AI Deployment Crisis: Why 70% Projects Fail But One Bengaluru Startup Found a Way
New Delhi [India], August 30: Artificial Intelligence is often discussed in terms of smart algorithms, powerful models, and futuristic promises. However, very few people know that behind the scenes, the industry struggles with a challenge. This challenge is deployment, and it rarely makes headlines. Studies suggest that nearly 70% of enterprise AI projects never make it past the demo stage. These projects may look impressive in labs but collapse when faced with real-world users. This quiet crisis is now defining the difference between companies that rise and those that disappear. And in this narrative, a small team, which is Bengaluru-based, has emerged as a curious anomaly.
In Bengaluru, two 22-year-old founders, Manas Bhasin and Aditya Singhal, started a platform called Echidna AI Foundry. Their work stands out because it powers more than one million AI operations daily across 50+ companies. Unlike the global race for building smarter algorithms, their focus is on infrastructure. It is a thing which is less glamorous but a more critical backbone. With a deployment success rate of 94%, they challenge the industry norm of failure. This shows that the future of AI may not belong only to those designing models but also to those solving the question of how to make them work at scale.
The shift becomes clearer when you look at how client companies benefit. Before using the platform, engineering teams spent almost 65% of their time just maintaining basic AI systems. After implementation, their focus on innovation jumped from 35% to 78%. This change is not a minor win but an existential one in a competitive environment where speed of iteration matters most. AI success is no longer about who builds the smartest brain but about who can keep that brain alive and working for millions of end-users every single day.
Each of the 50+ companies using Echidna AI Foundry serves thousands of end-users. Together, nearly 10 million people across the world indirectly interact with systems quietly powered by this infrastructure. When a client upgrades fraud detection, millions experience safer banking instantly. This is the unseen effect of infrastructure. Small fixes at the base create massive benefits at the top. History offers a lesson here, too. Cloud computing rose to dominance not because of the flashiest servers but because players like Amazon solved scale and reliability, enabling others to innovate freely. The same cycle is repeating now in AI.
The industry still pours huge resources into research breakthroughs, but the critical layer that makes those breakthroughs usable remains underfunded. As co-founder, Bhasin says, everyone dreams of building advanced research, but fewer are willing to invest in “boring” infrastructure. Yet, this is where real business value hides. The winners of AI’s next phase may not be the ones with the smartest models, but the ones ensuring those models can work reliably. Bengaluru’s hidden players highlight this shift, showing how infrastructure is quietly becoming the new profit engine of Artificial Intelligence.
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