Opsight AI Pvt. Ltd. revolutionizes the manufacturing sector with its cutting-edge Industrial AIoT and SaaS solutions. The company is transforming how manufacturers harness data, empowering them to make smarter, data-driven decisions, streamline operations, and unlock their full potential through advanced analytics. Opsight AI is on a mission to lead Indian manufacturers toward operational excellence by shifting them to data-centric methodologies. Through the integration of IIoT, AI, and ML, Opsight AI helps manufacturers tackle their biggest challenges—minimizing equipment downtime and reducing costly rework—by providing predictive insights that drive informed decision-making.
In an exclusive conversation with The Interview World, Pulak Rijhwani, Founder and CEO of Opsight AI Pvt. Ltd., sheds light on how his company converts operational data into actionable insights using AI. He underscores the rigorous measures in place to ensure the precision of predictive analytics and addresses the potential for error in such predictions. Pulak also offers valuable insights into the current pace of Industry 4.0 adoption and shares his vision for the next wave of innovations that will build on their existing solutions. Here are the key takeaways from his interview.
Q: How is Opsight AI converting operational data into actionable insights to drive efficiency and value for your clients?
A: At its core, Opsight AI is redefining how manufacturers transform operational data into actionable insights. This operational data can come from any source on the shop floor—whether it’s CNC machines, robotic systems, or compressors deployed across multiple factories or within a single plant. We capture data from these machines and build sophisticated AI/ML models around it, starting with real-time monitoring. Over time, as the data flows through our platform, we advance to predictive analytics.
Our insights extend beyond just machine monitoring. We provide deep visibility into production metrics—such as daily production output, overall equipment efficiency, and cycle times. Additionally, we offer historical data access, enabling manufacturers to pull data from previous years, months, or any specific period they need.
However, the real power of our AI lies in its ability to predict machine downtime and assess the quality of components in real-time. That’s our vision—predicting and preventing operational issues before they occur. While we are in the early stages of this journey, we’ve already built a minimum viable product (MVP), successfully completed four to five pilot projects in the industry, and are now focused on commercializing our solution to scale new heights.
Q: How do you ensure the accuracy of predictive analytics data in manufacturing to enable companies to take corrective actions?
A: I spent 12 years in the manufacturing sector before launching this venture, so I’ve seen firsthand how data from manufacturing systems often fails to reach top management in a meaningful, actionable format. The quality of that data is critical. That’s exactly why we deploy our own edge gateways directly on the machines—they capture data at the source. These gateways then securely transmit that data to our platform.
Once the data pipeline is in place, we ensure both the integrity and quality of the data. We don’t just monitor the machines for our clients; we also monitor the performance of our own edge devices. For example, if we’re managing a fleet of 100 edge devices, we track metrics like CPU utilization and temperature to ensure they’re operating reliably. This dual-layer monitoring—of both the machines and the gateways—ensures robust data security and guarantees the accuracy of our predictive analytics. That, in turn, empowers companies to make informed, corrective decisions with confidence.
Q: What is the likelihood of errors occurring in the predictive accuracy of events in this scenario?
A: Errors can often originate from the equipment itself, and if the equipment isn’t providing reliable data, we detect that immediately. We’ve set strict thresholds for data parameters to ensure the quality of data we’re receiving from the equipment.
If the data deviates from those thresholds, we investigate its reliability and verify its accuracy. To further safeguard against network losses or data gaps, we’ve built in redundancy—our edge devices also store data locally. In case of network interruptions, the device retains the data and seamlessly syncs it to the cloud once the connection is restored. We take every step to minimize errors and ensure the highest accuracy in our predictive analytics.
Q: What is the current adoption rate of Industry 4.0 technologies, and how are they impacting operational efficiency and performance?
A: Adoption rates are strong at the moment, especially among OEMs and tire manufacturers, particularly in metro cities where Industry 4.0 is well understood. It’s no longer just a buzzword—people have been aware of it for the past 5 to 7 years.
However, its penetration into smaller industrial hubs is still lagging. When I visit places like Ludhiana or Rajkot and speak with manufacturers, it’s clear that many remain unfamiliar with the principles of Industry 4.0. In these areas, there’s still a need for greater awareness. The government is making commendable efforts to bridge this gap by establishing Centers of Excellence that promote these technologies and demonstrate their benefits to the manufacturing sector. Among OEMs and Tier 1 companies, adoption is excellent, with the automotive sector leading the charge in embracing Industry 4.0 innovations.
Q: What new technologies or innovations are you developing to enhance the solutions you currently offer to your clients?
A: The new technologies we’re developing aim to incorporate digital twin capabilities as well. Right now, our focus is on IIoT, Big Data Analytics, and AI/ML—these are the three foundational tech stacks that underpin our work with manufacturing data.
As emerging technologies continue to evolve, the opportunities for reimagining innovative solutions are vast. We are keenly attuned to the shifting dynamics in the technology landscape, ready to seize every opportunity for advancement.