Put AI where it actually helps your product.
Not a demo that impresses for a week, but AI wired into your real software — a chatbot that answers from your own data, a feature that drafts or summarises, a workflow that stops needing a human for the boring part. I connect the OpenAI and Claude APIs into the apps you already run, and make them reliable. Remote, worldwide.
AI features that live inside your product.
The value isn't the model — it's how well it's wired into your data, your app and your workflow. Here's the kind of AI integration I build, with the plumbing done properly.
Chatbots & assistants
Support and internal assistants that answer in your voice — with guardrails, so they help customers instead of inventing answers.
Answers from your own data
Retrieval over your documents, database or knowledge base (RAG) so the AI responds from your content, with sources — not just the internet.
Document processing & extraction
Reading invoices, contracts, forms and emails — pulling out the structured data your systems need, at a fraction of the manual time.
Workflow automation
Drafting, classifying, summarising and routing — the repetitive text work that used to need a person, done as a reliable step in your process.
Wired into your app
The AI feature built into your existing .NET or React app and its API — not a separate tool your team has to remember to use. See backend work →
Cost, safety & fallbacks
Token and cost control, prompt-injection care, rate limits and graceful fallbacks — so the feature is safe to put in front of real users.
What every AI integration includes
An AI feature is only useful if it's reliable, controllable and honest about what it doesn't know. Every build ships with that engineered in.
- OpenAI / Claude API integration in your app
- Retrieval over your own data, with sources
- Prompt design & guardrails against bad output
- Token & cost controls, with usage visibility
- Prompt-injection and safety handling
- Graceful fallbacks when the model is slow or down
- Clean API layer so you can swap models later
- Honest limits — where AI helps, and where it shouldn't decide
A software engineer first, using AI as a tool
The hard part of an AI feature isn't calling the API — it's the data access, the app it lives in, and the reliability around it. That's ordinary engineering, and it's what I do.
- Full-stack: I build the app the AI plugs into, too
- AI kept behind a clean interface, not scattered everywhere
- Realistic about what AI should and shouldn't do
- Your data handled carefully, not sent everywhere
- Honest scope and a clear quote up front
- You own all the code and the prompts
From an idea to a reliable AI feature, in five steps.
Frame
What the AI should actually do, where it helps, and where a human still decides. We agree the scope honestly — including what's out.
Data
Getting the AI access to the right content — your documents, database or knowledge base — cleanly and safely.
Build
The prompts, retrieval and API layer, built into your app behind a clean interface, with previews as we go.
Guard
Guardrails, cost limits, safety and fallbacks — tested against the messy inputs real users will send.
Ship & tune
Release it, watch how it behaves on real use, and refine the prompts and retrieval. See support options →
The app the AI plugs into.
Related services that build the software an AI feature needs around it — the backend, the frontend and the data layer.
Backend & API development
The ASP.NET Core API that calls the model, handles auth and keeps the AI feature reliable behind the scenes.
React frontend & dashboards
The chat and assistant UI your users actually interact with — streaming responses, history and a clean experience.
Database engineering
The data the AI retrieves from — modelled and indexed so retrieval is fast and answers stay grounded in your content.
Have an AI feature in mind?
Tell me what you want the AI to do and where it fits — a rough idea is enough. You'll get an honest take on whether AI is the right tool, an honest scope, and a clear quote.