A single asset view to

sync all systems

Test live demo

DOWNLOAD OUR 2024 AI FOR UTILITIES PRIMER

Sent via email! Please check your junk folder as well

Oops! Something went wrong

Built AI first specifically for Utilities

50x

Faster asset report generation

18%

Longer asset lifecycles

1,200

Annual hours saved scoring

Single asset view on autopilot with AI

Data requests in seconds, not weeks

From six weeks to six seconds, Senpilot instantly returns requests

The weekend promise

Setup cloud buckets on Friday and have a Singe Asset View by Monday

Cleaner data

Automated data extraction, loading, and transformation

Effortlessly plug into current systems

Asset management made easy

Superhuman asset registry

Make better asset decisions faster and easier. Sync with multiple systems and leverage AI to keep your asset registry clean. All asset data together in one place.

Turbocharged reporting

Generate reports 50x faster. Leverage trained AI to write detailed reports, with substantive Utility specific calculations and insightful charts.

Ambient AI

Leap forward with ambient AI. Surveillance mode is integrated with ADMS and SCADA systems, flagging complex anomalies and patterns.

See all features

Latest AI x Utilities

July 16, 2024

Predictive maintenance: future-proofing asset management in utilities

AI based predictive maintenance varies widely in benefits with costs decreasing only 15% while downtime can improve up to 60%. The accuracy of a predictive model is correlated to the amount of failure training data available.

May 7, 2024

Machine learning in the wear-out period of a utility asset

In the US/Canada, 15% of assets are running past expected life. The average Utility can unlock up to $3B in value by leveraging a machine learning to manage wear-out period assets.

April 19, 2024

Managing the 30% of US distribution assets at end of life

Approximately 30% of Utility distribution assets are in the end of life period. Technology, specifically AI and ML, can now be leveraged to run assets longer than before, through the wear-out period.