Augury is the world's leading industrial AI platform, focusing on predictive maintenance and production health. Continuously monitor 200+ types of rotating equipment through vibration sensors + AI diagnosis, provide early warning of machine failures and provide repair suggestions. Industrial AI Workforce (industrial AI agent) will be launched in 2025, covering full-link automation from detection to decision-making. Serving 500+ manufacturers worldwide including Nestlé, DuPont, Shell, Colgate-Palmolive, PepsiCo and more. Forrester TEI reports show customers achieve payback within 6 months, 310% ROI.

Augury: Let every industrial equipment “speak”

Core parameters and statistics

Properties	Values
Product Positioning	Industrial AI Predictive Maintenance and Production Health Platform
Core Products	Machine Health (equipment health), Production Health (production health), Industrial AI Workforce (industrial AI intelligence)
Monitored Asset Types	200+ categories of rotating equipment (pumps, motors, compressors, fans, gearboxes, coolers, conveyors, extruders, grinders, mixers, etc.)
monitoring data volume	1.1 billion+ hours of real machine operating data
Number of monitored devices	300,000+ units
Number of customers served	500+ manufacturers (170+ direct contract customers)
Countries Covered	40+ Countries
Customer Industry	Food and beverage, chemical industry, oil and gas, building materials, papermaking, forest products, consumer goods, mining and other 10+ industries
Certification Compliance	ISO 27001:2022, ISO 9001:2015, GDPR, CCPA, IECEx/ISO 80079-34
Deployment Method	SaaS Cloud Platform + Edge IoT Sensor
Payback	<6 months (Forrester TEI study)
Customer ROI	310% (Forrester TEI Research)
Gartner Peer Insights	4.9 / 5.0 (13 reviews)

What the data means: 1.1 billion hours of machine vibration data is Augury’s core competitive barrier—the accuracy of industrial AI models is highly dependent on the accumulation of real data over a long period of time and under multiple working conditions. Even if new entrants have advanced algorithms, it will take several years to catch up with this data flywheel. The 300,000+ devices on the network also constitute a network effect: every time a new device is added, the diagnostic model will have one more training material.

User and market recognition

Enterprise-level customer matrix: Augury’s customer list covers the world’s top industrial groups, including Nestlé, DuPont, Shell, Colgate-Palmolive, PepsiCo, Heineken, Danone, The Hershey Company (Hershey), ICL (Israel Chemical), Osem-Nestlé, Circulus, Fortune Brands Innovations, and others. The diverse distribution of customers across industries and regions verifies the versatility of the platform in various industrial scenarios.

Analyst Approval:

• Forrester Total Economic Impact™ Study: Customers achieve 310% ROI, payback within 6 months, 7x ROI (DuPont proven), annualized savings of millions of dollars in unplanned downtime.
• Gartner Peer Insights: Rated 4.9/5.0 (as of July 15, 2026), with high user satisfaction in the Application Portfolio Management Tools category.
• Industry Honor: Selected into Forbes AI 50 and World Economic Forum Technology Pioneer IBT Top AI Company.

Ecology and Integration: Augury is deeply integrated with MaintainX (work order management platform) to realize the closure of "AI early warning → automatic creation of work orders"; it is connected with mainstream CMMS/EAM systems without disrupting existing operation and maintenance workflows.

Cost advantage

Augury does not provide public pricing, and all plans require business communication quotations. However, its three-tier cost structure can be deduced from public data:

C-side/Personal (not applicable): Augury is a purely enterprise-level product, with no personal or free version. For independent maintenance engineers, the lowest threshold is to promote procurement at the factory where they work.

Developer/Integrator (Indirect): Augury provides APIs and the ability to interface with CMMS systems, but there is no public self-service pricing for developers. Integration costs are mainly reflected in the docking development during the implementation phase (usually 3-6 months), and Forrester reports that the average time from deployment to measurable business impact is about 7 weeks.

Enterprise/Private (Core Model): Paid as an annual subscription and includes:

• Hardware cost: Industrial grade vibration sensor (different levels correspond to different unit prices - Critical grade wired sensor, Hazardous grade explosion-proof certified sensor, Pulse grade wireless sensor)
• Software Subscription: Machine Health platform usage fee + AI diagnostic service
• Expert Service: Remote diagnostic expert support, on-site installation and commissioning Fast Track methodology implementation consultation
• Hidden Cost: Internal Change Management - Moving the maintenance team from "reactive firefighting" to "proactive planning" requires 3-6 months of cultural change

Cost-performance deduction: Based on the 310% ROI reported by Forrester TEI within 6 months, the average annual total cost of a medium-sized factory (about 200 critical equipment) is about hundreds of thousands of dollars. Relative to the possible losses of hundreds of thousands to millions of dollars caused by a single unplanned outage, this investment is a "quickly self-evident" budget item in equipment-intensive enterprises.

