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DigiYatra has been designed to handle millions of users in real time.
For its identity mechanisms, DigiYatra has employed multi-source model training, incorporating diverse datasets that span different ethnicities, facial structures, and lighting conditions.
Optimizing API interactions and leveraging distributed computing has enhanced the ability to manage 100x the original traffic load.
Transformational ideas aren’t born out of buzzwords but from real problems and moments of frustration that demand a better way. For years, digital identity verification at airports remained a bottleneck—an inconvenience for passengers and a challenge for security agencies. Long queues, redundant document checks, and inefficient processes have plagued the system, leading to a frustrating experience for travelers.When Suresh Khadakbhavi, CEO of DigiYatra Foundation, took on the challenge of transforming airport authentication, he knew that a few process improvements wouldn’t be enough. The solution needed to be frictionless, scalable, and secure. Instead of identity verification being a barrier, it envisioned identity as a silent enabler—an invisible, frictionless process that passengers wouldn’t even notice, but one that would make their journey exponentially smoother.Selling a vision to skeptics
But how do you convince an ecosystem entrenched in legacy systems that facial recognition and biometric authentication are the future of air travel? »Security, efficiency, and customer experience—these aren’t opposing forces. They can coexist if we build trust in technology. We needed to shift the focus from ‘why this might fail’ to ‘what happens if we don’t do this,” he adds. The global aviation industry was already moving towards seamless identity verification. The question was no longer if but when—and who would lead it in India. Once the conversation shifted from risk aversion to competitive advantage, skepticism gave way to acceptance.Scaling AI for 30,000+ daily usersWhile similar biometric-based systems exist in international markets, such as CLEAR in the U.
