AI Agentic Workflow for Utility Rate Case Support via Load Shape Analysis

This workflow eliminates months of manual data slicing and scenario modeling for utility rate cases. By ingesting interval meter data and applying proposed rate structures, it generates detailed impact reports segmented by customer class, geography, and time-of-use. The operational upside comes from accelerating regulatory strategy, strengthening filing arguments with precise evidence, and reducing the labor cost of analysis by 70-90%. Implementation integrates with utility data lakes (e.g., OSIsoft PI), billing systems, and regulatory document management platforms.

The architecture is built on an agentic framework like LangGraph, where specialized agents handle data validation, rate application, and report generation. Critical controls include human review gates for final impact assumptions, exception routing for data gaps, and comprehensive audit trails for regulatory defensibility. Deployment typically involves containerized services in a private cloud, integrating with SAP for financial modeling and ServiceNow for document lifecycle management, ensuring repeatable, governed operations for every rate case cycle.

This workflow eliminates months of manual data slicing and scenario modeling by regulatory analysts. It ingests interval meter data (AMI), applies proposed rate structures, and simulates customer bills to produce detailed impact reports segmented by customer class, geography, and load profile. The operational upside comes from accelerating regulatory engagements, strengthening filing arguments with precise evidence, and reducing the cost of expert consultant labor typically required for this analysis. Implementation integrates with data lakes (e.g., Hadoop, Snowflake), uses pandas/Spark for data processing, and applies optimization libraries to calculate bill impacts under complex tariff rules.

Architecturally, the system is built as a multi-agent orchestration on a framework like LangGraph or CrewAI, with dedicated agents for data validation, segmentation, simulation, and report drafting. It integrates with utility CIS (Customer Information Systems) like SAP for customer attributes and with regulatory document management platforms. Critical controls include automated data quality checks, confidence scoring on segmentation clusters, and a mandatory human-in-the-loop review gate for final reports before submission. The workflow outputs audit-ready visualizations and data tables, directly feeding into testimony preparation and stakeholder briefings.

Phase 1 establishes the data foundation, ingesting high-volume AMI data and proposed rate structures via secure APIs into a cloud data lake. We implement validation agents to flag data gaps and anomalies, ensuring the quality of the 15-minute interval load profiles that form the core of the analysis. This initial investment directly targets the most labor-intensive bottleneck: manual data collection and cleansing, which typically consumes 60-70% of a rate analyst's time pre-filing.

Phases 2 and 3 deploy the simulation and orchestration logic. The core agent applies proposed rates to historical and forecasted load shapes, running thousands of 'what-if' scenarios. Results are automatically synthesized into defensible reports segmented by customer class, geography, and rate impact. The final orchestration layer, built on frameworks like LangGraph, manages the approval workflow, assembling the full regulatory submission package and routing it through legal and executive review gates before automated filing, creating a repeatable, audit-ready process for every rate case cycle.

This system eliminates months of manual data slicing and scenario modeling by automating the ingestion of interval meter data, applying proposed rate structures, and generating detailed impact reports segmented by customer class and tariff. The operational upside comes from accelerating regulatory engagements, improving the strength of filings with precise evidence, and freeing expert analysts from repetitive data wrangling to focus on strategic arguments. Implementation requires integration with utility data lakes (e.g., OSIsoft PI), billing engines, and regulatory document management systems.

The architecture is built on a multi-agent framework like LangGraph, where specialized agents handle data validation, rate application, and report generation. Controls are critical: all modeled scenarios require versioning, and significant financial impacts trigger mandatory human review before document assembly. The system integrates with platforms like ServiceNow for approval gates and generates an immutable audit trail for regulatory scrutiny, ensuring the automation delivers both speed and defensibility.

The core business value is eliminating months of manual data slicing and scenario modeling for rate case filings, directly reducing legal and consulting costs while strengthening regulatory arguments with consistent, data-driven evidence. Operational savings stem from automating the ingestion of AMI data, application of proposed rate structures, and generation of impact reports by customer class. Implementation requires integration with meter data management systems (MDMS), regulatory databases, and reporting platforms, using orchestration frameworks like LangGraph to manage the multi-step analytical pipeline with built-in validation checkpoints.

Controls are enforced at each stage: data validation agents flag anomalies for human review; statistical significance checks prevent misleading conclusions; and a final regulatory approval gate ensures legal sign-off before submission. A phased rollout begins with a single customer class or rate structure, validating the model's outputs against historical manual analyses before scaling. Observability is built in via detailed audit logs of every data transformation, assumption, and model parameter, creating a defensible chain of custody for regulatory scrutiny and potential challenges.