Do you see engineering and process industries progressing with the adoption of Applied AI and Agentic AI and what momentum do you expect in FY2026-27?

Geopolitical uncertainty is disrupting energy markets and supply chains, forcing industries to maximize their resilience, agility, and efficiency. To address these evolving challenges, we established a dedicated Applied AI division roughly seven years ago. While our initial efforts centered on localized pilot projects, we pivoted last year to deploying enterprise-wide AI solutions for major petrochemical companies across the United States, Europe, and the Middle East. This strategic transition has triggered a massive acceleration in our market momentum.

Three years ago, we launched a dedicated software division focused on AI-driven industrial platforms and agents. By pairing foundation models with highly specialized, industry-specific agents, we deliver advanced solutions for process industries. Currently, we are partnering with a leading DCS provider to deploy this platform for a major chemical company, integrating AI with advanced process control systems to maximize operational intelligence.

Another major driver is the accelerating push for utility and energy optimization. With efficiency now a critical priority, organizations are increasingly deploying applied AI for power management. Moving forward, we expect to see highly specialized, domain-specific Industrial AI agents emerge as packaged products. These pre-built solutions will empower industries to deploy advanced, highly reliable intelligence on a self-service basis.

Reasons for creating separate divisions for software and AI-driven services? 

We began as a service-oriented company, helping clients implement existing technologies. However, we quickly noticed that generic solutions fell short in complex, highly specialized fields like process manufacturing. This realization drove us to pivot and systematically build custom products tailored to the industry's unique demands.

We developed a suite of industrial platforms and intelligent agents, including contextualised data hubs, optimization tools, and alert monitoring systems. By integrating advanced conversational AI, we enable engineers to interact naturally with machinery. Operating as intelligent assistants, these systems analyze natural language questions to retrieve precise, data-driven answers from engineering documents, operational logs, and historical data.

In process industries, accuracy is critical. Because incorrect AI responses can cause severe operational and safety hazards, industrial context must be deeply embedded into the platform. To meet this need for highly specialized engineering, we built a dedicated software group focused purely on developing this technology.

Will this expansion lead to increase in manpower?

Absolutely. While our services team grows dynamically with new projects, we consciously chose a different path for software. We invested in a dedicated 50-person team focused entirely on developing specialised industrial AI products. This strategic investment is now paying off, as seen in rising customer traction and market interest.

With recent orders worth nearly Rs. 200 crore in digital solutions. What are the next growth drivers for Ingenero?

Growth is accelerating across several key sectors. Applied AI continues to drive our expansion, propelled by enterprise-scale industrial deployments and specialized AI agents. Simultaneously, we are scaling our process safety business internationally, leveraging nearly 20 years of expertise to expand beyond our historical focus on the US market.

We are seeing surging demand from the Middle East, India, and beyond. Modern process safety is now proactive; instead of only analyzing incidents after they happen, the industry is prioritizing early detection and prevention using AI-driven monitoring and predictive systems.

We are seeing rapid demand for specialized engineering and digital services, specifically in Global Capability Centre (GCC) engineering, specialized process engineering, and safety engineering. Furthermore, a new wave of demand is emerging as companies increasingly seek our strategic guidance to kickstart their AI adoption and digital transformation journeys.

You have deployed digital twins that have helped companies save millions of dollars. Beyond financial returns, how do these tools change daily life of plant operators?

Digital twins transform decision-making by equipping operators, engineers, and executives with predictive intelligence and early threat detection. This foresight transitions teams from reactive firefighting to proactive management. By sharpening planning capabilities, these virtual models enable facilities to adapt instantly to volatile markets and sudden operational shifts.

Digital twins help operators run plants with far better intelligence and visibility. One exciting development is the emergence of specialised engineering copilots or chat-based AI systems. Over time, engineers are beginning to interact with these systems almost like highly experienced colleagues.

Is AI-driven engineering assistant already operational?

Yes, the platform is already operational. Over the past year, we deployed it across several sites while continuously improving its capabilities. Our primary focus remains reducing hallucinations and ensuring absolute accuracy. Unlike generic LLMs, our system is custom-built for process industries. It validates user queries, retrieves information exclusively from trusted engineering sources, provides clear source transparency, and explains the reasoning behind every recommendation. This rigorous process significantly enhances system trust and reliability.

