Problem, context and outcome
We identify users, decisions, information, constraints and the change the solution needs to produce.
How we work
From a real problem to an AI solution in production.
We combine business understanding, architecture, software and AI engineering, integration and evolution in one journey.
The journey
Seven steps, one continuous responsibility.
Understanding
Prioritisation
Architecture
Prototype
Integration
Production
Evolution
Architecture and controls
We define components, integrations, sources, permissions, quality criteria and human oversight.
Software and AI working together
We develop, test and iterate with data and situations representative of real use.
Production as a beginning, not an end
We observe, measure, maintain and expand the capability on a controlled foundation.
Hybrid organisation
People and AI building better AI systems.
AI is also part of how we research, develop, test, document and maintain. It does not replace judgement: it expands the capacity available to apply it.
Human capability
- Business understanding
- Design and architecture
- Decisions and validation
- Responsibility for the outcome
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AI capability
- Analysis and research
- Programming and testing
- Prototyping and documentation
- Support for quality and maintenance
Built-in control
The level of autonomy depends on context and risk.
Reliable context
The solution works with the information authorised for each task.
Appropriate access
Each person and system uses only the capabilities they need.
Human intervention
Approval is introduced wherever the decision or risk makes it necessary.
Understandable actions
We record the information needed to review behaviour.
Observable quality
We define how to verify usefulness, reliability and improvement.
Controlled change
Models, knowledge and processes can change without losing the architecture.
Every project starts by understanding what needs to work better.
Tell us about the process and we will assess the starting point.