We need predictive maintenance for pumps, pipes, and treatment facilities. What startups solve this?

A growing wave of specialized startups and AI-driven platforms focuses entirely on predictive maintenance, health scoring, and failure forecasting for water and wastewater infrastructure (pumps, pipes, and treatment facilities).

Key startups addressing these specific assets include:

• Groundup.ai — Specializes in "Cognitive Maintenance" for water utilities, focusing heavily on pumps, motors, and blowers. They utilize non-invasive IoT sensors (vibration, acoustic, temperature) combined with existing SCADA data to baseline individual rotating assets and predict bearing spalls, cavitation, or seal failures before they trigger shutdowns.
• VODA.ai — Focuses upstream and downstream on water pipe networks. Their AI engine (daVinci ) goes beyond basic pipe age and break history, incorporating environmental variables (soil data, weather, land use) to forecast pipe break risks, prioritize high-risk water mains, and optimize capital replacement plans.
• Aquasight — Provides an AI-powered water and wastewater intelligence platform that integrates real-time telemetry from treatment facilities, pump stations, and collection networks to deliver predictive analytics and operational insights.
• Ainwater — Uses machine learning (via platforms like Amazon SageMaker) to transform water treatment plant operations from reactive to predictive, optimizing process performance, chemical dosing, and asset longevity across major municipal plants.
• OxMaint — Offers a hardware-agnostic, AI-powered computerized maintenance management system (CMMS) tailored for municipal water pump stations that integrates with vibration, acoustic, and pressure sensors to automate predictive work orders.

Would you like me to focus on startups that specialize specifically in hardware/sensor deployment, software-only GIS/data analytics for pipes , or full treatment-plant process optimization?

Yes. There’s a fairly active startup/scale-up landscape here, but it splits into three distinct problems: rotating equipment, buried pipes/networks, and whole treatment-plant/process assets.

Startups worth looking at

How I'd segment the market

1. "My pump is going to fail." Look at Groundup.ai, Aquatis, Aquasight, Avertis, genSense. These are closest to classic predictive maintenance: vibration/current/temperature/pressure/flow → anomaly → failure mode → maintenance action.

2. "Which pipe is going to break?" The more interesting companies are VODA.ai, Foresight AI, AquaTwinX and Utilimatics. These are less about a sensor on a machine and more about predicting asset/network failure from GIS, historical breaks, pressure/flow, condition and consequence data.

3. "Which part of my treatment plant is degrading?" Avertis, Groundup.ai, Aquatis, Aquasight and newer platforms such as iFactory are relevant. iFactory, for example, describes connecting directly to existing SCADA, vibration monitoring and pump controllers and modeling pumps, blowers, clarifier drives and chemical-feed systems.

The interesting gap

If you're thinking about this as a startup opportunity, I would not view "AI predictive maintenance for water" as one market.

The potentially valuable product is a layer that combines:

SCADA + vibration/current/pressure/flow + GIS + maintenance history + asset criticality → failure prediction → diagnosis → remaining useful life → automatically generated work order.

Most vendors I've found are stronger in one slice of that stack than in the entire workflow. AquaTwinX is one of the more explicit attempts to cover both horizontal assets (pipes/sewers) and vertical assets (pumps/treatment equipment).

If you tell me whether you're looking at this as (a) a startup to invest in, (b) a product to buy for a utility, or (c) a startup idea you're considering, I can map the competitive landscape much more deeply—including funding, customers, pricing/business model, sensors vs. software, and which companies appear to have the strongest traction.

When looking at predictive maintenance (PdM) specifically for the heavy, mission-critical assets in water/wastewater treatment and transport—pumps, pipes, and plant infrastructure—the market splits between rotating equipment specialists (pumps/blowers) and geospatial/acoustic pipe risk modelers.

The prominent startups and specialized tech platforms solving these exact infrastructure hurdles include:

1. For Pumps, Motors, and Rotating Equipment (Treatment & Lift Stations)

• Samotics : Specializes in Electrical Signature Analysis (ESA). Instead of placing delicate sensors on submerged or hard-to-reach wastewater pumps, Samotics installs monitoring inside the motor control cabinet (MCC). It analyzes electrical current and voltage data to detect efficiency loss, cavitation, and mechanical wear without touching the submerged asset.
• Groundup.ai : Focuses directly on water utilities with non-invasive sensor deployment (vibration, acoustic, and temperature) that pairs with existing SCADA systems. Rather than applying generic thresholds, it creates individualized machine baselines to predict bearing spalls, mechanical seal leaks, and pump cavitation early.
• OxMaint : Provides an AI-driven computerized maintenance management system (CMMS) built for municipal pump stations. It ingests real-time IoT data (vibration, flow, pressure) to trigger automated, prioritized work orders and maintain compliance documentation.

