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As Director of Research for Industrial AI at ARC Advisory Group, I've been closely tracking the evolution of artificial intelligence within the industrial sector. My coverage of the "AI Wars" has highlighted the dynamic landscape of technological advancements and the crucial role of innovation in driving industrial progress. A significant aspect of this revolution is the emergence of agile and focused AI startups that are the guerrilla innovators in the AI Wars, tackling specific industrial challenges with novel solutions.
At the recent ARC Advisory Group Leadership Forum 2025 in Orlando, I had the pleasure of sitting down with Sunil Vedula, the Founder and CEO of NanoPrecise Sci Corp, an exciting Industrial AI startup making waves in the realm of predictive maintenance.
Our discussion explored the company's distinctive approach to asset management and its impact on asset-intensive industries.
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Watch the full interview with Sunil Medulla for deeper insights into an industrial AI startup's innovative approach to energy-centric predictive maintenance.
This blog post summarizes the key insights from our conversation, offering a roadmap for organizations seeking to unlock the potential of energy-centric predictive maintenance.
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Key Insights from Our Conversation
Here's a summary of the key insights gleaned from our engaging discussion
Energy-Centric Predictive Maintenance: NanoPrecise offers a solution that uniquely merges energy efficiency with traditional predictive maintenance by identifying faulty components that increase energy consumption.
AI-Powered with IoT Foundation: The company’s solution integrates an AI-based software platform with proprietary IoT sensors to collect and analyze asset data.
Addressing Data Scarcity with GenAI: NanoPrecise leverages GenAI to create synthetic data, overcoming the challenge of sparse data availability for effective fault diagnosis.
Causal AI for Explainability: The company employs a blend of AI techniques, emphasizing pattern recognition and root cause analysis to provide explainable and auditable insights.
Significant Customer Value and ROI: NanoPrecise has demonstrated tangible results, including substantial cost savings and impressive returns on investment for its clients in various sectors like oil & gas and steel.
Rapid Deployment and Scalability: The company’s sensor technology is designed for quick and easy installation, allowing for rapid deployment and scalability across industrial operations.
Let's delve deeper into the key topics we explored.
The NanoPrecise Solution: Marrying Energy Efficiency and Predictive Maintenance
We kicked off our conversation by understanding the core offering of NanoPrecise. Sunil explained that the company provides "energy centric predictive maintenance as a solution" for asset-intensive industries. This immediately piqued my interest, as it goes beyond the traditional focus of simply predicting equipment failures.
He detailed their five-step process, which seamlessly incorporates energy considerations into the maintenance workflow:
Anomaly Detection: Identifying deviations in sensor data.
Fault Diagnosis: Pinpointing the specific component within the machine that is at fault.
Remaining Time to Failure Prediction: Forecasting the time until potential failure due to the identified fault.
Energy Efficiency Calculation: Quantifying the increase in energy consumption caused by the faulty component.
Root Cause and Maintenance Action: Providing the underlying cause of the fault and recommending specific maintenance steps.
As Sunil aptly put it, "every faulty component is costing you more in energy consumption of that machine, so that's where we calculate what's the increase in energy consumption due to that fault and the fifth step is root cause and maintenance action to be taken." This dual focus enables NanoPrecise to deliver a strong value proposition by preventing downtime while optimizing energy usage—an essential priority in today’s industrial landscape. It also aligns with the growing role of Industrial AI in advancing sustainability initiatives.
Overcoming Data Scarcity with the Power of GenAI
A common hurdle in deploying AI for industrial applications is the availability of sufficient and high-quality data. I was particularly interested in how NanoPrecise tackles this challenge. Sunil revealed that the company's "secret sauce" lies in its ability to achieve this with relatively sparse data.
Sunil elaborated on their innovative approach, explaining that while 90 percent of faults lack direct information, they follow identifiable patterns. By leveraging GenAI and LLM-based pattern recognition, the company can generate synthetic data from extensive research, allowing them to detect and analyze faults as they emerge in real-world customer environments.
This strategic use of GenAI to generate synthetic data complements limited real-world data, enhancing the accuracy of fault diagnosis, especially for rare failure modes. It highlights the evolving and innovative applications of AI in Industrial AI.
Explainability and Trust using Agentic AI
We also explored the "black box" challenge often associated with AI models. Sunil stressed the importance of explainability and auditability in their solution, highlighting how their approach leverages Agentic AI and large language models to correlate maintenance logs with fault data. This enables them to pinpoint not just symptoms—such as a bearing fault—but also identify the root cause, like misalignment, and recommend precise corrective actions, such as laser alignment.
This emphasis on root cause analysis, powered by a combination of AI techniques, gives users a clear understanding of why an issue occurs and the appropriate corrective action. As I noted in our conversation, this approach addresses the common challenge with GenAI, which often functions as a "black box" where the reasoning behind recommendations is unclear. I asked, "So what I'm hearing from you is that because you've identified the root cause and interrelationships, you solve the problem of explainability and auditability—is that fair to say?"
Sunil’s affirmative response highlighted the critical role of explainability in building trust and driving adoption in industrial settings. This aligns with ARC’s emphasis on the need to understand the “why” behind AI recommendations to ensure effective implementation and scalable deployment.
Tangible Value and Rapid ROI: Customer Success Stories
The conversation then turned to the real-world impact of NanoPrecise's solution. Sunil shared compelling customer success stories, including:
Cydril Americas (Oil & Gas): Achieved nearly $3 million in value within a single year by monitoring just one or two rigs equipped with approximately 100 sensors.
Tata Steel (Steel Industry): Officially reported savings of "almost $5.5 million per annum, which is almost 10x ROI for what they spend on our software."
These examples underscore the substantial financial benefits and rapid return on investment that NanoPrecise provides, exemplifying the "fast time to value" approach that ARC recommends for prioritizing Industrial AI use cases. As Sunil put it, their value proposition is about achieving "two birds with one shot"—saving energy and preventing downtime.”
Key Insights and Takeaways
Our conversation with Sunil Vedula highlighted the promising role of Industrial AI startups in tackling industry-specific challenges. NanoPrecise’s innovative approach to energy-centric predictive maintenance—leveraging GenAI for data augmentation and prioritizing explainability through root cause analysis—positions it as a key player in this evolving space. Its proven customer success and rapid ROI further reinforce the impact and effectiveness of its solution.
Toward the end of our conversation, I summarized my key takeaway: "You're combining AI techniques, including generative AI and neural networks, with a smart sensor that boasts excellent battery life. Plus, you're implementing an efficient method to upload data to the cloud, ensuring its availability for over five years. Does that accurately capture the solution? Additionally, since your approach requires significantly less data, you can scale up quickly, achieve ROI faster, and expand efficiently." Sunil's enthusiastic agreement reinforced NanoPrecise's core strengths.
Quote
Sunil Vedula (NanoPrecise): “Every faulty component is costing you more in energy consumption of that machine. How do you prepone that maintenance action to save the energy consumption and eventual downtime—two birds with one shot? Thats what we are providing!”
"Prepone" is a term primarily used in Indian English, meaning to reschedule something to an earlier time. It's the opposite of "postpone." For example, if a meeting originally scheduled for Friday is moved to Wednesday, you’d say the meeting was "brought forward" or "rescheduled to an earlier date."
Engage with ARC Advisory Group
For ARC Advisory Group recommendations for navigating the AI Wars, closing the digital divide by embracing Industrial AI, assembling your Industrial-grade Data Fabric, and governing and guiding major decisions about enterprise, cloud, industrial edge, and AI software, please contact Colin Masson at [email protected] or set up a meeting with me, or my fellow Analysts at ARC Advisory Group.