You have a live product with real users, and you want AI inside it without tearing down what already works. Maybe a feature request keeps coming up, maybe a competitor just shipped something, or maybe you can see a workflow your users do by hand that a model could do for them. The hard part is not the demo. The hard part is making AI reliable, affordable, and safe to run in production next to code people already depend on. That is the work I do.

What I Build

I add AI features to apps that already exist. The concrete deliverables depend on what you need, and usually it is some mix of these. LLM features that sit inside your product, things like drafting, summarizing, rewriting, classifying, extracting structured fields from messy input, and answering questions over your own data. Retrieval and search so the model grounds its answers in your content instead of guessing, including embeddings, chunking, and a vector store wired into your existing database. Generation flows for text, and image or audio where it fits, with the queueing and storage to handle them. Structured output that returns clean JSON your code can trust, validated and typed, instead of a paragraph you have to parse and pray over. Underneath all of that, the unglamorous parts that decide whether it survives contact with real traffic. Cost-aware model choice so you are not paying flagship prices for work a smaller model does fine. Reliable async and webhook handling so long-running jobs do not block a request or silently die, with retries, idempotency, and a way to see what failed and why. Sensible guardrails, rate limits, and logging so you can watch spend and quality instead of finding out from an angry user.

How I Work

I work solo, and that is the point. There is no account manager, no handoff between a salesperson and the people who actually write the code, and no week lost while a team gets staffed. You talk to the person building the feature, every time. I scope honestly before I start. I tell you what is a few days, what is a few weeks, and what is genuinely hard, because the AI part is often easy and the integration with your real data and your real edge cases is the long pole. I keep you in the loop with working pieces you can click, not status decks, so you can correct course early instead of at the end. I am heavily AI-augmented in my own workflow, which means I move faster than a small agency on the same brief, and the savings show up in your bill and your timeline. You get a senior pair of hands that knows both the model side and the production side, instead of paying overhead for layers that do not touch the keyboard.

Proof

I ship this kind of thing for myself, not just for clients. You can see a range of it in my work. Tool Index is a live search product I built and run, search, indexing, and the plumbing that keeps it fast. Melodex is a live generation product with real async pipelines, the exact shape of work this service is about. I also built and run Apatero Studio, an AI generation product, so the model layer, the cost tradeoffs, and the queue reliability are problems I have already solved under load, not in theory.

Common Questions

How do we start? A short call where you show me the product and the feature you want. I follow with a written scope that names the deliverables, the risks, and a realistic timeline, so you can decide with real information. How long does it take? Small, well defined features can land in days. A retrieval system or a full generation pipeline with proper async handling is usually a few weeks. I give you the honest range up front and flag anything that turns out bigger than it looked. How does pricing work? I scope the work and we agree on it before I start, either as a fixed project or by the week for open-ended work. No surprise invoices. If the scope changes, we talk first. Do I have to rebuild my app? No. The whole premise is adding to what you have. I work inside your stack and your codebase, and I leave it cleaner than I found it. Ready to add AI to your product the right way? book a call.