AI Water Management
AI works best on data you're already collecting
Hydrotec's platform is built with AI and IoT as part of its long-term direction — using consumption, device and quality data already captured across the platform to support forecasting, anomaly detection and predictive maintenance as these capabilities mature.
Network Monitoring — Live
Illustrative
Sites Online
482 / 501
Litres Monitored
1.84M
Active Alerts
3
Avg. Water Quality
97.2%
Turbidity (NTU) — 7 days
Device Fleet
- ATM-0142New Delhi
- ATM-0143Konark
- PLANT-07Nashik
- ATM-0209Kasba, Kolkata
- SENSOR-14Khamar
- ATM-0301Ayodhya
The problem
Reactive operations cost more than predictive ones
Most water operators respond to problems after they occur — a machine breaks down, quality drifts out of range, or demand spikes unexpectedly. Historical data usually contains the early warning signs.
- Maintenance happens after failure, not before
- Demand spikes caught only once machines run dry or networks are stressed
- Quality anomalies detected by threshold alerts rather than pattern recognition
- Years of operational data collected but not used for forecasting
The Hydrotec approach
Applying AI to data the platform already has
Because Hydrotec's platform already captures continuous device, consumption and quality data, that same data is the foundation for AI-driven forecasting and anomaly detection — developed and rolled out progressively rather than promised as a single finished feature.
AI Forecast & Anomaly Detection
Planned capability7-Day Demand Forecast — Illustrative
Model Confidence
Depends on data volume per site — no fixed accuracy figure published.
Anomaly Feed — Illustrative
- Konark KioskTDS drifting above baselineHigh
- Kasba DepotDispensing volume 18% below forecastMedium
- NDMC DelhiPattern consistent with normal operationHigh
Features
What's included
Demand forecasting
Predict consumption patterns to plan stock, maintenance visits and capacity.
Anomaly detection
Flag unusual patterns in quality or flow data that simple thresholds might miss.
Predictive maintenance
Identify machines likely to need service before they fail.
Trend intelligence
Surface long-term patterns across sites that aren't obvious from raw dashboards.
Built on real operating data
Models trained on the same operational data already captured by the platform.
Progressive rollout
Capabilities introduced as they're validated against real deployments, not oversold upfront.
Architecture
AI as a layer over existing data, not a separate system
Forecasting and anomaly detection models are designed to run on top of the same device, consumption and quality data already flowing through the platform — no separate data pipeline required.
Plants, networks, ATMs, tanks
Sensors, meters, controllers
Ingestion, storage, rules
REST & webhooks
Web operations console
Field & customer apps
Forecasts, anomalies
Use cases
Where this fits
Water ATM fleet planning
Forecast demand by site to plan refills, staffing and maintenance visits.
Early quality warnings
Pattern-based detection to complement fixed quality thresholds.
Utility-scale demand planning
Longer-horizon forecasting to support capacity and investment decisions.
Technical detail
Current status
AI capability is part of Hydrotec's stated technology direction and is being developed against real operating data. Specific model capabilities below are described as planned rather than guaranteed current features — confirm current availability with our team.
- Forecasting and anomaly-detection capability under active development
- Built on historical device, consumption and quality data already collected
- Rollout planned progressively per capability, validated against real deployments
- No claim of a fixed accuracy figure without deployment-specific validation
{
"site_id": "site-nairobi-14",
"forecast_horizon_days": 7,
"predicted_demand_litres": 18400,
"confidence": "planned-capability"
}Why it matters
Benefits
Fewer surprise failures
Predictive signals aim to catch problems before they escalate.
Better planning
Forecasts support smarter staffing, stocking and capacity decisions.
No new data collection needed
Built on data the platform already captures.
Honest rollout
Capabilities introduced as they're validated, not all promised at once.
FAQ
Common questions
Are these AI features available today?
AI-driven forecasting and anomaly detection are part of Hydrotec's technology direction and under active development on real operating data. Availability varies by capability and deployment — talk to our team about current status for your use case.
What data does the AI use?
The same device, consumption and quality data already captured across Hydrotec's platform — no separate AI-specific data collection is required.
Will this replace threshold-based alerts?
No — pattern-based anomaly detection is intended to complement, not replace, the threshold alerts already available across the platform.
How accurate are the forecasts?
Accuracy depends on data volume and quality at a given site, and improves as more historical data accumulates. We don't publish a blanket accuracy figure without deployment-specific validation.
Get started
Ask us about AI capability for your deployment
We'll give you an honest picture of what's available today versus what's on the roadmap.