AI demos are easy. AI products are engineering. The gap is where most "AI features" die: a chatbot that confidently invents answers, a prototype that costs a fortune at scale, or a model integration so tightly coupled that switching providers means a rewrite. I build the version that ships and holds up.
I lead backend development on an AI healthcare platform that has analyzed more than 50,000 user scans, including a retrieval-augmented chat assistant and a custom-trained vision model serving layer. That is production AI with real users, not a weekend tutorial.
Grounded answers, not hallucinations
If your AI needs to be accurate about your product, your policies or your data, it needs retrieval-augmented generation. I build RAG pipelines that fetch relevant passages from your own content in a vector database and hand them to the model as grounding, so answers come from your material and the assistant says when it does not know. This was a hard requirement on the clinical product I work on, and it is the difference between a helpful feature and a liability.
Model-agnostic by design
Model providers leapfrog each other every few months. I design integrations so you can switch models in a config change, not a rewrite: a top-tier model for complex reasoning, a fast cheap model for classification, whatever the task needs. Your product should never be locked to one vendor's roadmap.
Custom model serving
When a generic API is not enough, I build the serving layer for your own models: queued inference through background workers, structured outputs with confidence levels, and a tier that scales independently from the rest of the app. I have done exactly this for a vision model processing user-uploaded medical images in production.
Costs and evaluation under control
Unbounded prompts and missing caching are how AI bills surprise people. I build in cost controls, caching and rate limiting from the start. And I build an evaluation set from real questions so you can measure whether the AI actually works and catch regressions on every change, instead of shipping quality drops invisibly.
Who this is for
Products that want to add a genuinely useful AI feature, not a chatbot bolted into the corner. If you have data and a workflow that AI could improve, I can build the feature that makes it real.
What you get
- An AI feature that is grounded in your own content, not one that makes things up
- A model-agnostic integration you can switch between providers without a rewrite
- Cost controls, caching and rate limiting so the feature is affordable at scale
- An evaluation set so you can prove the AI works and catch regressions
