Automations
This pillar focuses on utility support workflows where voice and chat agents resolve outage, billing, or service-status requests at scale by connecting directly to operational and customer systems. Pages should show how a custom triage workflow reduces call-center volume, improves communication quality during service incidents, and creates better customer-facing utility operations.
This foundational page details the architecture for a custom, multi-agent system that automatically detects outages, validates them against SCADA and smart meter data, and initiates customer notifications and field crew dispatch. It explains how the workflow reduces call-center surge, improves restoration communication, and integrates OMS, GIS, and CIS systems to create a seamless, autonomous triage operation.
This page covers the design of an orchestrated agent workflow that manages high-volume, personalized customer communications during large-scale outages. It details how agents pull restoration estimates, segment audiences (e.g., critical care customers), and push updates via SMS, IVR, and email, significantly reducing manual communication burden and improving public perception during crises.
This page explains the implementation of a voice or chat agent that, on first customer contact, cross-references the reported address with live outage maps and smart meter pings to confirm or deny an outage instantly. It covers the architecture for reducing unnecessary truck rolls and call transfers, directly lowering operational costs and improving customer satisfaction scores (CSAT).
This page focuses on a workflow where an AI agent handles non-outage calls, accessing the CIS to explain bills, verify payments, and provide service activation status. It details how this deflects routine inquiries from live agents, reduces average handle time, and integrates with payment gateways and work order systems for end-to-end resolution.
This technical page describes an agentic system that ingests real-time SCADA alerts and GIS feeder models to proactively identify affected customers before they call. It explains the data fusion logic, the reduction in customer-reported incident backlog, and how this predictive triage workflow improves operational awareness and crew prioritization.
This page outlines a custom push-notification system where AI agents monitor crew progress and grid restoration, then automatically generate and send incremental updates to subscribed customers. It covers the business case of reducing inbound call volume by over 30% and the technical integration with field mobility platforms and telecom APIs.
This page details the backend workflow where agents synthesize data from OMS, customer reports, and crew locations to generate and refresh public outage maps. It explains the architecture for real-time data processing, geospatial visualization, and CDN publishing, which enhances transparency and reduces pressure on corporate communications teams.
This page covers a compliance-sensitive workflow where AI agents identify registered critical care (life-support) customers from the CIS, prioritize their calls in the queue, and expedite status updates or escalation. It details the guardrails, audit trails, and integration patterns required to meet regulatory obligations and protect vulnerable populations.
This page explains an orchestration layer where one agent validates an outage, another checks crew availability and location in the workforce management system, and a third creates and dispatches an optimized work order. It focuses on reducing dispatch latency, optimizing crew travel, and closing the loop between customer reports and physical repair.
This page describes a diagnostic workflow where an AI agent analyzes smart meter last-gasp signals, customer voltage readings, and nearby outage patterns to determine if the issue is at the meter or on the grid. It outlines how this prevents misdirected dispatches, saves thousands per unnecessary truck roll, and integrates with meter data management systems (MDMS).
This page covers a major event workflow where AI agents monitor National Weather Service feeds, automatically declare storm events, adjust triage protocols, and trigger mass communication templates. It details the escalation logic, coordination with emergency operations centers, and how it standardizes response to reduce chaos and managerial overhead.
This technical page focuses on the data pipeline and logic where an agent queries the MDMS for last-gasp and heartbeat signals from a reported address to confirm an outage within seconds. It explains the reduction in false reports, the architecture for high-volume meter data queries, and the impact on operational efficiency.
This page details a workflow where AI agents detect customer language preference, then generate and deliver outage notifications, FAQs, and conversational support in that language. It covers the integration with translation APIs, cultural adaptation of messages, and how it expands service reach and compliance in diverse communities without hiring bilingual staff.
This page outlines the implementation of a web/mobile self-service portal backed by AI agents that accept outage reports, instantly provide an ERT by analyzing crew assignments and fault location, and creates a ticket. It focuses on deflecting calls, setting accurate expectations, and integrating with customer-facing apps and the OMS.