How is engineering leveraging AI specifically to improve sustainability and process manufacturing efficiency?

AI drives corporate sustainability by optimizing emissions, energy use, and waste reduction in real time. By processing complex industrial variables simultaneously, these systems deliver actionable intelligence faster than traditional methods, empowering companies to seamlessly balance regulatory compliance, profitability, and environmental goals.

Some common misconceptions companies still have about AI-driven digitalization?

Many leaders mistake AI for a basic analytics tool. In truth, it acts as a dynamic decision engine, driving operational intelligence through specialised agents. Similarly, the common "data-first" strategy is flawed. Gathering massive amounts of data does not automatically create value. Success requires identifying specific business outcomes first, then working backward to gather the data needed to achieve them.

Contrary to the myth that a single AI model can do it all, industrial AI demands a hybrid approach that blends machine learning with domain expertise and first-principles engineering. Furthermore, industrial AI is never simple plug-and-play; successful implementation requires rigorous engineering integration, governance, and validation.

Differing approaches of digital transformation between western and Indian companies?

The difference is largely driven by historical evolution. Many western companies already have mature IT and OT infrastructures with numerous legacy systems and software layers in place. As they transition into AI and agentic AI applications, they must integrate with these existing environments. In India, companies often have fewer legacy constraints. This creates an opportunity to leapfrog directly into next-generation technologies, similar to how India leapfrogged into digital payments through UPI. So while the end goals may be similar globally, the transformation journey and implementation path can differ significantly.

How do AI-based systems optimize operations such as heat exchanger cleaning and fouling management?

Modern digital solutions allow companies to monitor process conditions in real time, replacing traditional periodic studies done every few years. AI models continuously track equipment performance to detect early signs of fouling or degradation. This allows operators to schedule maintenance or cleaning at the optimal time. Consequently, companies avoid both premature cleaning costs and the energy losses caused by waiting too long. This shift significantly improves energy efficiency, product yield, and overall operational performance.

How do hybrid AI models combine first-principles engineering with machine learning (ML)?

Hybrid AI models merge engineering fundamentals with data-driven machine learning into a single, unified framework. This integration imposes physical and logical constraints on the AI, curbing erratic behavior while retrieval-augmented generation guarantees responses rely strictly on validated operational documents, massively boosting accuracy and system trustworthiness.

Biggest challenges in integrating carbon capture systems into existing industrial infrastructure?

Integrating new carbon capture systems into existing brownfield facilities is highly challenging because older infrastructure was not originally designed to support them. Moving these technologies from laboratory settings to commercial scales demands thorough integration planning and complex engineering retrofits. Consequently, companies face the multi-dimensional task of managing financial profitability, regulatory compliance, operational demands, and environmental targets at the same time. Furthermore, because these advanced systems are technically sophisticated, structured workforce training is vital to bridge the specialized skills gap.

Given that your solutions enable remote access to critical infrastructure, how important is cybersecurity?

Cybersecurity is absolutely critical. Cybersecurity is built directly into our product architecture, not added as an afterthought. We follow a security-by-design approach to protect both IT and OT environments according to global industrial standards. Our solutions deploy flexibly across cloud, private cloud, on-premises, or hybrid setups. Every deployment includes encryption, secure data pipelines, role-based access, strict compliance, and continuous security validation.

Looking ahead, how do you see Agentic AI reshaping process industries?

Agentic AI represents a paradigm shift beyond traditional analytics and static models. The future of industrial operations centers on specialized AI agents engineered to automate workflows, guide critical decision-making, and orchestrate complex enterprise processes. During this transition, maintaining rigorous human oversight is vital. Over time, operational workflows will naturally restructure to integrate these intelligent agents. Full operational autonomy will be deployed incrementally, contingent upon extensive validation, robust safeguards, and continuous machine learning. Ultimately, the industrial sector is moving away from fragmented digital applications toward comprehensive, enterprise-wide intelligence systems.

What growth are you expecting in FY 2026-27 and are there any plans for listing?

We are a closely held private company, so we do not disclose financial numbers. However, we expect a strong double-digit growth in FY 2026-27. As far as listing is concerned, currently there is no plan for an IPO in the next one to two years.