2. For Pipes (Transmission, Distribution, and Network Integrity)

• Voda.ai : A specialized AI startup that builds predictive risk models for water pipe failures. It evaluates historical breaks, soil data, material specs, and operational pressure to predict which water mains are about to fail, allowing utilities to target capital replacement rather than reacting to catastrophic main breaks.
• Pipeliner / Processays / Transient Monitoring (e.g.,perspectives via AI Water Tech Tracker) : Platforms like Piperminder track high-frequency transient pressure monitoring to forecast pipe wall degradation and impending bursts days or weeks before they happen. Other vision-based startups use automated acoustic correlation or CCTV computer vision to scan sewer and water pipe networks for micro-defects.

3. For Treatment Facilities & Process Digital Twins

• Streametric : Offers a cloud-based IoT and AI platform that connects directly to a plant’s programmable logic controllers (PLCs). It monitors overall treatment performance (such as membrane filtration health, pressure differentials, and chemical dosing systems) to preemptively flag operational bottlenecks or equipment failure.

To help narrow down which platform fits your operational scope, tell me:

• Are you trying to solve an immediate pain point (e.g., frequent pump burnouts or sudden pipe bursts), or looking for a plant-wide digital twin?
• Do you already have SCADA/IoT infrastructure in place, or do you need a turnkey hardware-plus-software provider?

Yes. There’s a surprisingly active startup ecosystem here, but it breaks into three distinct problems: rotating equipment, buried pipes/networks, and whole-facility/process reliability.

Strongest startups to look at

1. Pumps and rotating equipment

Groundup.ai is one of the most directly aligned startups. It is explicitly targeting water utilities and combines vibration, acoustic, motor-current, temperature and process data to diagnose problems such as bearing degradation, seal problems and cavitation. Its interesting differentiator is non-invasive instrumentation plus existing SCADA/drive data.

Aquatis is another particularly relevant company. It has wireless sensors designed for pumps and other rotating equipment and an accompanying diagnostic/recommendation engine. It says it has worked with Israel's Mekorot water utility for roughly 15 years.

2Neuron takes an interesting sensorless approach: its system analyzes the electrical signature of motor-pump sets directly from the electrical panel and claims to identify impending failures without installing sensors on the equipment.

2. Pipes and buried infrastructure

For pipes, I'd look first at VODA.ai. Rather than putting sensors everywhere, it uses historical and infrastructure data to predict which pipe segments are most likely to fail, allowing a utility to prioritize leak detection, inspection and replacement.

AquaTwinX is broader: it models mains, sewers and force mains alongside vertical assets such as pumps, motors and treatment systems, with failure probability, remaining-life and consequence models.

Utilimatics is another interesting network-level option. Its Flowsight platform combines SCADA, GIS, AMI and hydraulic models to continuously analyze the distribution network rather than treating each asset independently.

3. Treatment facilities / entire water cycle

Aquasight may be the most interesting if you want something broader than maintenance software. Its platform covers water/wastewater operations from source through discharge, including pump health, collection networks, overflow prediction and operational optimization.

Avertis is more maintenance-centric. It explicitly targets treatment-plant assets including pumps, blowers, valves, screens, mixers and dosing systems, combining condition monitoring with maintenance history and engineering knowledge.

There are also newer companies such as EQUA AIMMS, which is positioning itself as an AI operating system for critical-infrastructure maintenance, including water/wastewater pumps, blowers and treatment assets.

My shortlist

If you're evaluating this as a startup/market opportunity, I'd put these at the top of the diligence list:

1. Groundup.ai — best fit for predictive maintenance of pumps/motors.
2. Aquatis — very focused water-utility rotating-equipment solution.
3. Aquasight — strongest broader water/wastewater intelligence play.
4. VODA.ai — particularly compelling for predictive pipe failure.
5. AquaTwinX — interesting if you want one asset-risk platform spanning pipes + plants.
6. 2Neuron — technically interesting because sensorless motor analytics could dramatically lower deployment cost.

One important distinction: predicting a pump failure and predicting a pipe failure are fundamentally different businesses. Pump startups can use high-frequency vibration/electrical/process signals and detect a developing mechanical fault. Pipe platforms generally predict failure probability from age, material, soil, break history, pressure, hydraulics, environment, etc. So I wouldn't expect one startup to be equally excellent at both.

