Predict, price, and prioritize your risk

Enhance traditional cadence-based vegetation management with the power of predictive data science. Our machine learning models adapt to changing variables, offering you an informed decision-making system tailored to your goals. Understand the impact of every decision in seconds, not years, and implement data-driven strategies to optimize spend, reduce downtime, and increase operational performance.

The results

14% SAIFI improvement

20% vegetation management savings

77% trim target reduction

The E Source advantage

Our AI-powered solution is designed to support the wide range of utility operations for distribution and transmission efforts. We develop a data-driven digital simulation so you can test ideas, run experiments, and perform scenario planning to prioritize vegetation management and accelerate the speed to value


AI-powered predictive intelligence to support evidence-based scenario planning that enhances risk-spend efficiency.


Comprehensive risk profile with configurable levels of detail built by applying advanced data science to more than 400 variables, including satellite imagery, across third-party and utility data.


Insights tailored to organizational context and recommendations are integrated with work management systems, complete with expert guidance from data scientists and consultants with deep utility expertise.


Purpose-built for utilities and designed to be actionable, with recommendations that adapt to changing variables, reducing downtime, increasing vegetation management efficiency, and lowering wasted spend.

Learn from utilities

Learn from experts

E Source GridInform Vegetation Management

A solution to accurately predict vegetation-induced outage risk and optimize mitigation returns.

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Optimizing vegetation management: Why data allows us to make better decisions than ever before

Learn how utilities can lower operations costs and improve reliability by applying predictive data and machine learning to vegetation management. With data science, utilities will also minimize problems from unplanned work and ultimately improve safety and customer satisfaction.

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A Northeastern utility implements a data-driven vegetation management solution

This case study explains how the E%nbsp;Source Data Science team helped a Northeastern utility deploy a risk-based vegetation management solution that delivered a 14% improvement in System Average Interruption Frequency Index (SAIFI).

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Make better vegetation management decisions today