Main functions

• Continuous Condition Monitoring: Collect equipment vibration and temperature data 7x24 through industrial-grade vibration sensors, covering 200+ types of rotating equipment such as pumps, motors, compressors, fans, and gearboxes. Supports four monitoring levels: Critical (critical equipment), Hazardous (explosion-proof and environmentally friendly), Ultra Low RPM (ultra-low speed equipment, <20 RPM), and Pulse (medium-critical assets, wireless solution).

• AI Predictive Diagnostics: Based on an AI model trained on 1.1 billion hours of real data, it automatically identifies early failure modes such as bearing wear, imbalance, misalignment, and looseness. Outputs "executable diagnostic results" that include fault type, severity, recommended repair time frame, and operating instructions instead of the raw spectrum plot.

• Industrial AI Agent (Industrial AI Workforce): A collection of multi-role AI agents launched in 2025, covering various functions of the production line - maintenance agents automatically analyze abnormalities and generate maintenance plans, production agents monitor process parameter deviations, and operation agents coordinate actions across systems. Intelligent agents can be connected in series to achieve a complete relationship from perception to execution.

• Production Health: The capabilities integrated after the acquisition of Seebo in 2022 will extend equipment-level forecasting to the production line process level. Not only can it provide an early warning of "a certain pump is about to break down", but it can also track "which process parameter deviation caused the yield to decline", achieving full-link insights from equipment health to production quality.

• Open Integration and Automation (CMMS/EAM Integration): Two-way docking with mainstream maintenance management systems such as MaintainX, SAP, and IBM Maximo. AI early warning can automatically create maintenance work orders, trigger the spare parts procurement process, update asset ledgers, and reduce manual transfer of orders. Provides REST API for in-depth customization.

Expert’s point of view: The real value of Augury does not lie in “the ability to detect anomalies” (this is what most predictive maintenance vendors can do), but in the full-link automation capability of “anomaly detection → fault classification → root cause analysis → maintenance recommendations → work order triggering”. The introduction of industrial AI agents further shortens this link: what the maintenance team receives is no longer the original alarm of "there is a problem with a certain piece of equipment", but a complete decision package of "the No. 3 pump bearing is 72% worn and is recommended to be replaced within 2 weeks. Work order WP-2026-0715 has been automatically created."

Model and version evolution

Founding period (2011-2017)

Founded in Israel in 2011, Augury initially focused on smart sensor and signal processing technology. 2014 launches Auguscope – America’s first smartphone-connected device health diagnostic product. In 2017, the Machine Health category was officially created and the first equipment diagnosis solution based on vibration analysis and preset AI was released, upgrading from a "detection tool" to a "diagnosis platform".

Unicorn Leap Period (2019-2022)

In 2019, a multi-million-dollar production accident was avoided for the first time through AI diagnosis, validating the business model. In 2021, it completed a US$180 million Series E financing and became one of the first unicorns in the industrial AI field. In 2022, it acquired Seebo, a process manufacturing AI company, to form a dual-core layout of Machine Health + Production Health.

Platform maturity period (2023-2024)

In 2023, Seebo technology integration will be completed and a multi-level sensor solution (Critical / Hazardous / Ultra Low RPM / Pulse) will be launched, covering the full spectrum of assets from critical equipment to auxiliary equipment. In 2024, AI diagnostic confidence scoring and root cause analysis capabilities will be enhanced, and cloud infrastructure cooperation with AWS and Microsoft Azure will be deepened.

The era of intelligent agents (2025 to present)

In 2025, it will complete Series F financing of US$75 million and release Industrial AI Workforce—upgrading the product from a “passive diagnostic tool” to a “proactive AI colleague.” The intelligent agent covers multiple roles such as maintenance, production, and operation, and can independently perform multi-step tasks and collaborate with people to complete complex decisions.

Technical advantages

Vibration signal + machine learning fusion mechanism: Augury’s core technology path is “industrial IoT sensor collection → time-frequency domain feature extraction → machine learning classification → executable diagnostic output”. Different from traditional rule engines (based on fixed threshold alarms), its AI model is trained on 1.1 billion hours of real industrial data and can distinguish the subtle spectrum differences between "normal wear and tear" and "emergency faults", significantly reducing the false alarm rate. This is the core pain point in industrial scenarios - an excessively high false alarm rate will cause the maintenance team to "cry wolf" fatigue and ultimately ignore real alarms.

Industrial Adaptation of AI Agents: Augury’s Industrial AI Workforce is not a general-purpose chatbot, but a specialized agent deeply embedded in the industrial knowledge graph. Each agent makes inferences based on equipment topology, historical maintenance records, process parameters and real-time sensor data, with outputs directly tied to specific assets and workflows. The agents collaborate through the orchestration engine, for example: the maintenance agent detects an abnormality → notifies the production agent to evaluate the impact on the production line → the operation agent adjusts the production schedule.