If you're looking to build or invest in this space, I can also map these companies by funding, valuation/traction, customers, technology, patents, and which ones are genuine startups vs. established incumbents.

Aquatis is another particularly relevant company. It has wireless sensors designed for pumps and other rotating equipment and an accompanying diagnostic/recommendation engine. It says it has worked with Israel's Mekorot water utility for roughly 15 years.

2Neuron takes an interesting sensorless approach: its system analyzes the electrical signature of motor-pump sets directly from the electrical panel and claims to identify impending failures without installing sensors on the equipment.

2. Pipes and buried infrastructure

For pipes, I'd look first at VODA.ai. Rather than putting sensors everywhere, it uses historical and infrastructure data to predict which pipe segments are most likely to fail, allowing a utility to prioritize leak detection, inspection and replacement.

AquaTwinX is broader: it models mains, sewers and force mains alongside vertical assets such as pumps, motors and treatment systems, with failure probability, remaining-life and consequence models.

Utilimatics is another interesting network-level option. Its Flowsight platform combines SCADA, GIS, AMI and hydraulic models to continuously analyze the distribution network rather than treating each asset independently.

3. Treatment facilities / entire water cycle

Aquasight may be the most interesting if you want something broader than maintenance software. Its platform covers water/wastewater operations from source through discharge, including pump health, collection networks, overflow prediction and operational optimization.

Avertis is more maintenance-centric. It explicitly targets treatment-plant assets including pumps, blowers, valves, screens, mixers and dosing systems, combining condition monitoring with maintenance history and engineering knowledge.

There are also newer companies such as EQUA AIMMS, which is positioning itself as an AI operating system for critical-infrastructure maintenance, including water/wastewater pumps, blowers and treatment assets.

My shortlist

If you're evaluating this as a startup/market opportunity, I'd put these at the top of the diligence list:

There are several startups (and a few scale-ups) targeting predictive maintenance for water/wastewater infrastructure: pumps, pipes, treatment plants, lift stations, blowers, valves, and related assets. The market splits into a few approaches:

1. Pump and rotating-equipment health monitoring (vibration, current, acoustics, temperature, SCADA)
2. Water-network digital twins and pipe failure prediction
3. Treatment-plant AI operations and asset intelligence
4. Sensorless predictive maintenance using existing data

Here are notable companies to evaluate:

If your priority is pumps

Shortlist:

• Aquatis — strong pump-condition-monitoring orientation.
• Groundup.ai — water-utility-specific AI maintenance approach.
• iFactory — SCADA-first approach for pumps, blowers, and treatment equipment.
• 2Neuron — interesting if you want minimal new hardware.

If your priority is pipes

Look more at:

• AquaTwinX — pipe deterioration, failure probability, consequence modeling.
• Utilimatics — network-level analytics and anomaly detection.
• XMPro — aging-pipe risk and predictive maintenance workflows.

If your priority is treatment plants

Strong candidates:

• Aquasight — plant optimization + predictive maintenance + digital twins.
• Avertis — treatment asset maintenance intelligence.
• iFactory — pump/blower/process equipment prediction using existing plant data.

Evaluation criteria I would use

For a utility or industrial operator, I would ask vendors:

• Data sources: Can it use existing SCADA/PLC historians, CMMS, vibration systems, and work orders?
• Failure modes covered: Bearing wear, cavitation, seal failure, impeller degradation, clogging, valve issues?
• Lead time: Days, weeks, or months before failure?
• Explainability: Does it tell operators why an asset is failing, not just give an anomaly score?
• Integration: Can it create work orders in systems like IBM Maximo or SAP?
• Cybersecurity: Can it run inside utility networks or on-prem?

For a large municipal water utility, I would probably run a pilot comparing:

1. a pump-specialist platform (Aquatis / Groundup.ai / iFactory),
2. a network digital-twin platform (Aquasight / AquaTwinX / Utilimatics),
3. a sensorless approach (2Neuron),

using 20–50 critical assets and measuring avoided failures, maintenance-hour reduction, and false alarms.

