ENERGY & UTILITIES / Proof of Value
Prove impact before production.
Test an AI workflow against your historical records in a controlled sandbox. Corvana measures output quality, review and rework, and full operating cost so your team can decide whether the workflow is worth deploying.
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Illustrative workflow. The named approver reviews the evidence before any consequential action.
Test the complete workflow.
The gate is in the path — not bolted on after.
Grounded in your own records
The sandbox uses approved records, permissions, templates, and review steps from your workflow. Regulatory response preparation, shown here, is one example; the same method can test planning, asset, and customer-service work.
Reported against your own historical work.
Measure quality and cost together
Report errors, missed evidence, reviewer corrections, and time spent across the whole process. Include model usage, human review, and ongoing support in the operating case, with capacity released and cash savings reported separately.
Three steps from a test to a decision.
Map
Define the cases, source records, acceptance criteria, and baseline before testing. Keep historical final answers out of the agent’s inputs.
Test
Run the workflow in the sandbox and capture its output, exceptions, model costs, and reviewer effort. Include failed attempts and the manual fallback.
Prove
Compare quality and full cost against the baseline. Deliver the findings and assumptions with a recommendation to proceed, revise and retest, or stop.
Prove what the workflow is worth.
Start with one workflow, a representative set of historical cases, and success criteria agreed before the test. Deployment remains your separate decision.
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