LLM integration: intelligence, built in.

The problem

Your team answers the same questions, reads the same long documents, and searches the same folders every day. Your customers wait for a human to reply to something an assistant could answer instantly. The models that solve this exist — but wiring them in properly (accuracy, privacy, cost) is real engineering.

What we build

We integrate models such as GPT, Claude and Gemini into your software and workflows: customer-facing chat that actually knows your products, internal copilots that draft replies and reports, smart search across your documents, and extraction that turns PDFs and emails into structured data. We design for accuracy first — grounding answers in your data, adding citations, and keeping a human in the loop where it matters.

What you get

• A working integration in your product, website, or internal tools
• Retrieval over your own documents so answers are grounded, not guessed
• Privacy controls: what data is sent, where it's stored, who can see it
• Cost monitoring and model choice tuned to your budget
• Evaluation on real questions before launch, and after

Example use-cases

• Support assistant — answers product questions from your docs, hands off to a human when unsure.
• Document intake — reads incoming contracts or invoices and fills your system automatically.
• Internal copilot — drafts proposals, summarises meetings, searches company knowledge.

Common questions

Is our data used to train the AI?

No. We use business-tier APIs where your data is not used for training, and we can keep sensitive data on your own infrastructure.

Which model do you use?

Whichever fits the task and budget — we're not tied to one vendor and we'll show you the trade-offs.

How do you stop it making things up?

Grounding answers in your documents, citing sources, restricting scope, and testing on real questions before go-live.

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