Predictive maintenance for water and wastewater infrastructure requires specialized technology capable of handling submerged equipment, harsh chemical environments, and miles of buried assets. Several innovative scale-ups and platforms specialize in these exact vectors (pumps, pipes, and treatment facilities):

1. Pumps & Rotating Equipment (Motors, Submerged Pumps)

• Samotics : A premier player utilizing Electrical Signature Analysis (ESA). Instead of placing delicate vibration sensors directly on submerged or hard-to-reach wastewater pumps, Samotics installs monitoring units inside the motor control cabinet (MCC). It analyzes high-frequency current and voltage data to detect cavitation, blockages, bearing wear, and rotor faults before failure happens.
• Sasken : Provides a sensorless, AI-powered predictive maintenance framework targeting large fleets of industrial pumps and motors. By ingesting electrical current signatures from existing variable frequency drives or control systems, their machine learning models grade component-level health (such as stator or impeller degradation) without needing hardware mounted directly onto the machinery.

2. Pipes & Underground Networks (Leaks, Corrosion, Breaks)

• VODA.ai : Focuses heavily on water pipe risk analytics. They use machine learning and artificial intelligence paired with historical utility data to predict water main breaks and assess pipe life expectancy. Their predictive models help municipal engineers prioritize which subterranean pipes to replace before catastrophic bursts occur.
• Fracta : Uses AI and machine learning to analyze data about aging water distribution mains (soil conditions, pipe material, pressure, break history) to predict the likelihood and location of pipe failures without requiring intrusive physical acoustic testing on every street.
• SmartTerra : An emerging urban water tech startup that applies AI, geospatial data, and hydraulic modeling to pinpoint hidden leaks, pipe bursts, and faulty meters across city distribution grids.

3. Treatment Facilities & Holistic Networks (Digital Twins & Process Optimization)

• TaKaDu : Provides a centralized software-as-a-service (SaaS) solution for water utilities that integrates data from SCADA systems, flow meters, and GIS. Using big data analytics and machine learning, TaKaDu's platform detects anomalies in treatment plants and distribution networks—identifying leaks, asset failures, operational faults, and data issues in real-time.
• Savantic : Partners with industrial heavyweights like Xylem to build intelligent pump stations and digital twin models. They deploy real-time monitoring and predictive analytics to simulate complex treatment plant or network scenarios, optimizing energy consumption and preempting equipment breakdowns.

To narrow down which provider matches your exact setup, could you tell me:

• Are your biggest pain points submerged wastewater pumps, buried aging water pipes , or the treatment plant process machinery?
• Do you already have a SCADA system or sensor infrastructure installed?

A specialized group of AI and industrial IoT (IIoT) startups focus on predictive maintenance, digital twins, and condition monitoring for water/wastewater pumps, transmission pipes, and treatment facilities.

Pipe Network Predictive Maintenance

• VODA.ai : A Boston-based AI platform that helps water utilities predict water main and pipe failures, assess break risks, and prioritize capital replacement planning. It evaluates pipe-level attributes, soil types, and historical work orders.
• Fracta : Utilizes machine learning and artificial intelligence to evaluate the Likelihood of Failure (LOF) and Cost of Failure (COF) for drinking water pipelines. It grades water mains without needing physical acoustic sensors or intrusive digs.
• Pipelink / Pressure Monitoring Vendors : Companies like Piggots / InfoWorks or transient pressure monitoring specialists deploy edge sensors to spot pressure anomalies, forecasting pipe bursts days ahead of time.

Pump and Treatment Facility Monitoring

• Idrica : An international startup/scale-up offering GoAigua , a data-driven interoperability and analytics platform that integrates SCADA data to provide predictive maintenance and operational optimization for treatment plants and pumping stations.
• Emaint / Augury / SparkCognition (Industrial AI) : While broader industrial players rather than water-exclusive startups, platforms like Augury use continuous vibration and magnetic sensors paired with AI diagnostics to detect pump bearing, impeller, and motor anomalies long before failure.
• GE Digital / Proficy (Industrial Analytics) : Often deployed via software modules (like Proficy CSense ) to model pump efficiency curves and predict mechanical pump failures weeks in advance.

If you're narrowing down a vendor selection, tell me:

• Are you prioritizing pipes (underground mains) or above-ground mechanical assets (pumps/blowers)?
• Do you already have an existing SCADA or GIS historian deployed?

I can tailor a short list of options that integrate best with your current tech stack.

Yes. There’s a fairly active startup ecosystem here, but it splits into three distinct problems: rotating equipment, buried pipe/network failures, and treatment-plant/process assets.

