Artificial intelligence,
3 services.

From the strategy question through to the model running in production.

AI consulting

End-to-end involvement, from strategic questions to implemented solutions. You get a pragmatic, results-oriented team rather than a deck.

  • AI strategy: the vision, where AI can realistically be applied, the impact on the business model, and the maturity you are starting from
  • Planning and execution of AI transformation: staging the work, running the program, and prioritizing use cases
  • Implementation: assessment, data collection and preparation, then the model itself
  • MLOps consulting: architecting how models get built, shipped and managed
Explore service

MLOps

Put machine-learning models into production, operate them as demand grows, and improve them over time while keeping ownership of the intellectual property.

  • Your AI team is still forming, or demand exceeds its delivery capacity
  • You expect to use the models for 24 months or more and need to own their intellectual property
  • You need to reduce upfront capital expense by shifting early costs to operating expense
  • Your R&D capacity cannot keep pace with new model development on its own
Explore service

MLaaS

Machine learning as a service. Pre-built models put to work now, for teams that need value before they need ownership.

  • A need to move quickly from proven models rather than build from scratch
  • An AI team still forming, or demand running ahead of delivery capacity
  • A long horizon with budget sitting in operating expense
  • Limited R&D capacity to track new model development
Explore service

AI Readiness
Assessment.

Five dimensions, reviewed together. The output is a picture of gaps, risks and accelerants, not a score.

Request the assessment
  • DataWhat exists, where it lives, whether it can be trusted
  • TechnologySystems in use, how they connect, what they cannot do
  • StrategyWhich business outcomes the work is meant to move
  • People & cultureSkills, ownership, and appetite for changing how work is done
  • Governance & processesDecision rights, risk, and how changes get approved

AI does not only mean generative AI

We pick the approach that fits the problem. Often that is several of these together.

  • Machine learning
  • Data engineering
  • Workflow automation
  • System integrations
  • Analytics
  • Custom software
  • Enterprise search / RAG
  • AI agents

Data
is the other half of the same job.

The foundation the AI work stands on, and valuable on its own.

Data, 3 services