See how the work is structured
Learn how we assess the process, business case, data security, and quality before development begins.
Explore the methodology →Expert guide
How to evaluate an AI project before sign-off
Evaluate the entire system using independent scenarios, negative tests, integration checks, monitoring, and documentation.
Define clear acceptance criteria
For every scenario, specify the input, allowed output, required fields, sources, latency, confidence handling, and fallback.
- Functional result
- Quality threshold
- Performance
- Failure handling
Use an independent test set
Acceptance tests should differ from demo examples and include negation, short messages, context changes, conflicting instructions, missing data, and attempts to bypass safeguards.
- Normal cases
- Boundary cases
- Negative tests
- Adversarial tests
Test integrations and failure modes
Check duplicate webhooks, retries, timeouts, partial failures, permissions, and consistency. A model error must not cause an uncontrolled data change.
- Idempotency
- Recovery
- Access control
- Data consistency
Verify operations and project handover
You receive the source code, environment configuration with secrets excluded, architecture, data model, API contracts, tests, metrics, known limitations, deployment instructions, and rollback procedures.
- Monitoring
- Runbook
- Known risks
- Ownership
Choose your next step
Start at the stage that matches your situation
You do not need a detailed brief to begin.
Assess one business process
Determine whether the task calls for AI, rule-based automation, or a process redesign.
Assess a process →Share a defined task
Send the current process, constraints, and expected outcome, and we will suggest a practical first step.
Discuss your project →