Blog

Infibeam’s Phronetic.AI, Nawgati Sign MoU for Real-Time Agentic AI & Video-LLM

Infibeam’s Phronetic.AI Signs MoU with Fuel-Tech Leader Nawgati Offering Agentic AI and Video-LLM Technology that Automates Reasoning Capabilities on Real-Time Basis

New Delhi, July 04, 2025 – Nueromind Technologies Private Limited (“Phronetic”), a cutting-edge deep-tech AI company owned by Infibeam Avenues Ltd, has entered into a Memorandum of Understanding (MoU) with Nawgati Tech Private Limited, a pioneering deep-tech company transforming India’s fuel and mobility infrastructure. This deal aims to co-develop next-generation AI-powered solutions tailored for the fueling, fleet, and energy sectors to automate reasoning capabilities for accurate decision making on real-time basis by using CCTV-video inputs.

Phronetic’s ABM platform is a multimodal technology that combines Video-Language Models (Video-LLMs) with a reasoning model. The Video-LLMs provide insight for understanding the environments, while the reasoning models provides intelligence in contextualizing this information. Ultimately, Phronetic’s Agentic AI, known as the field agent, enables real-time actions based on these insights.

Companies often face operational challenges related to improving efficiency and ensuring SOP (standard operating procedures) compliance. Although current technologies can identify or detect these challenges, they fall short in providing decision-making and action-oriented suggestions due to their lack of reasoning capabilities with video inputs.

“While reasoning capabilities using text are available in the market, there is a significant gap in reasoning models that effectively utilize video inputs, especially in industries such as manufacturing and retail, where physical production and on-the-floor activities take place. By leveraging reasoning capabilities to analyze video feeds, organizations can dramatically enhance their efficiency and SOP compliance, which will ultimately contribute to their business growth,” explained Mr. Rajesh Kumar, CEO of Phronetic AI, emphasizing that Phronetic’s technology addresses the pressing need for accurate decision-making by automating reasoning tasks and aiding to take an action.

Expressing his enthusiasm, Mr Rajesh Kumar further said “Partnering with Nawgati allows us to apply our intelligence at scale and create real-world impact in high-throughput, high-stakes energy operations.”

Mr. Vaibhav Kaushik, Co-Founder & CEO of Nawgati, said: “Nawgati is leading the digital transformation of India’s fuelling infrastructure through deep-tech solutions powered by AI and advanced deep learning algorithms. Our flagship platform, Aaveg, is deployed across thousands of fuel stations to monitor retail operations in real time, reduce pilferage, and ensure SOP compliance. We are delighted to work with Phronetic and leverage their advanced AI capabilities to enhance the intelligence and efficiency of our solutions. Together, we’re unlocking a new era of autonomy and efficiency across fuel and fleet networks.”

Key Highlights of the Collaboration:

Agentic AI Deployment: Phronetic will integrate its Field and Desk AI Agents into Nawgati’s tech stack to enhance operational intelligence, automate customer interactions, and support intelligent decision-making across various environments.

Go-to-Market (GTM) Partnership: Phronetic and Nawgati will jointly go to market with AI-powered offerings for the fuel, fleet, and energy sectors, leveraging Nawgati’s vast on-ground deployments, deep-tech Aaveg platform and Phronetic’s agentic AI capabilities.

Nawgati’s flagship product, Aaveg, is a vertical SaaS platform used by energy players to optimize forecourt operations, reduce inefficiencies, manage congestion, monitor SOPsand enhance compliance. Nawgati’s product is deployed across thousands of fuel outlets across the country. In addition, Nawgati powers India’s largest fueling app, serving over 2 million users, offering a seamless end-to-end fueling experience. Nawgati has also played a pivotal role in digitising CNG fleet programs across India, streamlining logistics and enhancing transparency for commercial vehicle networks. Fuel outlets of Indraprastha Gas, Mahanagar Gas, Torent Gas, and petrol pumps operated by dealers of Indian Oil, Bharat Petroleum, Hindustan Petroleum and JioBP use Nawgati’s products.

