Turning An Electronic Invoicing System Into A Smart, AI-Driven Workflow

The Opportunity

As energy providers shift toward outcome-based and service-oriented models, the opportunity was to turn energy optimization into a scalable, repeatable service.

Rather than delivering one-off implementations, the goal was to create a platform that could support multiple customers while maintaining consistent performance and operational efficiency.

This required a secure foundation capable of ingesting real-time data from connected devices, applying AI-driven analytics, and supporting continuous optimization as a managed offering.

By designing and engineering this platform, we enabled the launch of an innovative Energy-as-a-Service model built for scale, reliability, and long-term differentiation.

The Approach

The engagement focused on system-level architecture, not incremental tooling.

The objective was to build a unified energy management platform that:

• Collects high-quality data from distributed IoT devices
• Applies intelligence centrally to optimize consumption patterns
• Operates autonomously while preserving operator oversight
• Scales securely across thousands of locations

AI was introduced as an augmentation layer, enhancing decision-making and automation while keeping policies, controls, and accountability firmly in human hands.

What We Did

We architected the energy management platform as an integrated system spanning custom hardware, data infrastructure, AI intelligence, and operations.

• Custom IoT device engineering
  Engineered and deployed 13+ proprietary IoT devices to monitor and control energy usage across locations. Devices were purpose-built for HVAC control, energy metering, lighting, and site-level monitoring, and were fully tested, certified, and productionized to meet FCC, UL, and ETL standards.
• End-to-end device lifecycle ownership
  Delivered the full hardware lifecycle, from device design and lab testing to manufacturing, fulfillment, and after-market support, ensuring reliability, security, and consistency at scale.
• Centralized energy data ingestion and normalization
  Collected high-frequency energy and operational signals from deployed devices into a unified platform for real-time visibility and analysis.
• AI-driven optimization and forecasting
  Applied machine learning models to detect inefficiencies, forecast consumption patterns, and recommend or execute optimization actions across sites.
• Automated scheduling and load management
  Enabled dynamic control of energy usage based on demand, pricing, and operational constraints.
• Anomaly detection and early warning
  Identified abnormal consumption patterns and potential equipment issues early, reducing waste and operational risk.
• Security and access controls
  Embedded security controls across devices, data pipelines, and control layers to ensure safe and compliant operation at scale.

Each capability was introduced incrementally, allowing the platform to evolve without disrupting ongoing energy operations.

AI in Operation

Once deployed, the AI operates continuously within the energy management platform.

Real-time data from IoT devices is analyzed to identify patterns, predict demand, and evaluate optimization opportunities. Where confidence is high, the system executes adjustments automatically. When anomalies or edge cases arise, they are surfaced to operators with full context for validation and intervention.

This runtime model enables autonomous optimization at scale while maintaining transparency, control, and human oversight.

The Impact

The AI-enabled energy management platform is deployed across over 10,000 locations, delivering measurable operational, financial, and sustainability outcomes.

Key impacts included:

• Energy cost reduction, with customers lowering energy usage by up to 30%, significantly reducing operating expenses
• Sustainability gains, with average annual carbon savings of 19% across customer environments
• Defensible innovation, with multiple platform technologies now patented, reinforcing long-term differentiation

From a business perspective, the platform enabled rapid growth of the Energy-as-a-Service model. The organization is on track to nearly double annual recurring revenue, approaching $200 million, supported by partnerships with major enterprise brands.