AI Gets Energy Multi-Source Billing Done in Minutes

AI Got Energy Multi-Source Billing Done in Minutes

By automating data extraction and validation across dozens of utility invoice formats, we helped simplify and speed up one of our client’s most time-consuming processes.

Overview

Our client, a leading Energy-as-a-Service provider, was processing thousands of utility invoices every month, each arriving in a different format from dozens of energy companies across the U.S. What started as a manual, time-intensive workflow had become a serious scalability challenge.

To solve it, we designed the AI-powered Utility Invoice Processing Solution (UIPS); a system that automates data extraction and validation while bringing new levels of speed, consistency, and control to the billing operations.

Challenges

1

COMPLEX UTILITY ECOSYSTEM

Invoices arrived from more than fifty different energy companies, each with unique layouts and data structures. Analysts had to interpret each one manually, a process prone to delay and human error.

2

OPERATIONAL BOTTLENECKS

Each invoice required between five and ten minutes of manual work to process and validate. As volumes grew, the existing process could no longer keep pace with business expansion.

3

ACCURACY & DATA CONSISTENCY

Inconsistent data capture and formatting made it difficult to maintain reliable billing accuracy and analytics. A unified, learning-based system was needed to standardize and scale.

What We Did

Built a modern AI architecture

We developed a modular platform using JHipster, Spring Boot, and Java at its core, supported by Spring Cloud microservices for scalability and resilience. The system combines OCR and large language models to interpret, normalize, and structure utility invoice data across multiple formats.

Automated data extraction and mapping

Invoices (PDFs or images) are submitted via API, triggering an automated extraction flow that identifies and maps key data fields such as usage, rates, service periods, and totals into a standardized Excel output for downstream use.

Introduced human-in-the-loop validation

Invoices flagged as “Need Review” are routed to Data Quality Analysts through a validation form. Instead of manual entry, analysts now verify or adjust extracted values within seconds, ensuring both accuracy and efficiency.

Established scalable template management

The first phase supports a shared template, with upcoming phases enabling configurable templates per utility provider, allowing new formats to be added quickly without developer intervention.

Created a foundation for continuous learning

All extraction results are logged for future accuracy benchmarking and model improvement, ensuring the system becomes more precise and adaptive over time.

The Impact

MINIMIZED HEALTH RISKS

Up to 80% Reduction in Manual Effort

Processing time per invoice dropped from up to ten minutes to under one minute, freeing operations teams to focus on higher-value work.

Smooth communication between support tiers

Improved Accuracy and Data Consistency

Automated extraction and normalization reduced human error, creating cleaner, more reliable billing data.

BRAND ELEVATION

Scalable and Future-Ready

The modular, cloud-native architecture allows rapid onboarding of new templates and expansion across regions as operations grow.

REINVENTING THE BUSINESS AND OPERATIONS

Empowered Operations Teams

Analysts now spend their time on oversight and optimization rather than data entry, improving productivity and morale.

IT System Visibility

Laying Groundwork for Advanced AI

The solution establishes a foundation for future enhancements such as anomaly detection, confidence scoring, and integration with enterprise billing systems.

OUR TECH PROFILE

Here’s a look at how we approach modernization and AI, the capabilities we bring to every project, and proof of impact through client success across industries.

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