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
Discuss your project →