Founded at BITS Pilani, Nawgati is at the forefront of fuel-tech innovation, developing intelligent digital infrastructure solutions for India’s energy sector. By unifying fuel stations, fleet operators, and consumers on a single platform, Nawgati enhances network utilization, streamlines refueling operations, and offers real-time visibility into fuel availability and fleet movement. The company operates at the intersection of deep-tech, mobility, and energy, and is backed by marquee investors including GAIL (India) Ltd, Ajay Upadhyaya, Deepak Bhagnani Family Office, MeitY Startup Hub, Department of Science & Technology (DST), Jash Bhurjee, BITS Spark, and Aamara Capital.

Phronetic is an AI business vertical under Nueromind Technologies Private Limited, a pioneering deep-tech AI company and a subsidiary of Infibeam Avenues Ltd.

Beyond Automation: Exposing the Security Gaps in Industrial Autonomy

In today’s fast-moving industrial IoT (IioT)  landscape, embedding AI-powered decision-making into operational technology system has fostered a sense of greater control, quicker response times, and improved predictive capabilities. However, this perception of control can, in reality, be a potentially dangerous illusion.

India’s industrial IoT market is experiencing rapid growth, reaching USD 9.40 billion in 2024 and projected to grow at a CAGR of 12.9% to USD 28.15 billion by 2033, driven by the increasing adoption of cloud computing, digital twins, robotics, and blockchain technology.

Autonomous systems are now responsible for critical infrastructure: smart grids, manufacturing lines and water treatment facilities, all relying on interconnected sensors and AI for autonomous decision-making. But as the layers of automation deepen, so too does the complexity, making it increasingly difficult to understand or audit decisions made by machines. The Indian government’s Digital India and Make in India initiatives are accelerating the adoption of AI and IIoT across sectors, encouraging organizations to integrate AI into their operations and fueling the country’s emergence as a global AI hub.

As more layers of automation are added, the number of interconnected components – think of sensors, AI algorithms, communication network, and control systems- grows exponentially. Each new layer introduces more variables, dependencies and potential points of failure. AI models themselves often operate as “black boxes,” making decisions based on patterns and data that are not always transparent. Plus, these systems constantly adapt and learn in real time, which adds unpredictability. All of this combined makes it harder to fully grasp, track, or audit how decisions are made inside the system, driving complexity ever higher.

The role of AI in OT environments

AI is transforming OT environments by enabling real-time analytics, predictive maintenance, dynamic response mechanisms and system-wide orchestration. Here are key examples of how AI is applied:

• Predictive maintenance: In manufacturing, AI models forecast machinery failures based on metrics like vibration analysis and thermal imaging, reducing downtime.
• Anomaly detection: In the energy sector, AI monitors voltage and frequency to detect abnormalities in grid performance before outages occur.
• Autonomous control systems: In water treatment, AI algorithms dynamically adjust chemical dosages and valve operations based on sensor data.

India’s artificial intelligence market is projected to grow at a CAGR of 29.1% from 2025 to 2032, with AI adoption continuing to expand rapidly across manufacturing, healthcare, finance, and urban infrastructure including smart cities, traffic management, and public safety.

These implementations are part of Industry 4.0, where cyber-physical systems not only automate processes and boost efficiency but also blur the lines between IT and OT. Traditionally, OT systems were isolated, air-gapped and closed off from external networks for security. Now, with the rise of smart sensors, cloud computing and (smart edge) connected devices, those boundaries are blurring. IT and OT are becoming deeply intertwined, creating new opportunities, but also new risks, as the systems become more interconnected and dependent on each other. By 2026, cloud technology is expected to represent 8% of India’s GDP, potentially increasing the nation’s GDP by USD 310–380 billion and creating 14 million jobs. Cloud platforms are enabling Indian manufacturers to process real-time data from connected devices, optimize production, and reduce operational costs

