Reaching New Heights: a conversation with Hemanth K Rajasekhar

A conversation with Hemanth K Rajasekhar on engineering, entrepreneurship, and AI — Founder & Managing Director, India, NeuAlto.

After more than two decades across telecommunications, embedded systems, semiconductors, and global customer delivery, Hemanth K Rajasekhar co-founded NeuAlto to bring enterprise experience to focused product engineering. In this edited conversation, he reflects on building teams, learning from technology cycles, and leading through the rise of AI.

From engineering to entrepreneurship

What led you to establish NeuAlto?

I began as a computer science student at BMS and later completed an executive MBA at IIM Bangalore. After more than two decades at Wipro, I had seen technology from several angles: engineering, customer delivery, team building, and business leadership. That experience gave me both the confidence and the motivation to become an entrepreneur.

Working in the services industry exposes you to a wide range of business problems. Over time, my co-founder, Mohan Bethur, and I saw an opportunity to build useful products, solve customer problems more directly, and create meaningful opportunities for early-career engineers. That became the foundation of NeuAlto.

What does the name NeuAlto mean to you?

The name brings together the idea of the new — "Neu" — with "Alto," the Spanish word for high. For us, it represents reaching new heights. It reflects our ambition to grow through work in artificial intelligence, cybersecurity, cloud engineering, and product development.

How did your years at Wipro shape you?

A large organization can offer extraordinary breadth. I worked onsite with customers in the United States and Canada and moved across several technical domains. I began in telecommunications, when optical networking was at the cutting edge, and later moved into embedded systems. Our teams developed software for cable modems, internet routers, and home-networking stacks, including technology relevant to connected devices such as remote CCTV systems.

I also spent a significant part of my career in the semiconductor vertical. The work ranged from enterprise applications and engineering portals to chip design and verification for semiconductor customers, including Texas Instruments. Although I stayed with one organization for many years, the technologies, customers, geographies, and responsibilities kept changing. That variety was an education in itself.

Building NeuAlto

What opportunity did you see in the market?

As large services companies grow, their processes naturally become heavier. Smaller customers and emerging product companies can struggle to receive attention when their projects are below the commercial thresholds of very large providers. We saw room for a focused engineering partner that could move quickly, stay close to the customer, and still bring enterprise experience.

Our work with companies such as Nirmata reinforced that model. NeuAlto contributed software development and quality engineering talent, including Kubernetes expertise. We have applied the same product-engineering mindset in cloud, AI, cybersecurity, automation, and electronic data interchange. In EDI, for example, platforms such as Cleo bring together partner connectivity, APIs, file transfer, and workflow automation.

Why build DeltaMax and OptiMax?

Data has been a natural focus because it draws on our experience and addresses a recurring customer need. Data migrations and modern data pipelines can be difficult to monitor: records change, anomalies pass through rule-based checks, and teams spend too much time investigating failures. DeltaMax is designed to give organizations continuous visibility into data quality, flag anomalous behavior, and improve trust during migration and ongoing operations.

OptiMax addresses a different decision problem. Marketing information is often fragmented across channels, geographies, budgets, and customer segments. OptiMax is intended to help marketing and business leaders understand performance, identify valuable and active customer segments, allocate spend more intelligently, and connect marketing activity to revenue outcomes.

The larger goal is not simply to produce another dashboard. It is to shorten the distance between a signal and a useful decision.

Which customer problems are you most excited to solve next?

We are discussing OptiMax with organizations in retail, lighting, insurance, and financial services, including credit-card businesses. DeltaMax also has potential beyond conventional enterprise data. We are exploring how its data-quality and monitoring capabilities could support sensor-rich IoT environments and, over time, carefully governed use cases in life sciences and healthcare.

Those are areas where reliability matters. Expansion has to be driven by real customer requirements, strong validation, and the appropriate privacy, safety, and regulatory controls — especially in healthcare.

Leadership, talent, and culture

Which early experiences most influenced the way you lead?

In a services organization, people often develop along a technology path or a leadership and management path. I moved toward management, where I built teams and worked with customers across Asia-Pacific, Europe and the Middle East, the United States, and Canada. Leading across cultures and time zones taught me how to build confidence, create clarity, and keep teams aligned with customer outcomes.

What qualities do you look for when hiring engineers and technical leaders?

Technical foundations matter, and today I expect candidates to be familiar with AI and cloud technologies. But knowledge alone is not enough. Communication is essential because engineers interact frequently with customers, and energy and curiosity shows whether someone will keep learning when the technology changes.

For technical leaders, I also look for judgment: the ability to frame the real problem, make trade-offs, explain a decision, and help other people do their best work.

Why is it important to stay technically informed while managing a growing organization?

Customers are not looking for generic presentations. They want to know how technology applies to their problem, what makes NeuAlto different, and what outcome we can realistically deliver. That makes it important for a leader to stay close to developments in the market, keep learning, and remain connected to the engineers doing the work.

You do not have to write every line of code, but you must understand enough to ask good questions, recognize weak assumptions, and guide the customer toward a sound solution.

What advice would you give an engineer who wants to become a technology leader?

Build depth in a domain. AI can automate more of the mechanics of coding, but it cannot remove the need to understand customers, systems, constraints, and consequences. Engineers who combine domain knowledge with architecture, communication, and sound judgment will remain valuable even as the tools change.