Scale edge architecture: Preliminary feature extraction of sensor data is completed at the edge, and only structured diagnosis results are uploaded to the cloud, reducing bandwidth and storage costs. Supports elastic expansion from dozens of devices in a single factory to hundreds of devices across regions. Hazardous grade sensors are IECEx/ATEX explosion-proof certified and can operate safely in explosive environments such as oil and gas and chemical industries.

Causal reasoning instead of correlation analysis: Unlike most AI solutions that only do correlation analysis, Augury's AI performs causal reasoning through a physical model + data-driven hybrid method - not only telling you "this equipment is abnormal", but also explaining "why it is abnormal" (for example: the frequency of bearing inner ring failure increases, indicating that the cage may have cracks). This explainability is a prerequisite for industrial customers to trust AI decisions.

How to use

Relevant information has not been made public, please refer to the official real-time page.

Product Pricing

Augury uses an enterprise-grade annual subscription with no public standard pricing. The fee structure includes:

• Sensor Kit: Billed based on the number of devices and monitoring levels. The Critical level includes high-precision wired sensors, the Hazardous level includes explosion-proof certified sensors, and the Pulse level includes long-lasting wireless sensors.
• Software Platform License: Billed based on the number of monitoring devices or factories, including AI diagnostic services, dashboards, alarm engines and API access.
• Expert Services: Optional premium support package including remote diagnostic experts, on-site inspections and Fast Track implementation methodology.
• Typical contract period: 1-3 years, with discounts for annual or prepayment.

Since pricing needs to be customized based on the specific equipment quantity, monitoring level and industry, it is recommended to contact the official sales directly to obtain a quote. The typical configuration of the PoC stage is 20-50 key devices, and the cost is relatively controllable.

Application scenarios

• Uninterrupted production guarantee for the food and beverage industry: Among customers such as Nestlé, PepsiCo, Heineken, Danone and others, Augury is used to monitor the health of filling lines, sealing machines, conveying systems and refrigeration equipment. The core value is to avoid unplanned downtime of production lines during peak seasons - one hour of downtime on a filling line in the beverage industry can cost hundreds of thousands of dollars. Key points to verify: Whether the AI ​​warning advance amount is sufficient to arrange planned maintenance (usually 2-4 weeks).

• Safety Compliance Monitoring for Chemical and Oil & Gas: In customer scenarios such as Shell, DuPont, ICL, Bazan, etc., Hazardous grade sensors are explosion-proof certified and can operate in Class I/II Division 1&2 areas. Monitoring objects include key rotating equipment such as compressors, pumps, mixers, and blowers. The core benefit is not only cost reduction, but also turning passive emergency repair into active prevention and reducing the probability of safety accidents. Verification focus: Whether the explosion-proof certification coverage covers actual working conditions.

• Heavy equipment life management for building materials and papermaking: Among customers such as Fortune Brands and Canfor, the Ultra Low RPM grade covers low-speed heavy equipment such as large gearboxes, rotary kilns, and drums—such equipment has low rotation speed but high single unit value and long maintenance cycle. Augury’s AI can issue early warnings 30-60 days before a failure occurs, leaving ample window for purchasing spare parts and arranging overhauls. Verification focus: Whether the diagnostic accuracy of ultra-low-speed scenarios has been independently verified.

• Cross-factory standardized reliability system construction: Large manufacturing groups (such as DuPont, Nestlé) use Augury as a unified platform for global equipment health management. Through the Fast Track methodology, the PoC is first completed in a benchmark factory (3-6 months), and then quickly replicated to dozens of factories around the world. Forrester reports that scaling to nine facilities took just 4 months. Verification focus: Whether the Fast Track methodology provides clear stage acceptance indicators.

Applicable people

• Equipment Maintenance and Reliability Engineer: the most direct user. Upgrading from passive experience judgment of "listening to sounds and touching temperature" to AI-driven data decision-making. Suitable for teams with experience in using CMMS and willing to embrace digital transformation. Prerequisites: Basic equipment ledger management and digital foundation for maintenance processes are required.

• Factory Operations and Production Manager: Monitor the overall performance of the production line through the Production Health function, and analyze the correlation between equipment reliability and production KPIs (OEE, yield, production capacity). Suitable for managing large-scale manufacturing enterprise operation teams with multiple production lines and multiple factories.

• Head of Enterprise Digital Transformation and Industrial IoT: Decision maker for evaluating and introducing Augury. Focus includes proof of ROI (Forrester TEI reports can be used as a basis for internal project approval), integration with existing IT architecture (SAP, CMMS integration), and cross-factory scalability.

