AI features that ship inside the software you already have

AI work we've already built

Everything below is a category we've delivered in production — not a capability we're hoping to learn on your budget.

Built into the platforms we already engineer

AI features fail most often at the integration layer — the plumbing between the model and your real systems. That's exactly the layer we've spent years in:

• Drupal — AI-assisted content workflows, intelligent search, and document chat built on Drupal's maturing AI ecosystem.
• Laravel & Node.js — AI features inside your portals, dashboards and bespoke applications: the systems where your data already lives.
• React & React Native — assistant and chat interfaces, streaming responses, AI-powered UI in web and mobile apps.
• WordPress — content assistance, site search and support chat for the platform your marketing team runs.
• Standalone — and when it shouldn't live inside any of them, we build it as its own service with an API into everything else.

Your data, handled like it matters

• Model choice on merit — commercial APIs, EU/UK-hosted endpoints, or self-hosted open models where data residency demands it. We recommend per project, not per partnership deal.
• UK GDPR by design — data minimisation, clear processing agreements, and configurations where your data is not used to train anyone's models.
• Access controls carried through — an AI assistant must respect the same permissions your systems already enforce. Document chat that leaks the HR folder is a lawsuit, not a feature.
• Costs made visible — AI features have running costs. We instrument usage from day one so your monthly bill is a dashboard, not a surprise.

Prototype first, promises second

1. Use-case scoping

   Where AI genuinely saves hours or wins revenue in your workflow — ranked by payback, not by demo appeal.

2. Fixed-price pilot

   A working prototype on your real data in weeks — so decisions get made on evidence, not vendor slides.

3. Evaluate honestly

   Accuracy measured against real cases, failure modes documented, running costs projected. Sometimes the pilot's answer is "don't scale this" — that's a cheap lesson, and we'll say it.

4. Ship into production

   Integration with your systems, permissions, monitoring, fallbacks and escalation paths — the engineering that separates a demo from a product.

5. Monitor & improve

   Models change, prompts drift, usage evolves. AI features go onto a care plan like everything else we build.

What an AI integration actually costs — and why

Pilots run £5,000–£15,000 fixed-price, and where a project lands in that range comes down to three things. First, data readiness: clean, digital, well-organised source material sits at the bottom of the range; scanned archives, inconsistent formats and five systems that disagree with each other push toward the top, because data wrangling is real engineering time. Second, integration depth: a standalone assistant your team visits is simpler than one embedded inside your CRM with permissions mirrored and actions wired in. Third, the accuracy bar: a research aid that speeds a human up can tolerate occasional misses; anything customer-facing needs guardrails, evaluation sets and fallback paths, and that engineering is where careful AI work differs from a weekend demo.

Running costs are the question most agencies dodge, so we'll answer it plainly: for typical business workloads — a document assistant for a mid-size team, support triage on a few hundred tickets a week — model usage generally lands in the tens of pounds per month, not thousands. We project your specific number during the pilot from measured usage, and design with cost ceilings so an unexpectedly popular feature can't produce an unexpectedly horrible invoice. Where volumes are large, self-hosted open-weight models often beat API pricing — another decision the pilot's numbers make for you.

And the question underneath all of it: build now or wait? Our honest take — waiting for the technology to settle means waiting forever, but betting the company on it is equally wrong. The pattern that works is picking one workflow where the payback is obvious, proving it with a measured pilot, and letting the evidence set the pace from there.

Common questions