Examples of automation per OT vertical

• Energy: AI predicts load demand, optimizes energy dispatch and autonomously reroutes power in case of faults. Enel in Italy, uses AI and smart grids to balance energy supply and demand dynamically.
• Manufacturing: Smart robots handle QA with computer vision, reorder inventory autonomously and self-correct production inefficiencies. Bosch uses smart robots equipped with computer vision for quality assurance on their production lines, spotting defects in real time.
• Critical Infrastructure: AI regulates traffic lights based on congestion data and operates dam sluice gates to balance water levels. The Oosterschelde Keering is part of the Netherlands’ famous Delta Works, a massive system of dams, sluices, locks and barriers designed to protect the lower laying country from flooding by the North Sea. The barrier’s sluice gates can be autonomously operated to regulate water flow and maintain safe water levels, balancing the need to protect against storm surges while allowing tidal movements and shipping traffic. Sensors continuously monitor sea levels, weather conditions and structural integrity, feeding data into advanced control algorithms that decide when to close or open the gates.

The paradox of autonomy

The paradox of autonomy lies in the delicate balance between human control and machine independence. Autonomous systems are designed to operate without constant human intervention, aiming to enhance efficiency and responsiveness. However, this shift places human operators on the sidelines, reducing their direct oversight. Meanwhile, AI-driven systems continuously evolve, adapting their decision-making models in ways that can be unpredictable and difficult to fully understand. Compounding this challenge, many fail-safes remain hard-coded based on outdated assumptions that fail to reflect the dynamic nature of modern AI behavior. As a result, the very autonomy meant to increase safety and control can paradoxically introduce new risks and uncertainties, highlighting the urgent need for adaptive oversight mechanisms that keep pace with evolving technologies.

The dangers of threat autonomy

Autonomy introduces a new threat class: systems that can be manipulated, misled, or repurposed by adversaries. Attackers no longer need to break a system, they just need to confuse or poison its decision logic. Examples include:

• AI confusion attacks: A sensor-fed AI in a power grid receives spoofed inputs, causing it to miscalculate loads and trip breakers unnecessarily.
• Over-optimization exploits: In a smart factory, attackers subtly shift input values, prompting AI to unknowingly degrade product quality while chasing efficiency.
• Cascading failures: Interconnected autonomous decisions in transport and energy grids can lead to systemic collapse if one node fails unpredictably.

For OT CISOs:

• Demand explainability:  Demand explainability by ensuring your AI-driven systems use explainable AI (XAI) models. When an AI makes critical decisions, like shutting down a turbine, it is key to understand the reasoning behind those actions. Explainability builds trust, helps diagnose issues and supports compliance by making AI decisions transparent and accountable. Ensure your AI-driven systems include explainable AI (XAI) models. If a machine shuts down, you must know why!
• Invest in red teams for AI models: In OT environments, red teams focused on AI models not only simulate cyber attacks, but also consider the physical impact on industrial processes and safety, especially as OT controls critical infrastructure, like power grids, manufacturing lines and water systems. These teams evaluate how AI-driven decisions could be manipulated to cause operational disruptions, equipment damage or even safety hazards. Invest in red teams for means testing both cyber and physical vulnerabilities to ensure robust, resilient and safe autonomous operations. These specialized teams simulate adversarial attacks or manipulation scenarios on AI systems.
• Implement operational drift monitoring: Continuously monitor for model deviation from baseline behavior. Operational Drift Monitoring is the process of continuously tracking and analyzing changes in an operational system’s behavior or performance over time. In OT environments, it helps detect deviations from expected patterns, whether due to equipment wear, configuration changes or emerging security threats. By identifying these drifts early, organizations can prevent failures, reduce downtime and maintain system integrity and safety.
• Embed human-in-the-loop (HITL) controls: Embed human-in-the-loop (HITL) controls to ensure that critical AI decisions, especially in high-stakes environments, are reviewed and verified by human operators. This approach combines the speed and efficiency of AI with human judgment and oversight, reducing the risk of errors or unintended consequences. HITL helps maintain safety, accountability and trust in automated systems where decisions can have significant real-world impacts.