that already exists and needs to get smarter: an assistant inside your app, search that understands what people meant, a manual process that shouldn't be manual. The product stays yours. The AI is a feature in it, not the whole thing.
The unglamorous truth is that most AI features fail on retrieval and evaluation rather than on the model. Getting the right context in front of the model, and knowing when its answer is wrong, is where the engineering actually lives. Prompt-only demos look great in a screen recording and fall apart in week two.
What this includes
- RAG pipelines: ingestion, chunking, embeddings, vector search
- Agents that call your existing tools and APIs
- LLM features wired into workflows your team already runs
- Evaluation, so you find out it's wrong before your customers do
- Cost and latency budgets, because both bite in production
What this isn't
I won't train a foundation model for you, and I'll usually argue you don't need a fine-tune when better retrieval would fix it more cheaply.
Typical stack
Where I've done this
"AI-Powered Chatbots for Business Growth" — a no-code platform to spin up chatbots trained on your own knowledge base, with a universal embed script for any site.
"Hire Smarter, Not Harder" — an AI resume-screening agent that reads, scores and shortlists candidates, and drafts rejection emails for the rest. Built and deployed solo.