POST/ai-integration
AI integrations
I wire language models into real applications and processes - not to impress on a demo, but to solve a concrete problem: speed up a process, support a decision, generate a useful suggestion. The first question I ask is always: what happens when the model answers wrong or slow.
The layer around the model, not just the API call itself.
This is for you if:
- you want to add an AI-powered feature to an existing application or process
- you have an idea for an LLM-based product but need someone to build the technical side
- your current AI prototype works in a demo, but you don't know how it holds up in production
What you get:
- An integration with a language model (OpenAI, Google Vertex AI) tailored to your case, not a generic wrapper
- A layer around the model: response validation, sensible defaults, handling errors and slow responses
- Iterative prompt refinement based on real cases, not just ones invented at a desk
- The AI feature wired into your backend or mobile app
example from my work
Unordered - an intelligent gift idea generator
I built an AI feature generating personalized gift suggestions in a Flutter app (Android/iOS/Web), running offline-first with data sync once the connection returns - backend on Firebase, infrastructure on Google Cloud Platform and Cloudflare Workers.
see detailsstack
- OpenAI API
- Google Vertex AI
- Python
- Firebase
- n8n