Which AI model do you use?

Whichever fits: Claude and GPT for quality, open models when cost or privacy matters. I design so the model can be swapped without a rewrite.

Will it make things up?

RAG grounds answers in your real content, and I add citations and guardrails. It is built to say "I do not know" rather than invent.

How do you control AI costs?

Caching, right-sized models, token budgets, and rate limits. You get cost visibility, not a surprise bill.

Add AI features people actually use.

I integrate LLMs like Claude and GPT into your product: chat, search, summarisation, and smart assistants, built to be fast, safe, and genuinely useful.

Start a projectFrom $3,000

What AI Integration involves

AI integration means adding intelligence to the product you already have: a support assistant that answers from your docs, semantic search that understands intent, auto-summaries, drafting, tagging, or extraction. Done well, it feels like a natural part of the app, not a bolted-on gimmick.

I connect the right model (Claude, GPT, or an open model) to your data with retrieval-augmented generation (RAG), streaming responses, guardrails, and fallbacks, so it is accurate, fast, and does not fall over when traffic spikes or the model changes.

What you actually get

RAG over your data

Answers grounded in your content with vector search, so the AI is accurate instead of confidently wrong.

Streaming and fast

Token streaming and edge deployment so responses feel instant, not laggy.

Safe by design

Prompt hardening, rate limits, cost caps, and human-in-the-loop where it matters.

A good fit if

SaaS teams adding an AI assistant, search, or copilot to an existing product
Businesses that want AI answering from their own docs and data
Founders who tried a quick AI demo and now need it production-grade

What is included

Model selection and prompt design for your use case
RAG pipeline: embeddings, vector store, retrieval
Streaming chat or assistant UI wired into your app
Guardrails: rate limiting, cost controls, content safety
Evaluation and monitoring so quality does not drift
Clear docs so your team can extend it

How it runs

A calm, predictable process

No surprises, no black box. You see a live URL from week one.

01
Scope
We pick one high-value use case and define what "good" looks like.
02
Prototype
A working slice against real data in days, so you can feel it early.
03
Harden
Guardrails, evals, and cost controls before it meets users.
04
Ship
Deployed on fast infra, monitored, and handed over.

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