We make your data AI-ready — the data architecture, knowledge graphs and semantic foundations that let AI reason over your business instead of guessing at it.
A roadmap from where your data is today to where your AI needs it to be — blueprints, business cases and funding your board will back.
AI-native data platforms with AI data governance and semantic models that give your data meaning, trust, and value.
Knowledge graphs provide your business facts to your AI. Your graph can thrive with diverse data from across your business.
AI grounded in a knowledge graph answers with your facts — your customers, assets, policies and how they relate — not plausible fictions.
Fewer hallucinations. Answers with provenance you can audit. Meaningful data shared consistently between people, systems and models.
We've built semantic data foundations since before they were the answer to the AI question. Today, this is how you make AI work.
Data architecture is the clever design about where your data lives, what shape it's in, and how it moves from source to user — decisions that, done well, let you move faster, defend your numbers, cut the cost of duplicated work, and put AI on a foundation it can actually trust.
It's why one company can launch a new product, report, or AI feature in weeks, while a competitor with tangled, siloed data takes a year for the same thing.
Every new initiative first has to find, clean, and connect the relevant data — bad architecture makes that a project in itself, every time.
It's what lets you answer "can we defend this number to a regulator" or "where did this figure come from" — with a straight answer instead of a scramble.
For a business under regulatory scrutiny, that traceability is the difference between a clean audit and a costly one, and increasingly it's what decides whether an AI system's outputs are usable in decisions that matter.
Without it, every team ends up solving the same data problem separately — five versions of "customer" that don't agree, duplicated integration work, analysts spending most of their time hunting for and reconciling data instead of using it.
That's a real, ongoing cost that shows up as slow, expensive projects rather than a line item anyone points to.
It sets the ceiling on what AI can do for the business. An AI initiative can't outperform the data it's built on — if the data's fragmented or untrustworthy, the AI will be too, no matter how good the model is.
Executives who get this fund the data foundation before the AI pilot; the ones who don't end up with a flashy demo that can't be trusted with a real decision.
AWS and Azure platforms that ingest, transform and serve data reliably at scale.
Data governance, security, master data and metadata design — trust built in.
Ontologies, taxonomies and conceptual-to-physical data models that capture meaning.
DCAT-based indexes so every data asset is findable — by people and by AI.
Ingestion, storage, transformation and visualisation that unlock spatial insight.
Self-service business intelligence that finds new insight in connected data.
Audit your current data, systems and AI ambitions to find where the biggest gaps and opportunities lie.
A rich, data-driven future for your business — made concrete in a strategy your executives own.
Roadmaps defining the change for people, process and technology — with the steps to get there.
Semantic models, ontologies and governance that give your data meaning, trust and value.
Modern cloud platforms, integrations and knowledge graphs stood up alongside your team.
Platforms, graphs and analytics shipped into production — with the capability to run and grow them.
Start with a conversation about where your data is today — and where your AI ambitions need it to be.