AI Engineering Principles
AI is a capability we engineer, not a personality we adopt. Held to an engineering bar, it is genuinely useful. Left ungoverned, it is a liability. We treat it like any other system component: tested, bounded, and observable.
Rules first, models for the rest
Anything that can be expressed as a deterministic rule should be. Rules are predictable, cheap, testable, and explainable. We reserve models for what is genuinely ambiguous — language, unstructured documents, ranking under uncertainty — and let the rules handle the rest. Most reliable "AI systems" are mostly not AI.
Evaluation, or it does not ship
A model's output is a claim, and claims need evidence. We build evaluation into intelligent systems so we can measure whether a change actually helps rather than trusting a good demo. Without evaluation, you are not engineering — you are hoping.
Human authority where it counts
Where a system touches money, credit, risk, legal obligations, or customer communications, a human holds the authority. The system assists, proposes, drafts, and ranks; a person decides. We design for oversight, auditability, explicit authority, escalation paths, and reversibility. We do not ship silent, unaccountable autonomy into consequential decisions.
Model selection and fallback
The right model is the one that meets the requirement at acceptable cost and control — which is sometimes a large hosted model and sometimes a smaller one running on owned hardware. Systems are built so a model can be swapped, and so a failed or low-confidence response falls back gracefully instead of failing the whole workflow.
Data handling
Client data is not sent where it should not go, and it is not used to train models without consent. When control or confidentiality demands it, inference can run on infrastructure the client owns.
Observability
Intelligent systems are logged and monitored like any other production system: what was asked, what came back, how confident, how long, how much. You cannot operate what you cannot see.