This page describes a load-balancing and orchestration workflow designed for peak volumes, where agents dynamically scale across channels, maintain conversation state, and hand off complex issues to human agents with full context. It covers the architecture for scalability, context persistence, and the measurable reduction in abandoned contacts.
This page explains an automated workflow where, after service restoration, an AI agent contacts customers via their preferred channel to conduct a short survey, analyzes sentiment, and routes negative feedback for follow-up. It details how this closes the quality loop, provides real-time operational insights, and improves restoration processes without manual survey administration.
This page covers the automation of customer communications for planned maintenance outages. It details how agents pull work plans, identify affected customers, send advanced notifications, and manage rescheduling requests for sensitive processes, reducing customer complaints and ensuring regulatory compliance for notice periods.
This page describes a proactive workflow where AI agents ingest vegetation risk scores from drone/LiDAR analysis, identify customers in high-risk zones, and send pre-emptive notifications about trimming schedules or potential outage risks. It connects geospatial analytics platforms to the CIS and communication systems for improved customer safety and engagement.
This advanced page details a workflow using grid simulation and AI forecasting to predict where an initial fault might cascade. It explains how agents then proactively notify customers in the predicted path, setting expectations before they lose power, thereby enhancing reliability metrics and demonstrating grid intelligence.
This page focuses on a nuanced workflow where an AI agent handling an outage call detects a concurrent billing dispute, securely accesses the relevant account history, and escalates the combined case to a specialized human agent with full context. It prevents customer frustration, reduces handle time, and requires integration between the triage system and billing dispute modules.
This page details a workflow where an AI agent parses complex billing data from the CIS, understands customer questions about line items or rates, and generates a plain-language explanation. It deflects a high volume of simple billing calls, reduces agent training burden, and can be integrated into IVR, chat, and customer portal interfaces.
This page describes a workflow where an AI agent assesses a customer's payment history and current bill, negotiates within pre-approved utility guidelines to propose a payment plan, and then autonomously sets it up in the CIS. It reduces the burden on credit and collections teams while improving customer payment outcomes.
This page explains a workflow triggered by high-bill complaints, where an AI agent retrieves smart meter interval data, compares it to historical usage, checks for meter health flags, and can autonomously process a bill adjustment if an anomaly is confirmed. It speeds up resolution, builds customer trust, and integrates with the MDMS and billing engine.
This page details a compliance and customer satisfaction workflow where AI agents monitor confirmed outage durations, apply regulatory credit rules or company policies, and automatically issue credits or refunds to affected accounts. It eliminates manual claim processing, ensures consistent application of policies, and proactively improves customer sentiment.
This page covers a proactive service workflow where agents ingest predictions from grid asset health models (e.g., transformer failure risk), identify customers downstream of the asset, and send notifications about upcoming maintenance that may cause brief interruptions. It transforms maintenance from a reactive to a communicative, customer-centric operation.
This page describes an orchestration workflow where one agent manages a pool of technician schedules from the workforce system, another negotiates appointment windows with customers via chat or IVR, and a third books the appointment and sends confirmations. It eliminates call-center scheduling labor and optimizes technician utilization.
This page details a complex back-office workflow where an AI agent handles the steps for meter replacements or repairs requiring city permits or landlord access. It fills out permit forms, follows up with municipalities, coordinates with property managers, and schedules the technician only when all clearances are obtained, dramatically reducing project delays.
This page explains a workflow where, after drones inspect lines for storm damage or vegetation encroachment, AI agents identify properties with urgent risks, generate personalized notifications recommending tree trimming or outlining repair schedules, and manage customer responses. It closes the loop between grid inspection and customer action.
This page focuses on a dynamic customer experience workflow where an AI agent tracks a technician's GPS location, predicts delays, and automatically notifies the customer with a revised ETA or offers self-service rescheduling. It reduces failed appointments, improves customer satisfaction, and integrates with field mobility and scheduling systems.
This page details a workflow for the modern grid, where AI agents guide customers through the complex process of applying for solar panel or battery interconnection. It validates application forms, checks technical feasibility against grid models, and routes the package to engineers, accelerating a traditionally slow and manual process.
