Build · 4 min read ·
Appropriate AI by default, not AI at any cost
How we decide when a feature should use a model, when deterministic software is the better choice, and what every AI build must pass before release.
Not every problem needs a model. Some of the most valuable “AI projects” we could ship would be worse than a well-designed form, a rules engine or a search index.
The question we ask first
Would deterministic software be more reliable, more interpretable or more economical here? If the answer is yes, we build that. AI earns its place when the task genuinely involves language, ambiguity or judgement that rules cannot capture well.
Where AI tends to earn its place
- Natural-language search and conversational interfaces over your own knowledge.
- Document extraction, classification and summarisation.
- Decision support where a human remains accountable for the decision.
- Workflow automation that combines AI steps with deterministic orchestration.
The gates every AI build passes
Whatever the use case, a production AI feature passes the same gates:
- Data classification and usage rights.
- Model and provider terms.
- Privacy and residency.
- Access control.
- Security testing.
- Model quality thresholds.
- Deployment approval.
- Human oversight.
- Incident and rollback plans.
These scale with how critical the system is. They are not optional because the project uses AI.
Measure it in production
Evaluation does not end at launch. We agree evaluation datasets, regression tests and online monitoring up front, so quality is part of the acceptance criteria and drift is visible after release.