Health Pro

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A line graph of an asset's age with a benchmark vertical line

Risk scoring on autopilot

Health and risk scoring

Use best in class health scores, or bring your own. Build with GIS fields, as well as other systems, to load current health score formulas. Scores are kept live updated - no wasted time with excels and manual calculations.

An illustrative UI formula builder with drag and drop fields of GIS dimensions
An illustrative matrix box view of risk and criticality of asset groups for a Utility

Generate scoring documents

Instantly generate scoring documents that explain scoring, variables used, ranges of variables, updates over time, and more. Useful in all regulatory files as well as internal reviews.

Pre-analyze risk scoring updates

Copilot will pre-analyze the impact of formula changes before they are published and used

A table depicting math of variance done on risk health and criticality scores of various Utility assets
An illustrative bar and line chart showing a specific Utility asset health

Future ready machine learning

Use ML to identify improvement opportunities to scoring formulas, while creating transparent context for regulators

Uncover capital needs with benchmarks

Multiple benchmarks

Select from an array of benchmarking sets or bring your own benchmarks.

Better comparisons

Senpilot DIG cleans and categories assets to ensure apples-to-apples comparisons across datasets.

Best-practice reports

Leverage regulator endorsed benchmarking sets such as Kinectrics.

A dashboard depicting a table of icons and asset groups with their benchmark
A women chatting on a mobile phoneAn illustrative bar and line chart showing a specific Utility asset health

Save millions on benchmarking

Use the cleanest data to live benchmark any assets at a time. Get the insights you need to make better asset decisions. Extend life by up to 30% with improved forecasting.

Best practice benchmarks

95%

Savings vs consultants

40x

More data vs average

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Latest research

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August 7, 2025

Why buying an AI solution is the smart choice for utilities

Utilities should buy AI solutions rather than build them in-house to meet growing demands for reliability, efficiency, and cleaner energy. Building AI systems internally requires significant time, resources, and specialized talent, while purchasing external solutions offers faster implementation (weeks instead of years), access to cutting-edge capabilities at a fraction of the cost, and industry-specific expertise from vendors who understand utility challenges. Bought solutions also provide greater scalability and flexibility through modular, cloud-based platforms that can adapt as utility needs evolve, while offering proven security measures and regulatory compliance that would be costly and challenging to develop internally. Partnering with experienced AI vendors delivers faster, more cost-effective, and higher-quality results than internal development, allowing utilities to focus on their core mission of providing reliable and efficient services to customers.

Pranav Yardi

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July 11, 2025

Foundational AI models: A practical guide for Utilities

AI continues to revolutionize the business landscape, and for utilities, effectively implementing foundation models is crucial for success. These powerful neural networks, initially trained on vast datasets, offer immense potential when tailored to an electric utility's unique operational data and challenges. This customization can be achieved through various methods: parameter-efficient tuning for quick adaptation to specific jargon, fine-tuning for mission-critical applications requiring high precision, and reinforcement learning to align AI outputs with safety and customer service protocols through continuous human feedback. Utilities can adopt AI through practical paths, such as Retrieval-Augmented Generation (RAG), for secure, context-specific answers from internal documents. This approach enables building custom solutions with pre-built tools for specialized needs, as well as comprehensive enterprise integration for real-time anomaly detection and optimized crew dispatch. Strategic AI implementation involves identifying specific pain points, selecting the appropriate customization approach, prioritizing security and privacy (including vendor certifications such as SOC 2 Type II and ISO 27001), and initiating small, manageable pilot projects that demonstrate immediate value. The goal is to achieve the right balance for a utility's objectives, ultimately leading to new levels of operational efficiency

Mike Hejmej

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June 18, 2025

Evening the playing field: AI access for all small utilities

Artificial intelligence (AI) is no longer exclusive to large utilities, despite their multi-million dollar investments. Smaller utilities, facing similar operational challenges, can now access affordable, scalable AI tools through shared AI infrastructure and services. These solutions enable them to improve predictive maintenance, optimize grid management, and enhance customer service at a fraction of the cost. By embracing partnerships with AI providers, smaller utilities can leverage these advancements to remain competitive, improve service quality, and ensure reliable energy for all communities.

Alex Peats-Bond

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