HENGI

INFRASTRUCTURE INTELLIGENCE

BETTER INSIGHT. EARLIER INTERVENTION.

BETTER INSIGHT.

EARLIER INTERVENTION.

Predictive intelligence for water network infrastructure

Hengi is developing an integrated predictive-maintenance approach for water infrastructure, connecting simulation, Edge AI and targeted inspection in a continuous workflow. The aim is to help operators understand asset condition, identify emerging risks and intervene earlier.

Hengi is developing an integrated predictive-maintenance approach for water infrastructure, connecting simulation, Edge AI and targeted inspection in a continuous workflow. The aim is to help operators understand asset condition, identify emerging risks and intervene earlier.

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Explore our approach

Built by a team from Imperial College London, combining skillsets across AI, software and engineering.

Built by a team from Imperial College London, combining skillsets across AI, software and engineering.

THE PROBLEM

Infrastructure is difficult to see. Failure shouldn’t be.

Infrastructure is difficult to see. Failure shouldn’t be.

01

Limited visibility

Hidden conditions make critical decisions difficult.

01

Limited visibility

Hidden conditions make critical decisions difficult.

02

Reactive maintenance

Teams often act after symptoms emerge, not before risks compound.

02

Reactive maintenance

Teams often act after symptoms emerge, not before risks compound.

03

Expensive inspection

Inspection capacity is scarce, costly and difficult to prioritise.

03

Expensive inspection

Inspection capacity is scarce, costly and difficult to prioritise.

OUR APPROACH

From uncertainty to a focused next step

From uncertainty to a focused next step

01

Simulate

Model asset behaviour using physics-based simulations and identify areas of potential risk.

02

Inspect

Direct sensing and inspection directed where information is most valuable.

03

Predict

Real world data refines the model, generating more informed forecasts and predictions.

TECHNICAL SOLUTION FRAMEWORK

A continuous loop from the physical world to predictive action.

A continuous loop from the physical world to predictive action.

Predictive maintenance loop

01 Physical-world model

02 Edge AI inspection

03 Prediction & Prioritisation

DATA FEEDBACK & CONTINUOUS CALIBRATION

DATA FEEDBACK & CONTINUOUS CALIBRATION

Identifies higher-risk assets early, helping target inspection and maintenance resources more efficiently.

Multi-source data integration

Multi-dimensional simulation

State evolution

Shifting from reactive anomaly detection to predictive and proactive maintenance

Trend forecasting

Risk identification

Maintenance recommendation

Beyond just collecting data, edge devices decide what to probe and how to inspect.

Live-stream anomaly detection

Adaptive sensing

Network-aware upload

CROSS-INFORMED INSPECTION CYCLE

Real-world data refines the model; the improved model informs the next inspection.

Real-world data refines the model; the improved model informs the next inspection.

APPLICATION FIELD: WATER NETWORK

Water networks are where predictive intelligence can start making a difference now.

What we could enable


Prioritise inspection: identify where investigation may be most valuable.
Understand deterioration: combine network information, simulation and real-world data signals.
Intervene earlier: help operators make more informed maintenance decisions.

PARTNERSHIPS

Building the future of infrastructure intelligence.

Building the future of infrastructure intelligence.

We are currently developing our first demonstration system, launching by the end of 2026. We are looking for utility and innovation partners.
email: contact@hengi-tech.com

We are currently developing our first demonstration system, launching by the end of 2026. We are looking for utility and innovation partners.
email: contact@hengi-tech.com

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HENGI

Predictive intelligence for infrastructure systems