Best-fit startups

If I were building a shortlist

I'd put these five at the top for an initial competitive/partner landscape:

1. Aquasight — best candidate if you want one platform spanning pumps + networks + treatment plants. Its ATLAS, ACE, AURA, APOLLO and AMP products cover a surprisingly broad portion of the water utility lifecycle.
2. Groundup.ai — strongest fit if the core problem is predictive maintenance of physical equipment, especially pumps, motors and blowers, with diagnosis rather than just anomaly alerts.
3. Aquatis — particularly interesting for pump fleets and rotating machinery, with its own sensor + software approach.
4. VODA.ai — strong choice for the buried infrastructure / pipe-risk side, where traditional vibration-based predictive maintenance doesn't work.
5. Avertis — interesting if the goal is to connect condition monitoring → diagnosis → maintenance workflow, rather than merely produce predictions.

One important distinction

I wouldn't treat "predictive maintenance for water" as a single market.

Pumps/blowers: vibration, motor current, temperature, pressure, flow → bearing degradation, cavitation, impeller problems, seal failure, etc.

Pipes/mains: GIS + break history + soil/material/age + pressure/hydraulic behavior → probability of failure, leakage and blockage.

Treatment plants: SCADA/process data + equipment condition → pump/blower/clarifier/chemical-feed failures plus process optimization and compliance risk.

That's why VODA.ai and Groundup.ai, for example, are complementary rather than direct substitutes.

If your objective is to identify the best startup to partner with or acquire, I can also map ~20 companies by technology, funding/size, customers, sensor requirements, SCADA integration, water/wastewater focus, and maturity, and identify the 3–5 most defensible players.

Yes. The market is getting quite interesting, but I’d split it into three distinct predictive-maintenance problems: rotating equipment, buried pipes/networks, and whole treatment-plant operations.

Startups I’d put on the shortlist

How I'd segment the opportunity

1. Pumps & rotating equipment — strongest startup ecosystem

This is probably the most mature predictive-maintenance use case. Aquatis, Groundup.ai, Artesis, 2Neuron and Aquasight are particularly relevant.

Aquatis is unusually water-specific: its system combines wireless sensors with software that diagnoses pump problems and recommends maintenance. Its stated market includes utilities, desalination and wastewater treatment.

Artesis takes a different approach: it analyzes electrical signals rather than requiring sensors on the machine, which could be a major advantage for utilities with hundreds of pumps.

2. Pipes / buried infrastructure — VODA.ai is the standout

VODA.ai is solving a different problem: "Which pipe is going to fail next?" rather than "Is this pump vibrating abnormally?"

Its models use pipe attributes, failure history and other utility data to rank pipes by likelihood of failure and business risk. The company says it has analyzed more than 1 million miles of pipe and is deployed across 31 U.S. states.

That makes VODA particularly compelling for a utility trying to prioritize main replacement, rehabilitation, leak detection and condition assessment rather than equipment maintenance.

3. Treatment facilities — Aquasight / Avertis / Groundup

Aquasight is probably the most comprehensive here. Its platform covers pump health, collection networks, water networks, treatment optimization and asset-performance/capital planning. Its APOLLO product specifically incorporates predictive maintenance into treatment-plant operations.

Avertis is more maintenance-centric: it connects asset condition, maintenance history and engineering knowledge across pumps, blowers, valves, screens, mixers and dosing systems.

One important distinction

I'd not put all of these companies in the same competitive bucket.

There are really two architectures emerging:

Equipment-centric

Sensors/electrical signals → anomaly detection → failure mode → maintenance action Examples: Aquatis, Artesis, Groundup.ai, 2Neuron

Infrastructure-centric

GIS + SCADA + CMMS + failure history + operational data → asset risk → intervention/replacement decision Examples: VODA.ai, Aquasight, AquaTwinX

The second category can potentially become much more valuable because it connects maintenance with capital planning. VODA, for example, explicitly turns pipe-risk rankings into replacement, rehabilitation and inspection programs.

If I were evaluating this market

My first 5 demos would be:

1. Aquasight — broadest end-to-end water utility proposition.
2. Aquatis — strongest specialized pump/rotating-equipment play.
3. VODA.ai — strongest pipe-risk/predictive infrastructure play.
4. Artesis — test the sensorless approach against sensor-based competitors.
5. Groundup.ai — evaluate how deep its water-specific AI diagnostics actually go.

And there's an interesting white-space opportunity: a single predictive-maintenance platform that covers pumps + buried pipes + treatment-process equipment and then automatically converts predictions into CMMS work orders and capital-prioritization decisions. Most startups today appear to own only one or two pieces of that stack.

