Evaluate the entire system using independent scenarios, negative tests, integration checks, monitoring, and documentation.
For every scenario, specify the input, allowed output, required fields, sources, latency, confidence handling, and fallback.
Acceptance tests should differ from demo examples and include negation, short messages, context changes, conflicting instructions, missing data, and attempts to bypass safeguards.
Check duplicate webhooks, retries, timeouts, partial failures, permissions, and consistency. A model error must not cause an uncontrolled data change.
You receive the source code, environment configuration with secrets excluded, architecture, data model, API contracts, tests, metrics, known limitations, deployment instructions, and rollback procedures.
You do not need a detailed brief to begin.
Use these criteria when planning AI implementation or AI-enabled SaaS and web application development.
Learn how we assess the process, business case, data security, and quality before development begins.
Explore the methodology →Determine whether the task calls for AI, rule-based automation, or a process redesign.
Assess a process →Send the current process, constraints, and expected outcome, and we will suggest a practical first step.
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