AI that runs in production. Not just in the demo.
We start with the business problem, not the model. Our AI practice finds the use cases worth funding, builds generative and agentic AI solutions on your data, integrates them with the systems your teams already use, and puts the security, evaluation and monitoring in place to run them with confidence. One team covers the whole path, from first workshop to a monitored system in production.
The problems this solves.
Pilots that never reach production
A proof of concept impressed in a demo, then stalled on data access, security review or integration work nobody planned for.
Too many ideas, no clear priority
Every team has an AI wish list. Few have a way to judge which use cases are feasible, valuable and safe to start with.
AI disconnected from the business
Assistants that cannot see your documents, systems or workflows give generic answers and get abandoned.
Risk, cost and accountability
Leaders need to know where data goes, how answers are checked, what it costs to run and who approves what an agent does.
AI & Intelligent Solutions services.
Generative AI
Enterprise LLM solutions, knowledge assistants and RAG applications grounded in your own data.
Agentic AI
Multi-agent workflows that plan, act across your systems and pause for human approval where it matters.
AI Automation
Business process automation and intelligent workflows that stay predictable, auditable and cost-controlled.
Enterprise AI Integration
Connect AI to the enterprise applications and data your business already runs on.
Related capabilities
From problem to production.
Discover
Assess readiness, map use cases against value and feasibility, and agree what success looks like before anything is built.
Prove
Build a focused prototype on your real data, with an evaluation set, so decisions rest on evidence rather than impressions.
Engineer
Turn the prototype into production software: integrations, access control, human approval where it matters, and proper testing.
Operate
Monitor quality, cost and usage in production, and keep improving prompts, retrieval and workflows as needs change.
Questions customers ask.
Where should we start with AI?
Usually with a short discovery: we look at your processes, data and constraints, then shortlist a few use cases that are valuable and realistic. Starting with one well-chosen use case beats a broad programme with no clear first win.
Do you build on a specific model provider?
No. We choose models per use case based on quality, cost, latency and where your data is allowed to go, and we design solutions so the model can be changed later without a rebuild.
How do you keep our data safe?
We agree data boundaries up front, apply least-privilege access to the systems an AI solution can reach, and keep sensitive data out of places it should not go. Governance is part of the build, not a review at the end.
How much does an AI project cost?
It depends on scope, data readiness and integration work. We'll propose a phased plan and estimate after discovery, so you can commit one stage at a time.
Talk to our AI Solutions team.
Someone from our Data & AI practice will reply within one business day to talk through the use case.