Use AI as leverage, not as a substitute for understanding. The leader's role is increasingly to define the right problem, direct the tools effectively, and validate the result.

What keeps you motivated when building a company becomes difficult?

Winning customer work can be difficult, especially for a growing company. What keeps us motivated is continuing to build: improving DeltaMax and OptiMax, exploring cybersecurity opportunities, and giving the team challenging problems to solve.

A smaller organization can also be an unusually strong learning environment. Engineers often gain breadth and responsibility much faster because they work close to the product, the customer, and the business problem at the same time.

Who has influenced your leadership style?

I have been inspired by leaders who combined technology with institution-building: Azim Premji at Wipro, N. R. Narayana Murthy at Infosys, and Scott McNealy at Sun Microsystems. Each, in a different way, showed how technical vision, management discipline, and culture can shape an enduring organization.

What do you enjoy outside work?

Cricket was a large part of my early years. I was fortunate to play alongside three cricket stars who went on to represent India: Anil Kumble and Vijay Bharadwaj during my time at National High School and National College, and Sujith Somasunder at BMS.

These days I play badminton regularly during the week, and I spend my leisure time trekking and biking.

Watching technology reshape industries

What technical work from your early career remains memorable?

When I worked in Canada with Northern Telecom, fiber-optic networking was advancing rapidly. I contributed to software associated with high-capacity optical systems such as OC-48 and OC-192. The constraints were very different from today: memory and storage were limited, so software had to be efficient by design.

We also developed cable-modem software from the ground up and worked with standards such as DOCSIS, first issued by CableLabs in 1997. Regional standards were a constant feature of that era: North America and Europe often took different paths, and in mobile the split was between CDMA, which was widely deployed in the United States, and GSM, which became the European standard and then spread worldwide. On the computing side, Unix-based development and Sun SPARC systems were common. Those experiences taught me that constraints can produce disciplined engineering.

What changes in communications technology stand out most?

The smartphone is one of the biggest. It became the one device people carry all day, combining communication, computing, photography, navigation, and entertainment. Companies that adapted slowly to that convergence lost ground. Nokia's transition illustrates the difficulty: it moved from Symbian toward a strategic partnership with Microsoft in 2011, making Windows Phone its primary smartphone platform, but the competitive ecosystem was already moving quickly.

Satellite communications offer another lesson in timing and business models. Motorola-backed Iridium launched commercial service in 1998, but the original company entered bankruptcy the following year as terrestrial cellular networks became cheaper and more convenient. The network survived under a new business model and later grew successfully. What strikes me is how early the idea arrived: much of what satellite constellations such as Starlink are doing now was attempted, in its own form, decades before the market was ready for it. The technology was remarkable; market readiness and economics were decisive.

Telecom economics changed just as dramatically. When I first traveled to Canada in the late 1990s, an international call could cost around three dollars per minute. Calling cards brought prices down, and internet-based communications eventually made the marginal cost feel close to zero. Companies that once defined the sector disappeared or changed fundamentally. AT&T was one of the largest carriers of that era, and its research arm, Bell Labs, was a place engineers were genuinely proud to work — an institution credited with tens of thousands of patents and a series of Nobel Prizes. Bell Labs was separated from AT&T in the 1996 Lucent spin-off and today sits within Nokia. The names have survived in one form or another, but the institutions themselves are not what they were.

What do those transitions teach today's leaders?

Technical excellence is necessary, but timing, distribution, economics, and customer behavior matter just as much. A company can be early with a strong idea and still struggle; another can arrive when the ecosystem is ready and scale quickly.

The practical lesson is to keep watching how people actually use technology. Assumptions that once sounded sensible — about storage, cameras in phones, or how people would pay for communication — can become obsolete very quickly.

AI, engineering, and the next chapter

How is AI changing your own work?

AI already helps me prepare reports and analyses that once took hours or even days. In some cases, the same work can now be completed in minutes. That is a meaningful productivity gain, but the result still depends on the quality of the instructions, the source material, and human review.

Will AI eliminate software-engineering jobs?

It will certainly change the shape of the work. A smaller team may be able to produce what previously required many more people, particularly for routine implementation. But AI still needs direction, context, and validation. Someone must define the requirement, make architectural decisions, examine edge cases, and decide whether the result is correct.

In fields such as electronic design automation, where errors can be expensive, human judgment remains essential. My view is that organizations should prepare for substantial productivity gains and role changes rather than assume that every engineering role simply disappears.

Where do you see AI having the greatest impact?

AI can be valuable wherever people must interpret large, complex datasets: engineering, cybersecurity, operations, marketing, and medicine. In healthcare, AI-enabled tools are already being evaluated and authorized for tasks such as image analysis and clinical decision support, while robotic systems assist with procedures.

The opportunity is significant, but healthcare also makes the limits clear. AI output must be clinically validated, used within its authorized purpose, protected by strong data governance, and reviewed by qualified professionals. Faster analysis is useful only when it is also safe, explainable, and reliable.

Where would you like NeuAlto to be five years from now?

I would like NeuAlto to be recognized as a trusted AI and engineering company: one that helps customers select the right technology, build the right systems, and solve problems quickly without compromising quality. The goal is not to chase every trend. It is to combine deep engineering, domain knowledge, and practical execution so that emerging technology produces measurable value.