Company
Useful AI needs boundaries, ownership and review.
We design AI features as part of a controlled software system—with explicit inputs, permissions, expected outputs, review points and fallback behavior.
- Purpose
- Data Boundaries
- Access
- Evaluation
- Human Oversight
- Monitoring
Principles
- Use AI for a defined business task, not because a model is available.
- Minimize data exposure and align providers/deployment with project requirements.
- Keep high-impact actions reviewable or approval-controlled.
- Measure quality using representative tasks, not demos alone.
- Log and monitor AI-assisted workflows where appropriate.
- Design fallback behavior for unavailable or low-confidence AI outputs.
- Avoid presenting model output as guaranteed fact.
AI architecture choices
Depending on the project, AI can be implemented through approved external model APIs, enterprise endpoints, retrieval systems, smaller task-specific models or private deployment patterns. The appropriate design depends on data sensitivity, cost, latency, evaluation results and operational support.
FAQ
Questions we hear often
Direct answers. Anything specific to your project is covered during discovery.
Do you train models on customer data?
This is defined per project and provider. We do not make a blanket statement: the data-use terms of the chosen provider and the engagement agreement apply, and they are documented in the project.
Can AI actions require approval?
Yes. Approval gates can be mandatory for selected workflows.
Can AI be disabled if needed?
Features can be architected with controlled fallback paths where the business process requires continuity without AI.