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

Addressing predictive maintenance (PdM) across the water and wastewater sector—covering rotating machinery like pumps , linear infrastructure like pipes , and complex operational environments like treatment facilities —typically involves a mix of specialized water-tech innovators and broader industrial AIoT platforms.

The market features several prominent startups and scale-ups targeting these exact assets:

1. For Pumps & Rotating Equipment (Vibration & Electrical Health)

• • Samotics : Highly specialized in Samotics using Electrical Signature Analysis (ESA). Instead of mounting delicate physical sensors onto submerged or hard-to-reach water/sewage pumps, they analyze electrical current and voltage signals to predict mechanical and electrical failures.
• • Augury : A heavy-hitter in machine health and diagnostics. They combine continuous vibration and ultrasonic monitoring with AI diagnostics to predict mechanical failures in large industrial water and wastewater pumps before catastrophic breakdown.
• • Nanoprecise Sci Corp : Focuses on automated end-to-end predictive maintenance for industrial machinery. They specialize in high-precision wireless vibration and energy consumption sensors that link equipment mechanical health directly to energy efficiency and ESG metrics.

2. For Pipes & Distribution Networks (Leak & Break Prediction)

• • SmartTerra : An AI-driven startup that uses hydraulic, geospatial, and sensor data to pinpoint water losses, identify leaky pipes, and detect faulty meters within urban municipal distribution networks.
• • Weplat / Nell Systems ( originally incubated via K-water) : Utilizes IoT acoustic and pressure sensors combined with AI models to continuously analyze nighttime minimum pressure drops and acoustic frequencies to locate pre-existing and emerging pipeline leaks.
• • SaaS/Platform approach : Many utilities also leverage broader geospatial AI mapping and acoustic correlation platforms like Kando (which focuses more on wastewater quality/effluent tracking in pipes) or Gutintegrity for pipeline integrity grading.

3. For Treatment Facilities (Process Optimization & Asset Health)

• • [ LEOM / Advanced Water AI platforms ] : Emerging smart-water layers use deep learning and reinforcement learning models to ingest real-time data from treatment plants. They evaluate basin chemistry, blower performance, and filtration metrics to predict anomalies and autonomously command valves, blowers, and treatment pumps.
• • [Fracta / Xylem Vue ecosystem] : While Fractal originally scaled as an independent AI assessment startup for predicting pipe failure risks (now part of broader water tech leaders like Xylem), enterprise utility software like OSIsoft/AVEVA or dedicated water accelerators are widely deployed to handle facility-wide predictive maintenance KPIs (Mean Time Before Failure, energy deviations, chemical dosing anomalies).

If you'd like to narrow this down, tell me:

• Are you looking specifically for municipal water utilities or industrial wastewater plants?
• Do you prefer sensor-embedded hardware or non-invasive/electrical monitoring?

I can help tailor this list to your exact deployment environment.

Yes. The market is getting fairly interesting, but it’s fragmented: some startups focus on rotating equipment/pumps, others on buried pipes, and a smaller group is trying to cover the whole water/wastewater asset lifecycle.

Best-fit startups to investigate

If I were evaluating the market

I'd divide the opportunity into three layers:

1. Pumps / rotating equipment → Groundup.ai, Aquatis, Avertis

This is the most conventional predictive-maintenance problem: vibration, motor current, temperature, flow, pressure, pump curves, operating history, etc. The goal is essentially "this pump is degrading; here's why and when/how to intervene." Groundup and Aquatis are particularly focused on this water-utility use case.

2. Pipes / buried infrastructure → VODA.ai, Foresight AI, SewerAI

Here you generally can't put a vibration sensor on the asset. Prediction comes from pipe age/material, break history, soil/environment, pressure, inspections, hydraulics, CCTV and other GIS/utility data. VODA.ai, for example, explicitly predicts pipe failure risk to prioritize inspection and replacement.

3. Treatment facilities / whole-system intelligence → Aquasight, AquaTwinX, Avertis, Ensaras

This is where things get especially interesting. Rather than predicting one component failure, these platforms attempt to understand the plant as a system—SCADA + asset data + process models + maintenance history—and recommend what operators should do. Aquasight's platform, for example, covers pumps, collection systems, water networks and treatment plants.

