Services_

Connecting your product to an AI model is the easy part. What actually matters is making it reliable: giving it the right tools to act on your behalf, grounding its answers in your own data instead of letting it guess, controlling what each request costs and how fast it responds, and testing changes before they reach a customer instead of finding problems after they do. I build that whole system, not just the connection to the model.

Running AI models yourself — instead of paying per request to a provider like OpenAI — is mostly an operations problem, not a model problem: keeping the service running, managing the graphics hardware it depends on, shrinking models to fit what you have, and shipping updates without downtime. hal0, the platform I built and run at home, is proof of that discipline: it targets AMD hardware most AI tooling ignores by default — built on AMD's ROCm software instead of the usual Nvidia path — and it runs ten different AI features (chat, search, transcription, speech, image generation) behind one API, each one isolated in its own container so a failure in one doesn't take down the rest. I bring that same operating discipline to wherever your models need to run — your own servers, a cloud account you control, or a mix of both.

Most 'AI features' are a chat box bolted onto an existing app. I build applications where the AI model is part of the core logic from the start, not an add-on — with reliable data handling, clear error messages when something goes wrong, and a setup any other developer can pick up and maintain. The same standard I'd hold any product to, AI or not.