We built the wildfire safety platform for a major California utility. Now we're bringing that to you.

PG&E filed for bankruptcy after wildfires caused over 100 deaths and $25.5 billion in settlements. California regulators approved $27 billion in wildfire prevention cost recovery across the state's major IOUs. Wildfire-related costs now represent 10-24% of total revenue requirements for California's investor-owned utilities. This isn't a California-only problem anymore; wildfire mitigation plans are spreading to other states, and the regulatory requirements keep expanding.

We built the solution for this exact problem at SDG&E. Their legacy VESTA notification system could only message 5,000 customers at a time. The notification process was a 37-step sequence that lived in one employee's head. Reporting was manual. We built a microservices architecture of five independent applications integrated with eight existing services, reduced the process from 37 steps to 10, and enabled SDG&E to notify 150,000+ customers in a single activation with three clicks. In 2022, SDG&E's closest competitor was fined 417 times the amounts SDG&E faced.

Mid-market utilities need platforms that connect weather data, vegetation intelligence, customer notification, PSPS execution, and regulatory reporting into a coherent workflow. Most are still running disconnected tools and manual processes. This is a problem with a proven solution, deployed at production scale for a major IOU.

The air gap between your operational technology and enterprise IT is largely gone, but most utilities haven't built the bridge properly. SCADA systems use industrial protocols (Modbus, DNP3, and IEC 61850) that were never designed for analytics. Your ERP, CRM, and billing systems hold financial and customer data in completely separate architectures. For a $200M regional utility, this means operational data about grid conditions, equipment health, and field crew activity is invisible to the business systems that drive decisions.

The urgency is coming from two directions at once. Cybersecurity: the China-linked Volt Typhoon group has been confirmed conducting long-term intrusions into U.S. energy infrastructure, and NERC CIP-015-1 now mandates internal network security monitoring across previously exempted systems. And AI readiness: 68% of energy executives cite legacy OT systems as their primary barrier to AI deployment. Energy companies spend 65% of their AI project budgets on data engineering alone, nearly three times the cross-industry average.

The technology solution involves secure DMZ architectures, real-time data ingestion pipelines from SCADA and historian systems, edge computing for latency-sensitive applications, and network segmentation that doesn't break operational workflows. The right approach is building the integration layer, not buying the $15M ADMS platform your utility can't absorb.

NERC CIP compliance is growing in both scope and complexity, and the compliance burden scales the same regardless of utility size. That creates a disproportionate "compliance tax" on mid-market companies. Violations trigger fines up to $1 million per day per violation. The 2025 CIP updates reclassify historically "low-impact" assets to stricter security controls. CIP-015-2, due June 2026, will expand continuous monitoring requirements further. FERC's FY2025 audits found persistent gaps around DER categorization, third-party vendor risks, and cloud services.

Beyond NERC CIP, state PUC reporting is becoming more data-intensive: rate case preparation, integrated resource planning, wildfire mitigation plan filings, and customer service quality metrics all require pulling data from disparate operational systems. For a utility operating across multiple regulatory jurisdictions (common in multi-state energy companies), the complexity multiplies fast. Enterprise solutions like ServiceNow or Industrial Defender can be over-engineered and overpriced for this segment.

The answer is a compliance management platform with automated evidence collection, continuous monitoring tools, and unified documentation systems, right-sized for the $200M utility that doesn't need a $2M compliance platform but can't keep handling audits with spreadsheets and email attachments.

When an acquirer targets a $200M energy services company, usually through a roll-up of smaller acquisitions, they inherit a patchwork of disconnected systems. Each add-on brings a different ERP, CRM, field service tool, billing system, and safety tracking method. The ENTRUST/EN Engineering story illustrates this perfectly: Kohlberg built the platform through 10 strategic add-on acquisitions in utility engineering services before selling to Leidos for $2.4 billion. Integrating 3,100 employees' legacy systems was a defining challenge of the hold period.

Post-acquisition, leadership mandates standardized reporting within 90 days. That requires extracting data from systems that were never designed to talk to each other. The immediate needs are operational visibility dashboards (real-time EBITDA tracking, capacity utilization, and safety and compliance metrics), field services platform consolidation (incompatible dispatch systems, work order formats, and technician certification tracking), and fleet management across what might be 200-2,000+ vehicles. Fleet optimization alone typically yields 10-15% cost reduction through better utilization and preventive maintenance.

The Technical Due Diligence assessment is the starting point. It finds the expensive technology surprises before they become your post-close problem. From there, the work is integration layers and data platforms that make consolidation possible on your timeline.

Traditional load forecasting models built on population growth and weather patterns are breaking down. EVs, heat pumps, data centers, and behind-the-meter solar create demand patterns that are concentrated, volatile, and faster than regulatory or investment processes can adapt. A single large data center consumes as much power as roughly 80,000 homes. The DOE projects 30-42 million plug-in EVs on U.S. roads by 2030, adding an estimated 100-185 TWh to national electricity demand.

For a mid-market utility, the problem isn't "better algorithms." AI-driven forecasting tools can reduce forecast errors by 20-50%. The problem is that the inputs are changing faster than models can be retrained. Over-forecasting means overbuilding and higher customer costs. Under-forecasting threatens reliability. Georgia Power saw expected large load additions decrease by 6 GW between Q2 and Q3 2025 as projects exited its interconnection pipeline, showing just how unpredictable these new loads are.

The technology solution involves AI/ML forecasting platforms that incorporate real-time AMI data, weather APIs, EV charging patterns, DER generation profiles, and economic indicators. But it also requires scenario-based modeling with probability ranges instead of point forecasts, which is a fundamental shift from how most utilities plan. That data infrastructure is the foundation everything else depends on.