The startup I'd watch most closely

If your requirement is literally:

"Predict failures across pumps + pipes + treatment facilities and turn those predictions into maintenance decisions." I'd put AquaTwinX and Aquasight near the top of the diligence list because they're attempting the broader asset-intelligence problem rather than solving only pump monitoring or only pipe inspection. AquaTwinX explicitly describes models for both horizontal assets (mains, sewers, force mains) and vertical assets (pumps, motors, treatment units, electrical/structural systems).

If you're primarily concerned with pump failures and unplanned downtime, I'd instead start with Groundup.ai, Aquatis, and Avertis.

If you're primarily concerned with water-main/sewer failures and capital planning, start with VODA.ai, Foresight AI, and SewerAI.

There is also an interesting emerging category around AI + autonomous inspection: RAVAM, for example, combines facility inspection, visual/thermal monitoring, equipment-health monitoring and predictive maintenance.

If you're evaluating this as a startup/investment opportunity, I can also map these companies by funding, customers, geography, estimated size, technology (sensors vs. software), and competitive whitespace—which would make it much easier to see where the market is still underserved.

Predictive maintenance for water and wastewater infrastructure—spanning high-intensity pumps, underground pipe networks, and treatment plants—relies on IoT telemetry, acoustic monitoring, and machine learning models to forecast failures before they trigger catastrophic outages.

Several specialized startups and scale-ups address these exact assets:

• Groundup.ai Focuses heavily on heavy industrial motors and water utility pumps. They deploy non-invasive sensors (vibration, acoustic, temperature) that sync with existing SCADA systems to profile individual machine baselines, flagging issues like bearing spalling, seal leaks, or cavitation weeks in advance.
• Ainwater Specializes in treatment facilities and process control. Through their Poseidon platform, they leverage machine learning and time-series forecasting to predict operational anomalies, optimize chemical dosing, and transition water treatment plants into predictive, actionable environments.
• Kando Concentrates on wastewater networks and treatment plants. They use real-time data tracking and IoT endpoints within municipal pipelines to monitor quality, flow anomalies, and network health, helping operators predict blockages and pollution events before they impact treatment streams.
• Fracta Specializes in aging drinking water pipe networks. Instead of physical inline tools for every mile of pipe, Fracta uses machine learning, geospatial data, soil conditions, pipe material, and historical break records to calculate the failure risk and remaining useful life of individual pipe segments.
• UptimeAI Backed by major industrial conglomerates like ABB, this AI-driven predictive maintenance platform targets complex industrial equipment (including large-scale pumps and rotating assets) by combining deep machine learning with built-in engineering physics to automate root-cause analysis.

If you'd like to narrow this down, let me know:

• Are you looking primarily for software that plugs into your current SCADA/Historian , or do you need hardware/sensor deployment too?
• Are you focused more on clean water distribution or wastewater treatment?

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

Best-fit startups

If I were building a competitive landscape

I'd divide the market like this:

1. Pump/rotating-equipment PdM

• Aquatis
• Groundup.ai
• Avertis
• General industrial PdM companies such as Dynamox, which monitors pumps and claims detection of 25+ failure modes.

2. Pipe/mains failure prediction

• Foresight AI
• VODA.ai
• Utilimatics

3. Treatment-plant intelligence

• Aquasight
• Avertis
• Gradiant
• Hydromantis/Mantis.AI, which combines plant digital twins with collection, pumping and distribution models and can extend into asset predictive maintenance.

The interesting whitespace

The market still looks fragmented. Most companies specialize in one layer:

sensor → asset health → failure prediction → work order or:

SCADA/GIS → network model → pipe failure risk Few startups appear to cover the complete chain across pumps + pipes + treatment assets, while also connecting the prediction directly to maintenance scheduling, parts, crews and operational consequences.

That's where I would look if you're evaluating startups for a utility or thinking about building something yourself.

My initial shortlist to diligence: Groundup.ai, Aquatis, Aquasight, Foresight AI, VODA.ai, and Avertis.

Yes. I’d break the market into three distinct predictive-maintenance problems—because the startups tend to specialize rather than cover the whole water stack.

My shortlist

If you're looking for companies to benchmark, partner with, invest in, or potentially acquire, I'd start with:

1. Aquasight — broadest platform
Probably the closest match to your entire requirement. It has separate capabilities for pump health, collection networks, water networks and treatment plants rather than just monitoring a single asset class.

