Our mission is to transform how people and machines work together to push the boundaries of human productivity.
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Joined July 2011
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Before you pick an AI tool, pick a metric.
Former plant manager Jared Pfeiffer on tying AI to what plants already track: downtime, throughput, yield, scrap, quality, energy. The strongest ROI comes from specific problems: bit.ly/4yNx169.
A boiler drive motor was shaking so badly you could see it. The call on site: replace the motor.
The data pointed somewhere else: a loose lock ring at the other end of the shaft. How Canfor turned that save into a culture shift across 16 mills: bit.ly/3VuwJTC.
Plants run equipment for 20-30 years. AI models improve almost weekly.
Stephan Liozu on the AI speed paradox, and why the answer isn't moving at software speed: bit.ly/4xZLRFZ.
In hydrocarbon operations, an offline production unit can mean ~$250K/hr in deferred revenue.
That's the cost of the coverage gap in hazardous zones, where standard sensors can't safely operate. Augury's Andrew Pry on what continuous monitoring changes: bit.ly/4rzKKvi.
76% of firms say poor or incomplete data is a significant barrier to their AI projects (Verdantix).
That number explains most of what goes wrong with agentic AI in manufacturing.
Augury's Anoop Mohan on getting it right ⬇️ bit.ly/4yJzSwY.
"To capture valuable factory-floor knowledge that might otherwise be lost to attrition and turnover, Augury's Industrial AI Workforce added the OKF Industrial Profile."
Great to see Augury featured in Google Cloud's piece on the agentic factory: bit.ly/4h6sR3z.
"Those same employees are going to eventually be focused on 'What do I do with the information?'"
Briggs and Stratton's Erik Syrjanen at IMTS, on the shift he expects as AI gets better at reading data across operating systems.
Full panel ⬇️ bit.ly/3Tf0Q0E
"Every site has people who can walk past a machine and tell you what's happening with it. That's 25 years of pattern recognition."
Augury's Ronen Salomon on the knowledge most at risk in manufacturing right now: bit.ly/4xIGb39
Augury retweeted
For #engineers, operators and reliability teams alike, turning disconnected data into actionable decisions require time-consuming manual investigation. @augurysys hopes to solve that problem through role-based #AIagents. Watch video: bit.ly/3QQeJBh.
#AI #automation
Augury retweeted
What happens when AI can connect machine health, maintenance, and operations data in one place?
On the next episode of The Wrench Factor, @augurysys CEO Elan Greenberg joins @theNickHaase to discuss the Industrial AI Workforce and why breaking down data silos could transform manufacturing.
🎧 New episode drops THURSDAY at 7:00 AM ET!
✅ Subscribe to the show anywhere you listen to podcasts
#TheWrenchFactor
Augury retweeted
From @augurysys: AI-powered predictive maintenance is transforming #OilAndGas operations—reducing downtime, improving worker safety, and enabling smarter asset management across distributed facilities.
#ProcessManufacturing #PredictiveMaintenance processingmagazine.com/maint…
Augury retweeted
Industrial AI systems can now capture the tribal knowledge of experienced reliability engineers in real time, Anoop Mohan from @augurysys explained. With 1.1 billion hours of machine data collected, Augury's AI agents learn from every diagnostic interaction a skilled engineer has with the system.
When a technician identifies a hissing or humming sound as a bearing issue, that diagnosis becomes a learning point. The AI logs it and uses it to guide future engineers facing the same situation, telling them whether they are looking at an inner bearing or outer bearing issue, what was done previously to fix it, and how urgent the repair actually is.
Anoop also pointed to the theoretical dimension. AI tools like Gemini have already absorbed all the academic and certification material for disciplines like vibration analysis, effectively qualifying in principle as certified analysts at that level.
Augury retweeted
Manufacturing is facing what Anoop Mohan from @augurysys calls a skills tsunami, with the average tenure of reliability engineers dropping from 10 to 15 years down to just two to three years.
Reliability engineers are central to keeping manufacturing operations running. When a machine fault is detected, they analyze it to determine the root cause, including whether the issue is related to inner or outer bearings, whether it is isolated to one plant or affecting multiple, and whether it impacts a single asset or several. That root cause analysis process can take days or even weeks.
Once the analysis is complete, the engineer opens a maintenance ticket (called a CMMS ticket), and a maintenance manager determines whether to schedule a repair immediately or defer it, weighing production schedules and line availability.
Anoop describes this handoff between roles as a swivel chair moment. Augury's approach is to replace those handoffs with AI agents, one focused on the reliability function and one on the maintenance function, coordinating with each other toward autonomous task completion while the reliability engineer and maintenance manager retain oversight.
Fewer than half of AI projects make it past the pilot phase. The ones that do tend to share one thing in common: they're not siloed: bit.ly/41pGoKI.
Augury retweeted
What happens after you detect a machine issue?
With @augurysys + MaintainX, the answer is: the right work starts immediately.
Augury identifies developing faults through AI-driven machine health monitoring, with certified vibration analysts prescribing next steps.
With MaintainX, manufacturers can turn those insights into action—giving technicians the context, asset data, and AI-powered assistance they need.
Integration available now: bit.ly/4lNlP4y
#PredictiveMaintenance #IndustrialAI #Reliability #Manufacturing
ALT A graphic featuring the logos of Augury and MaintainX. The top section has the Augury logo with a stylized wave symbol and "AUGURY" text. The bottom section includes the MaintainX logo, a stylized "X," with the text "Augury & MaintainX: MaintainX Partner Spotlight." The background is blue and white.
82% of manufacturing managers say they didn't actively seek a career in the industry. They fell into it. Now they're the ones carrying decades of expertise that's increasingly hard to replace: bit.ly/4uOutDP.
Technology doesn't create advantage - teams do. Companies mapping people and tasks before vendor demos, defining clear AI-human collaboration playbooks, and measuring where AI actually contributes to real work are the ones seeing results: bit.ly/4tD8HCm.
Manufacturing is becoming a digital environment requiring highly skilled technicians who can operate, repair, and troubleshoot high-tech equipment. Young people with digital skills might be perfect recruits for this evolution: bit.ly/4k8Xsxq.
Manufacturing professionals are quietly building AI fluency - 8 out of 10 top learning topics are AI-related according to new Udemy data: bit.ly/40q2b4o.
This new survey reveals a strategic shift in how manufacturers are addressing workforce challenges. With 71% reporting skilled labor gaps directly impact their operations, companies are turning to pragmatic technology solutions: bit.ly/4rWP3j1.
