Real-world deployments show 70% peak load reduction, 3x EV recruitment, 98% TOU retention and targeted income-qualified weatherization, all from a single modular platform

LOS ALTOS, Calif.: Bidgely today announced its industry-leading Analytics Workbench (AWB) is empowering utilities to bridge the energy equity gap through hyper-targeted engagement and enhanced enrollment for income-qualified (IQ) programs. By deploying artificial intelligence (AI) to identify households eligible for targeted assistance, Bidgely’s AWB increases utilities' success in delivering personalized financial relief and energy-saving support.

The company’s award-winning IQ solution, which detects eligible IQ customers who slip through traditional demographic filters or fall outside federal eligibility thresholds, is the latest release for its flagship AWB platform. AWB operationalizes Bidgely’s leading behind-the-meter (BTM) intelligence to advance multiple high-impact initiatives -such as Energy Audits, EV Load Management, Time-of-Use (TOU) rates and weatherization programs-all powered by cutting-edge AI built for utilities.

“Utilities no longer have to choose between solving for EV management or income-qualified program adoption; they can do both congruently at scale," said Abhay Gupta, CEO of Bidgely. "AWB provides the industry with a pragmatic entry point for AI. By starting with the specific use cases that matter most today, our partners can right-size their investment, establish a win and then scale to enterprise-wide implementation that drives a much faster path to ROI.”

Prevalent Use Cases & Results:

• Rate Design: A South Central U.S. utility achieved over 70 percent reduction from high-peak-usage customers by optimizing variable pricing programs.
• TOU Enrollment: A major Northeastern utility experienced a 98 percent retention rate by mitigating high bill shock during their Time-of-Day (TOD) rollout.
• EV Detection and Optimization: NV Energy tripled demand response recruitment and achieved a load-shift 2.5 times to 10 times greater than typical events.
• Income-Qualified Weatherization: Avista identified high-impact efficiency opportunities in vulnerable communities, achieving, for example, an individual energy savings of 7,500 kWh and nearly $800 in just three billing cycles.

A Modular Framework for AI Adoption

Bidgely’s AWB offers a modular starting point for utilities to implement AI without over-investing. Unlike traditional all-or-nothing data overhauls, AWB allows utilities to right-size adoption by focusing on high-priority needs-like energy equity-to establish success and prove ROI before scaling the technology to broader enterprise use cases.

A centralized engine for consumption analytics, AWB serves as a single source of truth that aligns marketing, demand-side management (DSM) and load research. Replacing siloed data with integrated appliance-level visibility, utilities can simultaneously solve for DER acceleration, affordability, and integrated grid planning.

This flexibility allows teams to pivot between different grid needs without starting data analysis from scratch every time. By quickly visualizing home energy patterns, utilities can identify which programs-from EV charging to IQ weatherization-fit a household's lifestyle. The result is a faster path to program adoption that is both relevant to the customer and effective for the grid.

To learn more about how utilities are leveraging AWB to enhance enterprise-wide operations, download this collection of customer snapshots.

About Bidgely

Bidgely is the pioneer of AI-powered energy intelligence, transforming raw meter data into high-definition insights for global utilities. Serving over 50 million homes, the company’s UtilityAI™ Platform leverages 19 foundational patents to optimize grid visibility, call center operations, and personalized customer engagement. Recognized by Fast Company as a "Top 10 Most Innovative Applied AI" company, Bidgely integrates precision energy analytics with horizontal AI ecosystems like Microsoft Copilot and AWS to modernize the grid with premises-level accuracy. www.bidgely.com

Fonte: Business Wire