2. Aquatis — best specialist for pumps
Interesting if the core problem is “tell me which pump is going to fail and why.” Their approach combines wireless condition sensors with diagnostic/recommendation software.

3. Foresight AI — best fit for pipes
Its products explicitly address pipe failure probability, leakage investigations and critical-main integrity. That's a different problem from pump predictive maintenance and worth treating as its own category.

4. VODA.ai — strong asset-risk / capital-planning angle
More focused on which pipes are likely to fail and where should the utility spend money? That's valuable if you're trying to connect predictive maintenance with asset-management decisions.

5. SenthiSYS / genSense — interesting equipment-monitoring plays
Both are particularly relevant if you want sensor-driven monitoring of distributed pumps, motors, blowers and other rotating equipment rather than a large enterprise digital-twin platform.

One important distinction

There are really two different predictive-maintenance architectures emerging:

A. Asset-level prognostics

vibration + temperature + current + pressure → detect degradation → predict failure → create work order

Aquatis, SenthiSYS, genSense and Groundup.ai are closest to this.

B. System-level digital twins

SCADA + GIS + AMI + hydraulic/process models → understand system behavior → detect anomalies → predict failures/overflows/inefficiencies → optimize maintenance

Aquasight, Foresight AI, VODA.ai and Utilimatics are closer to this.

If your goal is a single platform covering pumps + pipes + treatment plants, I'd investigate Aquasight first, then look at whether specialist vendors such as Aquatis/genSense can fill gaps.

If you tell me whether this is for a municipal water utility, wastewater utility, industrial water operator, or a company building a new product, I can also map ~20 startups by asset type, funding/stage, customers, geography, and competitive differentiation.

Here are top web results for exploring this topic:

f7i.ai·https://f7i.ai/blog/predictive-maintenance-startups-in-2026-which-platform-actually-solves-downtime Top 5 Predictive Maintenance Startups for 2026: Comparison 3. Can I use predictive maintenance on old (brownfield) machines? Yes. In 2026, you no longer need "smart" machines to have a smart plant. Startups like Factory AI use external sensors or "Edge" devic StartUs Insights·https://www.startus-insights.com Discover 5 Top Predictive Maintenance Startups - StartUs Insights Out of 976, the Global Startup Heat Map highlights 5 Top Predictive Maintenance Startups. The insights of this data-driven analysis are derived from the Big Data & Artificial Intelligence (AI)-powered Oxmaint·https://oxmaint.com AI Predictive Maintenance for Municipal Water Pump Stations AI Predictive Maintenance for Municipal Water Pump Stations. By James Smith on May 19, 2026. ai-predictive-maintenance-for-municipal-water-pump-stations. Municipal water pump stations operate 24/7 und iFactory AI·https://ifactoryapp.com The Role of Predictive Maintenance in Water Utilities - iFactory AI How Predictive Maintenance Ensures Reliable Water Service Delivery. From ML-driven pump failure prediction to distribution network digital twins and real-time water quality anomaly detection — iFactor Augury·https://www.augury.com 7 Top Predictive Maintenance Solutions and Their Applications I-care, Condition monitoring platform with hardware, software, and expert services, EMEA-based enterprise programs, Condition monitoring and periodic vibration analysis. Tractian, All-in-one condition IoT Analytics·https://iot-analytics.com Ranking: Top 20 Companies Enabling Predictive Maintenance The Top 5 Predictive Maintenance Companies. 1. IBM. IBM made #1 in the ranking due to its large workforce that indicates it works on the topic and due to the wide media coverage the firm gets. IBM's P Einnosys·https://www.einnosys.com**Predictive Maintenance** for Pumps : The Future of Industrial Reliability It's a costly game of catch-up, where every unexpected shutdown chips away at profitability and production schedules. At the center of this seismic shift sits predictive maintenance for pumps—a fundam XMPro·https://xmpro.com**Pump** Health Monitoring | XMPro ... CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DE coastapp.com·https://coastapp.com/blog/predictive-maintenance-software/6 Best Predictive Maintenance Software for 2026 (In-Depth Review)Predictive maintenance software helps solve this by using real-time condition data, historical maintenance records and sensor inputs to predict failures before they happen. Instead of guessing when as Tracxn·https://tracxn.com Top Companies in Predictive Machine Maintenance (Aug, 2026)Top Predictive Machine Maintenance startups. There are 497 Predictive Machine Maintenance startups which include Augury, Uptake, TRACTIAN, Infinite Uptime, Uptime AI. Out of these, 168 startups are fu

Yes. There’s a fairly strong startup ecosystem here, but it splits into three different predictive-maintenance problems: rotating equipment, pipe/network failure, and treatment-plant operations.

