Intelligence that understands context and drives outcomes.
From data and context to governed outcomes.
Data and contextGoverned outcomes
The challenge
Most AI pilots never reach production because they are designed as experiments rather than governed operational components within real workflows.
Our approach
We design AI systems as operational infrastructure — with data pipelines, model governance, evaluation frameworks and integration points built in from the start.
The outcome
AI capabilities embedded into production workflows, monitored, governed and delivering measurable operational value.
What this covers
Where we help.
- 01AI strategy
- 02Agentic systems
- 03Knowledge graphs
- 04Model evaluation
- 05Responsible AI
Built in · Quality Engineering
How Applied AI is proven
- AI system assurance
- Model evaluation
- Agent evaluation
- RAG evaluation
- Groundedness assessment
- Hallucination testing
Where to start
AI Assurance Sprint
For an AI or GenAI use case that is heading into a regulated process.
- Intended-use and risk assessment
- Evaluation plan and test set — accuracy, groundedness, robustness
- Assurance evidence pack and production monitoring approach
Other capabilities
- 01Technology Strategy
- 02Product Engineering
- 03 · You are hereApplied AI
- 04Connected Systems
- 05Quality Engineering
Bring us a problem worth engineering.
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