"QuaerisAI transformed how our operations team accesses data. Instead of waiting for weekly reports, our engineers get answers in seconds, with full traceability back to the source. We've cut incident response time dramatically."
Frequently Asked Questions
Agentic analytics for electric utilities is a class of AI software where autonomous agents — not dashboards — answer plain-English questions about grid, meter, and customer data by planning and executing multi-step queries across the utility's existing systems. Unlike traditional BI tools that visualize what someone already modelled, an agentic platform plans a sequence of steps (fetch, join, filter, forecast, root-cause), executes them against AMI, SCADA, OMS, GIS and billing data, and returns a governed, citation-backed answer. For utilities and cooperatives, this means an operations manager, member-services agent, or field supervisor can ask "which feeders had the most blink counts this month with no tree-trimming work order since 2024?" and get a direct answer in seconds, instead of waiting on an IT ticket.
The best analytics platform for an electric cooperative is one that connects natively to the systems you already run — NISC, Milsoft, Landis+Gyr, ESRI, your billing CIS — lets non-technical staff ask questions in plain English, and goes live in weeks rather than the year-plus a traditional data-warehouse project demands. Co-ops have lean IT teams (typically three to ten people), tight budgets, and data scattered across multiple vendor systems. The platforms that win in co-op environments share three traits: they read data where it lives instead of forcing a migration, field crews and member-services agents can use them directly, and pricing scales with meter count rather than starting at investor-owned-utility minimums. Quaeris was built for exactly that profile.
Utilities reduce SAIDI and SAIFI by combining OMS, AMI, GIS and weather data to spot the patterns behind outages before they recur — and by giving control-room and field teams that picture instantly during an incident. The biggest reliability gains come from three repeatable analytic moves: cross-referencing blink counts and momentary interruptions with vegetation work-order history to target right-of-way investment; detecting transformers operating above rated capacity from yesterday's peak load to drive proactive replacement; and assembling a unified incident snapshot — alarm timelines, recent line maintenance logs, impacted member counts — in seconds instead of toggling between three vendor systems. National Grid reported a 30% drop in tree-related events and a 38% reduction in customer interruptions using a data-driven vegetation approach; the same playbook is available to any co-op willing to query across its silos.
You unify AMI, SCADA, GIS and OMS data by querying each system in place through an agent that translates plain-English questions into governed queries — no central data warehouse, no months-long ETL project. Quaeris connects to each system as a data source, learns its schema and business definitions automatically through the Smart Semantic Layer, and joins data across them at query time. A single question like "show me substations with more than 5 blink counts this month that haven't had tree-trimming since 2024" pulls blink counts from the OMS, work orders from the asset and GIS systems, and produces one answer — without a warehouse build, without an IT ticket, and with a full audit trail of every step the agent took.
Co-ops analyze AMI and smart meter data without a data warehouse by connecting an agentic analytics layer directly to their meter data management (MDM) system and other source databases, then asking questions in plain English instead of building ETL pipelines. Traditional smart-meter analytics projects require Azure Synapse, Snowflake or a similar warehouse, daily ingestion pipelines, and a BI team to model the data — months of work and significant cost. Quaeris reads meter data where it lives, learns the relationships between AMI, billing, weather and asset data through use, and answers questions like "compare this member's hourly usage against last year, correlated with daily high temperatures" in seconds. The co-op keeps its existing MDM (NISC, Itron, Aclara, Sensus, Landis+Gyr) and skips the warehouse build entirely.
Yes — agentic analytics can be deployed safely on critical infrastructure when it runs inside the utility's own VPC or on-prem environment, uses a governed semantic layer to prevent free-form SQL, and produces a full audit trail of every prompt and agent step. Quaeris is architected around exactly these properties: customer data stays inside the utility's controlled environment, the AI model is the customer's choice (BYOM across OpenAI, Anthropic, Google, Meta, or a private/on-prem model), every query passes through a governed semantic layer that blocks arbitrary table access, and a prompt-level audit log records who asked what, which model answered, and which steps the agent executed. These are the architectural controls that NERC CIP, SOC 2, HIPAA and EU AI Act assessors actually evaluate, and they're standard in every Quaeris deployment.
Yes — Quaeris is a complementary, model-agnostic alternative to Oracle Utilities Analytics, NISC Operations Analytics, and similar utility BI products, designed to add a natural-language agent layer on top of the systems a utility already runs. Where those platforms require fixed reports, vendor-specific modeling, or a unified warehouse on their stack, Quaeris connects to your existing AMI, SCADA, OMS, GIS, billing and document systems and lets staff query across all of them in plain English. You don't replace NISC, Milsoft or Oracle — you give every operations manager, field supervisor, and member-services agent a faster way to get answers out of them, with no additional analyst on the bench.