The ones I'd investigate first

If you're evaluating this as a real procurement/venture landscape, I'd narrow it to:

1. Aquasight — platform play
Best candidate to investigate if the requirement is pumps + pipes + treatment facilities rather than just one asset class. It positions itself across treatment plants and pipelines and emphasizes integrating with existing infrastructure.

2. Aquatis — pump-specific predictive maintenance
Worth a serious look if pump failures are the biggest pain point. Its approach is sensor-driven and specifically built around rotating equipment.

3. VODA.ai — pipe failure prediction
I'd put this high on the list for a water utility with an aging distribution/transmission network. It focuses specifically on predicting failures and prioritizing intervention.

4. Foresight AI — network intelligence
Especially interesting if you're trying to move from individual leak detection toward risk-based asset management of an entire pipe network.

5. SenthiSYS — plant + equipment digital twin
Worth evaluating if treatment facilities are a major part of the requirement and you want maintenance information tied to a digital representation of the plant.

One important distinction

I'd separate these into:

• Predictive maintenance of equipment: Aquatis, SenthiSYS, iFactory, GATA
• Predictive failure of buried infrastructure: VODA.ai, Foresight AI, Aganova
• Utility-wide operational intelligence: Aquasight, Utilimatics
• Leak detection: there are additional companies such as AquaTrakr, which focuses more on detecting customer-side leaks from AMI rather than maintaining utility assets.

Also, there are large incumbents competing in this space—Flowserve's RedRaven, for example, predicts problems in pumps, seals and valves—so I'd explicitly distinguish "startup" from "best technology."

If you're looking at this from an investment, partnership, or buying-for-a-water-utility perspective, I can also build a 20–30 company landscape with columns for asset type, AI/ML approach, sensors required, SCADA integration, customer base, funding/stage, geography, and likely competitors.

Predictive maintenance for water and wastewater infrastructure requires a blend of solutions because pumps (rotating mechanical assets) and pipes (buried linear assets) fail for entirely different reasons.

Several specialized startups focus on these specific infrastructure challenges:

• Samotics : Specializes in predictive maintenance for submerged and harsh-environment equipment like wastewater pumps, mixers, and lift-station machinery. Instead of placing delicate physical sensors in wastewater streams, their SAM4 platform uses Electrical Signature Analysis (ESA) inside low-voltage motor control cabinets to monitor current and voltage 24/7. It detects mechanical stress, cavitation, and bearing wear months in advance.
• VODA.ai : Focuses heavily on the underground side of the equation—water and wastewater pipes. Their machine learning platform ingests historical break records, pipe material, soil conditions, and operational pressure data to rank buried pipe segments by risk of failure. This allows utilities to transition from age-based replacement schedules to targeted, data-driven capital planning.
• AssetWatch : Provides end-to-end condition monitoring tailored for treatment plants and pump stations. They deploy wireless vibration and temperature sensors on critical above-ground rotating assets (like blowers, large motors, and process pumps) and combine real-time AI anomaly detection with validation from certified reliability engineers.
• Groundup.ai : Offers an agentic AI platform for cognitive maintenance geared toward heavy industrial and utility assets. They deploy non-invasive acoustic and vibration sensors that integrate with existing plant SCADA systems to track the individual health baselines of critical pumps and motors without requiring system shutdowns or trenching.
• VAPAR : Automates condition assessment for underground pipes and stormwater networks using deep learning. It processes CCTV inspection footage to detect and score pipe defects in minutes rather than weeks, helping teams prioritize structural rehabilitation.

To help narrow down which approach fits your operation best, let me know:

• Are you currently struggling more with above-ground plant equipment (pumps/blowers) or buried infrastructure (pipes/mains)?
• Do you already have a centralized data historian or SCADA system in place?

