AI Integration Services & LLM Integration
AI wired into your product.
An LLM API call is one line of code. AI integration is everything around it: the right model, prompts that survive real inputs, structured outputs, retrieval over your data, retries, evals, and a cost curve you can live with. We build that part.
What we deliver
The part after the API key.
/ 01
LLM API integration
Claude API, OpenAI API, or Gemini — chosen per task and wired into your product with structured outputs, function calling, streaming, and fallbacks.
/ 02
RAG & knowledge search
Retrieval-augmented generation over your documents and data, so answers come from your knowledge base with sources — not from the model's imagination.
/ 03
AI features in your product
Summarisation, extraction, classification, drafting, search — shipped as product features your users touch, not as a chat box bolted on the side.
/ 04
Reliability & cost engineering
Eval suites, prompt versioning, caching, batching, and per-feature cost tracking — the difference between a demo and something you can run at scale.
Tools we reach for
- Anthropic Claude
- OpenAI
- Gemini
- Vercel AI SDK
- MCP
- pgvector
How we work
Assess
Where AI actually belongs in your product, which model fits each task, and what the feature must cost at your scale.
Design
Prompts, data flow, and failure modes on paper first — what happens when the model is wrong, slow, or down.
Integrate
Build with structured outputs and evals from week one, against your real data — not a happy-path prototype.
Harden
Monitoring, cost tracking, and model upgrades handled deliberately. Providers ship new models monthly; your feature shouldn't wobble.
FAQ
Questions we get a lot.
- What do AI integration services actually include?
- Everything between an API key and a dependable feature: model selection, prompt design, structured outputs, retrieval over your data, retries and fallbacks, eval suites, monitoring, and cost controls. The API call is the easy 5% — we build the other 95%.
- Which LLM API should we use — Claude, GPT, or Gemini?
- It depends on the task: they differ in reasoning quality, speed, context handling, and price, and the honest answer changes as models ship. We pick per task, benchmark on your real data, and keep the integration portable so you're not locked in.
- How much does LLM integration cost?
- Two costs matter: the build and the tokens. A first feature is typically a few weeks of work, and we model the running cost at your usage before writing code — so no surprise invoice from your AI provider in month two.
- Is this GDPR-compliant? Where does our data go?
- We choose providers and regions to fit your privacy requirements — EU processing options, data minimisation, no training on your data — and document exactly what leaves your systems before anything ships.
- What about hallucinations and reliability?
- Constrain and verify: retrieval so answers cite your data, structured outputs so responses are checkable, eval suites that catch regressions, and honest fallback behaviour when confidence is low. Reliability is designed in, not hoped for.
Related services
AI automation & workflows
The same wiring, pointed inward: back-office workflow automation that quietly removes hours from a week.
AI agent development
When a feature should act, not just answer — custom agents with scoped permissions and audit trails.
AI consulting & strategy
Not sure what to integrate first? An audit and a roadmap that's honest about cost and feasibility.
Working together
Have a feature that needs a model behind it?
Tell us what your product should do — we'll tell you which model fits, what it will cost to run, and how fast it can ship.