Practices

Vitalos AI & Data

We help you find valuable AI opportunities, build data foundations, prototype quickly, and engineer and run production systems responsibly. We use AI where it creates measurable outcomes and can be governed.

Discuss an AI opportunity

Vitalos AI & Data

Problems

Leadership wants measurable AI value but has no safe, prioritised route from experimentation to production.
AI pilots are scattered and few have made it into production.
Data across several systems is too fragmented or low-confidence to support decisions or AI.
Reporting and management information cannot be trusted.
Governance, privacy, security and human oversight of AI use are unclear.

Vitalos AI & Data

Capabilities

AI strategy and transformation
Generative AI
AI agents and agentic workflows
Machine learning
Intelligent automation
Data engineering, data platforms and data quality
Analytics and business intelligence
Enterprise AI integration
AI and data governance
Model and AI operations

Vitalos AI & Data

Approach

From opportunity to production
AI follows one journey: AI Opportunity Assessment, Prototype, Production, then Managed AI. Each stage ends with a decision: worth testing, worth production investment, ready for controlled live use, still delivering value safely.
Data foundations
Useful analytics and AI need data that is connected, cleaned, modelled and governed. That means ownership, lineage, quality, schema contracts, access control, retention and deletion, pipeline monitoring and documented metric definitions.
AI across every practice
AI & Data is both a specialist practice and a capability across the firm. Consulting shapes strategy, Engineering builds AI in, Experience designs AI interfaces and Managed Services monitors and improves live AI.

How we approach AI

No AI for AI's sake: every use case needs an outcome.
Prototype evidence is not automatically production readiness.
Data, security, privacy and human oversight are explicit.
Bounded, measurable workflows before broad autonomy; evaluation criteria before launch.
Cost, reliability and vendor dependence understood early; not tied to one model provider.