System Silos
14 systems. Zero joins.
Billing lives in Oracle. SCADA in a proprietary historian. Meter reads in flat files. GIS in a spatial DB. Each system answers one question. None answer cross-system questions.
Solution Power & Distribution
Grid data unified. Losses recovered.
Qwry.AI connects SCADA, billing, metering, GIS, and consumer databases, then resolves identity across them (one consumer, one meter, one feeder, however each system records it) without replacing any of them. AT&C loss analytics, tamper detection, and demand forecasting become questions you can ask in plain English.
SCADA & Billing Unified AT&C Loss Analytics Tamper Detection On-Prem Ready
The Problem
The data exists. The correlation doesn't.
A DISCOM runs dozens of data systems across billing, metering, SCADA, GIS, and consumer databases, none of them joined. Cross-system questions that should take minutes take weeks.
AT&C Losses
Loss identification takes weeks.
Correlating energy injected with energy billed requires joining SCADA with billing with consumer records. Without Qwry.AI, this is a manual exercise repeated every quarter.
Tamper Detection
Tampering is visible. Suspects aren't.
Anomalous consumption patterns exist in the data. Matching them to consumer accounts, transformer zones, and field inspection history requires cross-database joins that don’t exist today.
Demand Forecasting
Forecasts without full context.
Demand models built on billing data alone miss SCADA load curves, seasonal patterns, and infrastructure constraints that sit in separate systems.
Use Cases
Questions your teams ask every day.
Each of these queries joins SCADA, billing, metering, and GIS data, with verified SQL behind every answer, inside your perimeter.
AT&C Loss Attribution
Join energy injected (SCADA) with energy billed (billing DB) by feeder, transformer, and zone. Surface the feeders and divisions where technical and commercial losses exceed thresholds.
SCADABilling DBGIS
Tamper Candidate Surfacing
Cross-reference anomalous consumption patterns with meter read sequences and transformer load data. Rank tampering candidates by confidence score and zone, before field teams are dispatched.
Meter ReadsBillingSCADA
Demand Forecasting
Combine SCADA load curves with billing history, weather data, and infrastructure records. Generate feeder-level forecasts that account for the full system context.
SCADABillingGIS
Outage Root Cause
Join outage events with maintenance records, asset age, and load history. Identify the assets and zones at highest risk before the next failure, not after it.
SCADAMaintenance DBGIS
Revenue Recovery Tracking
Track disconnection, reconnection, and regularisation actions against identified loss clusters. Measure the revenue impact of enforcement campaigns in real time.
BillingField RecordsFinance
Regulatory Reporting
Generate AT&C loss statements, energy audit reports, and compliance submissions directly from the unified warehouse. Every figure traces to its source query.
All SourcesAudit Trail
Capabilities
Built for the data complexity of grid operations.
Connectivity
Reads legacy systems without replacement
Oracle billing, proprietary SCADA historians, flat-file meter exports, GIS databases. Qwry.AI connects to what is already running, with no system replacement and no migration project.
Verification
Every loss figure has a source query
AT&C percentages, tamper candidate counts, revenue figures; every number in the warehouse traces to the SQL that produced it. Admissible in regulatory submissions.
Sovereignty
Data never leaves your perimeter
On-premise deployment with your own PostgreSQL warehouse. SCADA and consumer data stays inside your infrastructure. Qwry.AI orchestrates; you own everything.
Workspace
Plain English for non-technical teams
Revenue officers, field supervisors, and regulatory teams query the warehouse in plain English. The platform generates SQL, verifies the answer, and returns a result anyone can act on.
Insights
Use cases & field notes from the data trenches.
Customer playbooks, technical deep-dives and field notes from deployments into messy enterprise estates.
Technical
JOIN dms_secondary ON erp.sku_code
→ 94% match · conf 0.94
→ 94% match · conf 0.94
Contextual data in personal care manufacturing & distribution: the context between systems is the missing asset.
Every personal care manufacturer generates data at every stage of its value chain, and cannot join it. How automated relationship discovery turns five to eight disconnected systems into cross-source answers in minutes, not days.
Read the white paper Use Case
ERP Secondary Sales GST
One view across distributors, billing, and compliance: how pharma distribution stops flying blind.
Secondary sales in spreadsheets. Distributor stock self-declared. Billing in the ERP. Compliance records as PDFs. How Qwry.AI connects them into one governed view, and what becomes possible when it does.
Read the use caseConnectors
Reads from the systems you already have.
Databases, warehouses, files and the apps you run: connect what you already have, don't migrate. Every source lands in one governed graph, with 200+ on the roadmap.
Don't see your connector? Tell us your stack. We'll scope it.
Reach out →Ready to recover
what the data already shows?
A direct technical conversation with the founding team. No sales deck. Tell us about your systems and we'll map out what becomes possible when they behave like one.
Email goes directly to the founding team. First response within one business day.