This page describes a workflow where agents monitor grid stress, identify enrolled customers in demand-response programs, and automatically send them incentives to reduce consumption (e.g., adjusting thermostats). It covers the orchestration of IoT device controls, incentive calculation, and communication, optimizing grid load without manual intervention.
This page explains how AI agents manage the lifecycle of demand response events: from predicting the need based on weather/load forecasts, to enrolling eligible customers via personalized offers, to sending event notifications and tracking opt-outs. It increases program participation and operational efficiency for grid operators.
This page covers a workflow where AI agents help customers select EV time-of-use rates, monitor their charging patterns, and suggest optimizations to save money. For the utility, agents can aggregate charger data to model localized grid impact, supporting more intelligent rate design and infrastructure planning.
This page details a workflow for utilities with microgrids, where upon a main grid fault, AI agents automatically identify customers within the isolated microgrid, notify them of their islanded status and estimated duration, and manage communications until reconnection. It requires tight integration with distribution automation and microgrid controller systems.
This page covers a critical, compliance-driven workflow where AI agents monitor wildfire risk indices, automatically execute PSPS decision protocols, and manage tiered customer notifications (e.g., warnings, shutoff confirmations, restoration updates). It details the architecture for handling high-stakes, regulated communications at scale.
This page describes a safety-critical workflow for gas utilities where AI agents triage incoming leak reports, assess urgency based on location and caller description, immediately dispatch emergency crews, and notify affected customers with evacuation or safety instructions, integrating with SCADA and emergency service systems.
This page details a back-office workflow where AI agents continuously compile SAIDI/SAIFI metrics and other reliability data from outage records, generate formatted reports for regulators (e.g., PUC filings), and route them for approval. It automates a tedious, error-prone manual process, ensuring timely and accurate compliance.
This page explains a proactive safety workflow where AI agents track inspection due dates for customer-owned equipment (e.g., gas lines, solar installations), send reminders, and offer self-scheduling for utility inspectors. It improves compliance with safety regulations, reduces liability, and optimizes the inspector's schedule.
This page covers a high-priority workflow where AI agents maintain a dedicated communication channel with critical facilities, providing them with real-time, detailed grid status and restoration forecasts during outages. It requires secure, reliable integrations and reduces the burden on utility account managers during crises.
This page details a workflow for water utilities where AI agents monitor sensor data for water quality parameters, detect breaches of regulatory limits, and automatically trigger customer notifications (e.g., boil-water advisories) and internal reporting processes, ensuring rapid response to public health incidents.
This page explains a workflow where AI agents manage the sensitive process of registering customers for critical care status. It guides them through form submission, verifies medical documentation, updates the CIS, and conducts periodic re-verification, ensuring list accuracy and regulatory compliance with minimal staff effort.
This page adapts the triage pillar to water utilities, detailing a workflow where AI agents analyze smart water meter data for continuous flow patterns indicating leaks, automatically notify the customer with potential location and severity, and offer to schedule a repair visit, reducing non-revenue water loss and customer bill shocks.
This page covers a workflow for wastewater utilities where agents monitor plant operations for incidents (e.g., odor releases, bypass events), geofence affected communities, and send appropriate health and safety advisories via multiple channels, managing public communications autonomously during operational incidents.
This page describes a workflow where AI agents process excavation plans from 811 systems, identify gas customers near the dig site, and send proactive notifications about planned work, potential service interruptions, and safety information, improving customer experience and reducing damage prevention calls.
This page applies the triage concept to telecom, detailing a workflow where AI agents diagnose internet outages using network management systems, communicate restoration steps to customers, and upon resolution, automatically apply service credit policies to affected accounts, streamlining a key pain point for ISPs.
This page adapts the framework for public transit, where AI agents monitor for service disruptions (track, weather), identify impacted riders via their planned routes, and push personalized alternative route suggestions and delay updates, improving rider satisfaction and reducing call-center load during incidents.