Managing the health of pumps, pipes, and treatment facilities requires a mix of rotating equipment monitoring, structural pipe analytics, and process optimization. Several specialized startups and scale-ups focus directly on these components:

• Aquasight Provides AI-driven digital twin solutions tailored for municipal water systems. Their platforms (such as ATLAS for pump health, ACE for collection networks, and APOLLO for treatment optimization) use real-time operational telemetry to predict equipment failure, forecast flows, and minimize compliance risks.
• Samotics Specializes in predictive maintenance for submerged and hard-to-reach industrial assets like water pumps, mixers, and centrifuges. Their proprietary SAM4 system uses Electrical Signature Analysis (ESA) via low-voltage motor control cabinets rather than mounting delicate sensors directly on harsh or submerged equipment.
• Voda.ai Focuses squarely on water distribution networks and pipe infrastructure. Their machine learning platform ingests physical asset data, soil conditions, history, and pressure telemetry to calculate failure probabilities and rank pipe segments by risk, allowing utilities to prioritize replacements before a main break happens.
• SewerAI Delivers an artificial intelligence platform built for sewer and wastewater pipe inspection. By processing high-volume field data and inspection camera footage, their software automatically detects, categorizes, and maps internal structural defects to streamline rehabilitation planning.
• AssetWatch Offers condition-based and predictive maintenance platforms specifically tracking critical vertical assets like blowers, filters, and pumps in water and wastewater treatment facilities to eliminate unplanned downtime.
• TRACTIAN Provides advanced AI-powered online asset monitoring and automated diagnostic toolsets combining vibration and energy data, widely deployed for industrial rotation machinery and heavy pumping infrastructure.

If you can share whether you are looking for municipal utilities or industrial plant scale , and if you prefer hardware-based sensors or software-only analytics , I can help narrow down which vendor fits your exact architecture.

Yes. There’s a surprisingly active startup ecosystem here, but it breaks into three distinct layers: rotating equipment (pumps/blowers), buried pipe/network assets, and whole treatment-plant operations.

Strongest startups to look at

The ones I'd investigate first

If you're looking for a startup partner rather than just a generic predictive-maintenance vendor, I'd narrow it to:

1. Aquatis — pumps
This is probably the closest match to "predict when our pumps are going to fail." Its sensors attach to rotating equipment and its software diagnoses equipment health and recommends interventions.

2. VODA.ai — pipes
Very different problem: rather than putting sensors on every pipe, it uses utility data/ML to estimate failure risk for buried infrastructure and prioritize what should be investigated or replaced.

3. Aquasight — treatment plants
If your goal is a plant-wide predictive layer, this is particularly interesting. Its APOLLO platform combines digital-twin capabilities with operational optimization, troubleshooting and predictive maintenance.

4. Flowless — utility-wide operations
Worth looking at if you want to connect distribution, pumps and facilities rather than buying separate point solutions. It combines leak/NRW management with flow, pressure and pump-status analytics.

5. genSense / Dynamox — equipment condition monitoring
These are more focused on the actual machinery: vibration, temperature, mechanical degradation, bearings, etc.

One important distinction

I'd avoid treating "predictive maintenance" as one market.

For a water/wastewater operator, I'd map the stack like this:

Physical asset → sensing → anomaly detection → failure prediction → work-order recommendation

• Pumps/blowers: vibration, temperature, motor current, pressure → Aquatis / genSense / Dynamox
• Pipes/mains: pressure, flow, GIS, age, break history, soil/environmental factors → VODA.ai / Utilimatics / Flowless
• Treatment processes: SCADA + process data + equipment condition → Aquasight
• Cross-system intelligence: SCADA + GIS + CMMS + IoT → Zelta / Flowless / Aquasight

There are also larger incumbents entering aggressively. For example, Flowserve's RedRaven predicts performance of pumps, seals and valves, while SUEZ offers ML-based predictive asset management for pipes and wastewater assets.

If you're evaluating this as a startup/market opportunity, I think the particularly interesting gap is a single predictive-maintenance platform that ingests existing SCADA/CMMS data and combines pump health + pipe failure risk + treatment-process degradation, rather than requiring utilities to deploy a separate point solution for each asset class.

If you tell me whether you're targeting municipal water utilities, wastewater utilities, industrial plants, or a specific treatment process, I can narrow this to ~10 companies and compare funding, customers, deployment model, sensors required, AI capability, and competitive differentiation.