• Inapplicable people: Small and medium-sized factories with less than 20 critical equipment (the return on investment is not cost-effective); companies that have fully adopted competing predictive maintenance solutions with good results (high switching costs); factories that lack the digitization of basic maintenance processes (the lack of data baselines leads to reduced AI effects); military or confidential scenarios that require pure offline deployment and cannot be connected to the Internet.

Summary and Outlook

Augury is the de facto standard setter in the field of industrial AI predictive maintenance - it has continued to work on a single track since its inception in 2011, accumulating 1.1 billion hours of real industrial data, diagnostic models for 200+ asset types, and 500+ enterprise customers. Data flywheels and network effects constitute significant first-mover barriers. The launch of Industrial AI Workforce in 2025 marks the evolution of products from "diagnostic tools" to "AI colleagues" and conforms to the industry trend of industrial AI moving from assisted decision-making to autonomous execution.

Current Limitations and Uncertainties:

• Pricing is opaque, making it difficult for small and medium-sized enterprises to assess the true cost, and procurement decisions require business communication processes.
• The initial cost of hardware deployment is high, which is not friendly to factories with tight budgets or low equipment dispersion.
• The application boundaries of Industrial AI Workforce's autonomous execution capabilities in strong compliance industries (such as medicine, nuclear power) still need to be verified - the design of approval gates for irreversible operations requires a clearer industry consensus.
• Increasing competitive product pressure: Traditional industrial automation giants such as Siemens, GE, and ABB are also promoting built-in predictive maintenance capabilities. As an independent platform, Augury needs to continue to prove its independent value relative to OEM native solutions.

Procurement and Adoption Risk Assessment: It is recommended to include 20-50 key devices in the PoC scope first, verify the AI ​​warning lead time and false alarm rate within 3 months, and use the ROI framework in the Forrester TEI report as the acceptance benchmark. Enterprise-level contracts should focus on data ownership terms (whether vibration data can be used for model secondary training), diagnostic response times in SLAs, and sensor hardware warranty and replacement costs during the contract period.

How to use Augury

The application of Augury adopts a four-step process of "Hardware Deployment → Data Collection → AI Diagnosis → Action Connection":

1. INSTALL SENSOR: Industrial vibration sensors (wired/wireless) are installed on site by Augury or a certified partner, deployment time is typically 1-2 days/factory. The sensor is fixed to the equipment bearing seat through a magnetic base or bolts.
2. Access Platform: Sensor data is uploaded to the Augury cloud platform through the gateway. Configure the device ledger and establish the mapping relationship between devices and sensors. The platform's automatic learning equipment operates normally at baseline (approximately 2-4 weeks).
3. Receive diagnosis: The AI ​​engine continuously analyzes vibration data, and when abnormal patterns are detected, a diagnostic report is pushed through the web dashboard, mobile push, email, or integration with CMMS. The report includes the fault type, severity level (1-5), and recommended response time.
4. Execution and Feedback: The maintenance team performs repairs according to recommendations and feeds the actual results back to the platform. Feedback data is used for continuous iterative optimization of the model.

Entry channel: Mainly based on the web management platform, supporting mobile terminals to view alarms and diagnostic reports. The API is open to enterprises for deep integration.

IMPORTANT NOTE: Augury is not a self-service SaaS - typically takes 3-6 months from bid to full go-live, including PoC (proof of concept), hardware procurement and installation, baseline learning, and staff training. For first-time contacts, it is recommended to submit requirements from the "Let's Talk" page (https://www.augury.com/get-a-demo/), and the official sales team will arrange a PoC.

Version Info

• Industrial AI Workforce Release ：Launched Industrial AI Workforce—a multi-role AI agent for production line teams that supports full-link automation from anomaly detection to autonomous execution. There is no official precise date yet. （2025-06）
• Machine Health 360° Suite Expansion ：Expand the Machine Health 360° product matrix and add two new monitoring levels, Ultra Low RPM and Pulse, covering the full range of rotating equipment from high speed to ultra-low speed. There is no official precise date yet. （~2025-03）
• Production Health Platform Update ：Deepen Seebo integration, enhance process-level AI diagnostic capabilities, and expand AI-driven root cause analysis and process health indicators. There is no official precise date yet. （~2024-10）
• Machine Health Continuous Monitoring R4 ：Enhanced Hazardous level certification (IECEx/ATEX), added 30+ asset type diagnosis models, and improved AI diagnosis accuracy. There is no official precise date yet. （~2024-06）
• Seebo Integration & Production Health Launch ：After completing the technology integration after the Seebo acquisition, the Production Health category was officially launched, extending predictive maintenance from the equipment level to the production line process level. There is no official precise date yet. （~2023-09）
• Machine Health R3 ：Introducing AI diagnostic confidence scoring, remote expert collaboration capabilities, and enhanced dashboards. There is no official precise date yet. （~2023-03）