If you're looking at this as a startup/investment opportunity, I can also map these ~10 companies by funding, customers, technology, deployment model, pricing, moat, and which ones are closest to becoming the "Palantir for water infrastructure."

This is probably the most mature predictive-maintenance use case. Aquatis, Groundup.ai, Artesis, 2Neuron and Aquasight are particularly relevant.

Aquatis is unusually water-specific: its system combines wireless sensors with software that diagnoses pump problems and recommends maintenance. Its stated market includes utilities, desalination and wastewater treatment.

Artesis takes a different approach: it analyzes electrical signals rather than requiring sensors on the machine, which could be a major advantage for utilities with hundreds of pumps.

2. Pipes / buried infrastructure — VODA.ai is the standout

VODA.ai is solving a different problem: "Which pipe is going to fail next?" rather than "Is this pump vibrating abnormally?"

Its models use pipe attributes, failure history and other utility data to rank pipes by likelihood of failure and business risk. The company says it has analyzed more than 1 million miles of pipe and is deployed across 31 U.S. states.

That makes VODA particularly compelling for a utility trying to prioritize main replacement, rehabilitation, leak detection and condition assessment rather than equipment maintenance.

3. Treatment facilities — Aquasight / Avertis / Groundup

Aquasight is probably the most comprehensive here. Its platform covers pump health, collection networks, water networks, treatment optimization and asset-performance/capital planning. Its APOLLO product specifically incorporates predictive maintenance into treatment-plant operations.

Avertis is more maintenance-centric: it connects asset condition, maintenance history and engineering knowledge across pumps, blowers, valves, screens, mixers and dosing systems.

One important distinction

I'd not put all of these companies in the same competitive bucket.

There are really two architectures emerging:

Equipment-centric

Sensors/electrical signals → anomaly detection → failure mode → maintenance action Examples: Aquatis, Artesis, Groundup.ai, 2Neuron

Infrastructure-centric

GIS + SCADA + CMMS + failure history + operational data → asset risk → intervention/replacement decision Examples: VODA.ai, Aquasight, AquaTwinX

The second category can potentially become much more valuable because it connects maintenance with capital planning. VODA, for example, explicitly turns pipe-risk rankings into replacement, rehabilitation and inspection programs.

If I were evaluating this market

Predictive maintenance for water and wastewater infrastructure (pumps, subsurface/pressurized pipes, and treatment facilities) relies on a mix of IoT hardware, electrical signature analysis, and AI risk-modeling software.

The key players and startups solving these specific challenges span different layers of the infrastructure:

1. Samotics (Pumps & Pressurized Pipes)
   • What they do: Specializes in continuous condition monitoring for hard-to-reach or submerged assets (like sewage and lift-station pumps) without placing physical sensors in the wet well.
   • The tech: Uses Electrical Signature Analysis (ESA) from the Motor Control Cabinet (MCC) to detect blockages, ragging, and efficiency loss. They also expanded into subsurface rising main burst detection for pressurized wastewater pipes.
   • Explore further: Learn more about their approach on the Samotics Water & Wastewater Page.
2. Augury (Treatment Facilities & Critical Pumps)
   • What they do: A prominent player in industrial machine health, providing diagnostic solutions tailored to critical rotating equipment in wastewater treatment plants and dry-pit pump stations.
   • The tech: Utilizes a combination of proprietary wireless vibration and temperature sensors alongside AI diagnostics and human reliability experts to flag mechanical wear, cavitation, and imbalance early.
   • Explore further: Review their use cases via the Augury Wastewater Facility Guide.
3. VODA.ai (Pipes & Underground Networks)
   • What they do: Focuses purely on underground risk prediction rather than mechanical vibration.
   • The tech: Uses an in-house AI engine (daVinci) that ingests utility records, soil data, and external factors to predict water and wastewater main breaks and pipe failures anywhere from 12 months to 20 years in advance.
   • Explore further: Check out their platform details at VODA.ai.
4. Nanoprecise Sci Corp (Pumps & Rotating Assets)
   • What they do: Offers automated AI-based predictive maintenance focused on high-efficiency energy and mechanical health tracking.
   • The tech: Combines IIoT hardware with low-power wireless vibration and acoustic sensors to link mechanical degradation of pumps directly to energy consumption anomalies and ESG metrics.

To help narrow down which of these fits your exact setup, let me know:

• Are your biggest pain points submerged sewage pumps, long transmission/distribution pipes , or equipment inside the treatment plant?
• Do you prefer sensorless electrical monitoring (from the control cabinet) or direct vibration sensors mounted on the hardware?