AI integration
Models in production.
Under the same controls as everything else.
Most AI pilots never leave the demo. We put models into the systems your teams already use, and treat every AI feature like the rest of your software: owned, tested, reviewed for security and recorded change by change.
What we build
AI that does real work inside your systems.
Retrieval over your documents
Answers grounded in your own policies, contracts and tickets, with citations back to the source and access that follows your existing permissions.
Extraction & classification
Invoices, forms, emails and claims read and routed automatically, with confidence thresholds that send anything uncertain to a person.
Drafting & summarisation
Replies, reports and case summaries drafted inside the tools people already use, ready for review rather than copy-and-paste.
Agents with scoped tools
Agents that act only through narrow, audited tools, with permissions set per action and anything consequential held for sign-off.
AI inside existing systems
Models connected to your CRM, ERP, ticketing and data warehouse, so AI works where the data and the people already are.
Cost & model routing
The right model for each task, with caching, usage budgets and reporting, so spend stays predictable as usage grows.
How it runs
From a scoped workflow to a monitored feature.
Scope
Pick the workflow, the data it may touch and what “good” looks like, in writing.
Evaluate
Build an evaluation set from real cases and compare models before anything is built.
Build
Integrate through our harness: isolated agents, layered checks, risk-based sign-off.
Operate
Monitor quality, cost and drift in production, with every prompt and output logged.
Controls
What every AI feature ships with.
A model is one component in a system. What makes it safe to run is everything around it: what it can see, what it can do, how it's checked and who signs off.
- Model-agnostic: Claude, GPT, Gemini or open-weight models, within your approved-vendor list.
- Runs on accounts your organisation owns, with data-retention terms agreed before anything is sent.
- Evaluated before launch and monitored after, with prompts and outputs logged.
- Tested for prompt injection and data leakage as part of security review.
- High-risk outputs go to a person before they take effect.
Questions
What technical teams ask about AI integration.
Whichever fits the task and your vendor policy: Claude, GPT, Gemini or open-weight models you host yourself. We compare them on your own evaluation set before choosing, and keep the integration portable so you can switch later.
Only to providers on your approved list, under business terms that don't allow training on your data, through accounts your organisation owns. We document what each feature sends, where, and how long it's kept.
Yes. Most of our AI work goes into existing CRMs, ERPs, ticketing systems and internal tools, rather than replacing them.
Every feature has an evaluation set agreed with your team before launch, and quality, cost and drift are tracked in production. If a feature stops meeting the bar, it's flagged and can be switched off in one action.
Put AI into production, properly.
Tell us the workflow you'd start with. We'll scope it and come back with a fixed quote.