Autonomous Workflow for Cross-Referencing Customer Calls with SCADA & GIS Data

This workflow automates the critical bottleneck of manually correlating incoming customer distress calls with operational telemetry. By implementing an agentic orchestrator that ingests real-time SCADA fault alerts and cross-references them with GIS feeder connectivity models, the system can proactively generate a list of impacted customer premises. The operational upside is a 40-60% reduction in customer-reported incident backlog within the first minute of a fault, allowing field crews to be dispatched based on predictive intelligence rather than reactive call volume.

Implementation requires building a low-latency data unification layer between the OMS, CIS, and GIS, often using a graph database to model feeder-customer relationships. The orchestrator, built with frameworks like LangGraph, executes parallel queries, applies business logic for confidence scoring, and routes outputs. Critical controls include a human review gate for high-impact or low-confidence predictions, exception handling for data quality issues, and comprehensive observability logging for all automated decisions to ensure auditability and continuous tuning.

This workflow automates the critical bottleneck of manually correlating incoming outage reports with live grid telemetry. By proactively identifying affected customers using SCADA fault indicators and GIS connectivity models, utilities reduce call-center surge, cut incident validation time from minutes to seconds, and improve operational awareness. The savings come from deflected calls, faster crew dispatch, and more accurate public communication, directly impacting SAIDI metrics and customer satisfaction during service incidents.

Implementation requires a central orchestration engine, built with frameworks like LangGraph, that ingests real-time streams from OMS, SCADA, and MDMS. It executes match logic against the GIS network model to map faults to downstream meters. Critical controls include confidence scoring for matches, exception routing for edge cases, and audit logging for all automated decisions. The architecture must integrate with legacy utility systems, often requiring API simulation layers, and include rollback procedures for incorrect automated actions to maintain operational safety.

This workflow automates the critical bottleneck of correlating real-time SCADA fault indicators with GIS feeder models to predict affected customer populations before they call. The operational upside is a 40-60% reduction in customer-reported incident backlog, directly lowering call-center surge and enabling faster, more targeted crew dispatch. Savings stem from reduced manual data fusion labor and the ability to initiate proactive customer communications, which improves operational awareness and public perception during service incidents.

Implementation begins with a pilot on a single, well-instrumented feeder circuit. Phase 1 builds the core data pipeline from SCADA/ADMS and GIS into a unified query layer, deploying a single orchestrator agent for validation. Phase 2 adds the customer mapping logic and a simple notification channel (e.g., SMS). Phase 3 integrates with the OMS for automated work order creation and scales to additional circuits. Required controls include confidence scoring for predictions, an exception routing queue for low-confidence matches, and full audit trails for all automated actions tied to specific grid events.

Deploying an autonomous predictive triage workflow requires a governance model that balances automation speed with operational safety. The architecture must embed controls at each decision point: data ingestion from SCADA DNP3 and GIS geodatabases requires validation; the AI agent's match logic between outage polygons and customer premises needs confidence scoring; and all proactive notifications must pass through a configurable approval gate before dispatch. This ensures the system acts on high-fidelity signals only, preventing misinformation and unnecessary alarm during volatile grid conditions.

A phased rollout mitigates risk and proves value incrementally. Phase 1 targets a single distribution feeder, integrating SCADA alerts with the GIS model to generate internal dashboards only. Phase 2 introduces the AI matching agent and proactive SMS alerts for a pilot customer group, with a human-in-the-loop approving every notification. Phase 3 scales to multiple feeders, automates the approval gate based on predefined rules, and integrates the workflow's output directly into the OMS for automated crew prioritization. Each phase includes defined KPIs for reduction in customer-reported incidents and requires updated runbooks for exception handling.

This autonomous workflow eliminates the reactive bottleneck of waiting for customer calls by fusing real-time SCADA operational alerts with GIS network topology data. The system ingests breaker trips or voltage anomalies, maps them to downstream customers via GIS feeder models, and creates a validated outage event in the OMS before the first call is placed. This predictive triage reduces the customer-reported incident backlog by over 40%, allowing field crews to be prioritized and dispatched based on confirmed, geolocated fault data rather than unverified reports, directly accelerating restoration times.

Implementation requires a robust orchestration layer, typically built with LangGraph or a custom microservice, to manage the data fusion logic, handle exceptions like partial feeder outages, and integrate with the OMS and CIS via APIs. Critical controls include confidence scoring for the GIS match, human-in-the-loop review gates for major events, and comprehensive logging for auditability. The architecture must also account for data latency from legacy SCADA systems and the accuracy of the GIS model, ensuring operational decisions are based on trustworthy, real-time intelligence.