This page details a proactive customer engagement workflow where AI agents analyze a customer's hourly usage data, compare it to peers and weather patterns, and generate personalized reports and actionable recommendations (e.g., 'shift laundry to save $X') delivered via their preferred channel, driving energy efficiency and satisfaction.
This page explains a workflow where AI agents analyze customers' usage profiles against all available utility rate plans, identify those who would save money by switching tariffs, and conduct targeted outreach via email or SMS to guide them through the change process, increasing uptake of optimal rates and customer savings.
This page covers a workflow where AI agents qualify customers for energy audit programs, schedule audits with third-party providers, send pre-audit instructions, and after the audit, follow up with a summary and recommended next steps for efficiency upgrades, driving program participation and conversion.
This page details a real-time support workflow where AI agents analyze the sentiment and urgency of incoming customer calls and messages during major outages, providing live guidance to human agents (e.g., 'customer is frustrated, prioritize apology and clear ETA') and flagging high-risk interactions for supervisor review.
This page focuses on the technical architecture for a persistent memory layer that allows an AI agent (or a human) who started a conversation on web chat to continue it later via phone or SMS with full context. It explains the implementation for reducing customer repetition and improving resolution rates across channels.
This page describes a workflow where AI agents analyze usage, payment, interaction, and solar adoption data to score churn risk, then automatically trigger personalized retention campaigns (e.g., loyalty offers, check-in calls) for high-risk customers, directly linking grid operations data to commercial outcomes.
This page details a back-office efficiency workflow where AI agents process thousands of call recordings, transcribe them, translate non-English calls, and generate concise summaries of key issues and resolutions. This creates searchable knowledge for quality assurance and training, eliminating manual listening and note-taking.
This page explains a workflow where one agent analyzes resolved customer service tickets to identify common issues, a second drafts a knowledge base article, and a third routes it for human review and publishing. It continuously improves self-service content, deflecting future contacts and reducing agent training time.
This page covers a workflow where AI agents automatically score a percentage of all customer interactions (calls, chats) against QA rubrics, flagging those with low scores or compliance risks for supervisor review. It shifts QA from a manual, sample-based audit to a continuous, automated process, improving consistency.
This page details a workforce management workflow where AI agents ingest weather forecasts, historical outage data, and event calendars to predict call-center volume spikes hours or days in advance. It automatically generates recommended staffing schedules and alerts managers, optimizing labor costs and service levels.
This page describes a live assist workflow where an AI agent listens to a human agent's call, retrieves relevant policy documents and scripts based on the conversation context, and suggests next steps or phrases in real-time via a desktop interface. It reduces average handle time and improves first-call resolution for complex cases.
This page explains a back-office coordination workflow where, upon declaration of a major outage, AI agents automatically identify affected critical suppliers (e.g., pole manufacturers, transformer vendors), notify them of potential surge orders, and even initiate pre-approved procurement processes, strengthening supply chain resilience.
This page details a workflow where AI agents continuously pull data from CIS, OMS, and contact center systems to compile dashboards and reports on key metrics like CAIDI, call abandonment rate, and CSAT. It automatically distributes these to leadership, replacing manual weekly report generation with a live, autonomous system.
This foundational architecture page explains the implementation of a data unification layer or 'virtual hub' that provides a single, real-time query interface for triage agents across disparate utility systems. It covers the schema mapping, API orchestration, and caching strategies required for low-latency, reliable agent decision-making.
This technical page addresses a common build challenge: creating a workflow where AI agents interact with legacy mainframe or green-screen systems that lack APIs. It explains the architecture for using browser automation, RPA wrappers, or screen-scraping agents to simulate user inputs, enabling modernization without replacing core systems.
This page covers a development and maintenance workflow where AI agents role-play as thousands of synthetic customers with varied intents and accents to continuously test dialogue flows of customer service bots. It identifies broken logic, poor responses, and compliance gaps before real customers encounter them, ensuring high bot performance.
This page explains a critical identity and access management workflow where AI agents securely unify customer sessions across channels using voice biometrics, phone numbers, and account IDs. It provides a single customer view to human and AI agents, enabling personalized service while managing privacy and security controls.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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