AI Integration Services

Artificial Intelligence

AI that fits the system you already run

Most businesses do not need a bespoke model. They need existing AI capability wired properly into the software, data and processes they already have — and they need to know what it costs and where the data goes.

That is the work we do. We start from a task that is genuinely slow or expensive today, prove the AI handles it acceptably, and only then build it into your workflow. If a simpler piece of software would do the job better, we will say so.

Good first projects

  • Answering repetitive customer questions
  • Reading invoices or forms into a system
  • Summarising long documents or calls
  • Triaging and routing incoming requests
  • Searching an internal knowledge base
What We Build

Six areas of AI integration

Chatbots & Virtual Assistants

Support and sales assistants that answer from your own documentation and data rather than making things up — deployed to your website, WhatsApp or internal tools, with escalation to a human when the assistant is out of its depth.

Document & Data Extraction

Turning invoices, contracts, forms and scanned paperwork into structured data your systems can use. Useful anywhere a person is currently retyping information from one screen into another.

Workflow Automation

Drafting, summarising, classifying and routing work automatically — triage of incoming email and tickets, first-draft responses, and report generation, with a human approving anything that matters before it goes out.

Search Over Your Own Content

Retrieval-augmented search across your knowledge base, policies and archives, so staff get a direct answer with a citation instead of ten links to read through.

Model & API Integration

Connecting AI models to the systems you already run — your CRM, ERP, database or in-house application — including provider selection, prompt design, evaluation and cost control.

Governance, Privacy & Cost Control

Deciding what data may leave your network, keeping audit logs of AI decisions, testing for accuracy before launch, and putting limits in place so token spend does not surprise you.

How We Work

Proof first, then production

AI projects fail when they go straight to a full build on the assumption the model will cope. We check that assumption early, while it is still cheap.

  • Pick one task with a measurable cost today
  • Build a small prototype on your real data
  • Measure accuracy against examples you agree up front
  • Integrate into your systems with a human in the loop
  • Monitor quality, cost and drift once it is live

What we will tell you honestly

AI is very good at some tasks and unreliable at others. Anything where a confident wrong answer is expensive needs a human check, and we will design that in rather than pretend the problem does not exist.

We will also be clear about running costs before you commit. Model usage is an ongoing bill, not a one-off build fee, and it should be part of the business case from the start.

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