Appiah AI
Method
A disciplined route from ambiguous opportunity to live system.
The method is intentionally practical. It gives executives clarity, gives technical teams constraints, and gives operational teams a system they can use without heroic effort.
Frame the decision
We identify the recurring decision, action, or judgment the system must improve. This prevents the project from becoming a vague chatbot or a dashboard nobody owns.
Map the operating reality
We document data sources, user roles, current workarounds, security constraints, approvals, exceptions, and failure modes before proposing architecture.
Design the smallest reliable system
We choose the right mix of retrieval, rules, model calls, user interface, integrations, and human review for the job at hand.
Evaluate before scale
We create test cases from real work, score outputs against business criteria, and define thresholds for release, rollback, and improvement.
Launch with ownership
We prepare adoption materials, monitoring routines, support paths, and management reporting so the system does not quietly decay after launch.
Production readiness
The checklist is part of the product.
- Clear business owner and operational owner.
- Known data lineage, permissions, and refresh cadence.
- Evaluation suite covering common, edge, and risky cases.
- Human override path and escalation thresholds.
- Security, privacy, and audit expectations documented.
- Monitoring for quality drift, latency, usage, and failure modes.