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Now onboarding validation pilot partners. Request your pilot →

Solution

Platform

Insights

About

Contact

Book a demo

Use Case

Right-sized testing, scaled to risk.

Computer Software Assurance shifts effort toward critical thinking and away from uniform, document-heavy testing. Eventt AI supports that approach by classifying each requirement by risk and scaling the test evidence to intended use and potential impact. High-risk functions receive rigorous automated execution, while lower-risk steps can be handled through hybrid or manual modes. The result is validation effort concentrated where it matters, with the reasoning recorded at every step.

Use Case

Computer system validation, from requirement to record.

Eventt AI runs the full CSV lifecycle on a single document spine. It ingests your approved requirements, generates risk-based test scripts, executes them against your configured system, and compiles the evidence into a review-ready validation package. Every result is reviewed and e-signed before it enters your validated record. The work happens in a fraction of the manual timeline, with traceability maintained throughout.

The scenario

You are moving from traditional CSV toward a CSA approach, and you want testing effort matched to risk rather than applied evenly across every requirement. Over-testing low-impact features wastes your team's time; under-testing high-impact ones exposes you at audit. The real challenge is defending how much testing is enough, and showing the risk rationale behind each decision. Eventt AI makes that rationale explicit and traceable.

Step 1

Classify each requirement by risk

Lumen assigns a risk classification to every extracted requirement, which you review and adjust against your own risk framework. The classification drives how much testing each requirement receives.

Step 2

Match the test approach to the risk

Scripts support automated, hybrid, and manual execution modes per step, so high-risk functions get full automated coverage while low-risk checks stay lightweight. You decide the depth of evidence each requirement warrants before execution begins.

Step 3

Execute and scale the evidence

The AI agent executes the scripts and captures evidence proportionate to each step's intended use and impact. Ambiguous results are escalated for review rather than passed, so critical functions are never under-evidenced.

Step 4

Record the rationale

Every risk classification, execution mode, and evidence decision is captured on the shared audit trail with its citation. Your reviewers, and any auditor, can trace why each function was tested the way it was.

( Validation effort concentrated on high-risk functions, with a documented risk rationale behind every testing decision. )

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