Service

AI Integration for Apps

I integrate AI into products in ways that actually ship — LLM features, retrieval-augmented generation, and automation embedded into real mobile and web apps, not demos.

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What I add

LLM-powered features (chat, summarisation, extraction), RAG pipelines that ground answers in your own data, and automation that removes manual steps — wired into your app with the reliability and cost control production demands.

Grounded, not gimmicky

AI features fail when they hallucinate or cost too much. I design retrieval, guardrails, and caching so the AI is accurate, fast, and affordable at scale — with a fallback path when the model is uncertain.

Background

I hold Google AI Essentials and IBM's Exploratory Data Analysis for Machine Learning, and I studied Artificial Intelligence at university — so the AI work rests on real fundamentals.

FAQ

Which AI models do you work with?

I default to the latest and most capable models for the task, and design the integration so you can swap providers without a rewrite.

Can you add AI to my existing app?

Yes — AI features can be layered onto an existing Flutter or web app, from a chat assistant to a RAG-powered search over your content.

How do you keep AI costs under control?

With caching, retrieval to keep prompts small, and model selection tuned per task — so quality stays high without a runaway bill.