Darwin

Start with a representative asset, field, property, project, or portfolio where you can compare the AI output with reliable source data. Confirm that the product actually covers the geography and data type you intend to use.

Use Darwin to produce a first set of insights, options, detections, or forecasts for its core workflow: AI energy platform for optimizing industrial operations, production, maintenance, and emissions workflows. Review the underlying inputs and compare important outputs with field observations, drawings, meters, market records, agronomic evidence, or other authoritative domain data.

Track exceptions rather than only successful examples. Record false positives, missed issues, forecast error, model drift, and cases where staff override the recommendation. For automated equipment or building controls, use staged permissions and make changes reversible. For property and investment decisions, independently verify title, condition, zoning, financial, and market assumptions.

Before scaling, confirm integrations, access controls, retention, sensor or imagery requirements, data licensing, geographic coverage, auditability, and operational ownership. Use the provider's current pricing and contract terms rather than assuming a plan remains unchanged.