226: Why VCs regret not investing in Acceldata - From IIT to Silicon Valley

Enterprise AI doesn’t fail because models are weak. 

It fails because data pipelines break, permissions drift, costs explode, and governance is treated like an annual checkbox instead of everyday operational discipline.

In this episode of Intelligent Indians!, Vikram Vaidyanathan sits down with Rohit Choudhury, Founder & CEO of Acceldata, for a deep conversation on how enterprise data systems actually behave at scale and why AI makes every weak foundation visible.

Rohit’s founder journey mirrors the last 15 years of data infrastructure: from scaling InMobi in the pre-cloud era, to the Hadoop days at Hortonworks, to pioneering data observability, and now pushing into Agentic Data Management as enterprises move from AI pilots to production.

The conversation goes deep into:

1. Why AI POCs often work in one team but collapse at enterprise scale 
2. The real cost of fixing data late, and why ingestion is the only place to fix it
3. How AI agents change the stakes: real-time failures, hallucinations explainability and ai updates
4. A practical playbook for CIOs and CDOs: SLAs, rapid experimentation, and leadership upskilling
5. Why cost control is becoming existential, from reruns to cloud bills to private/hybrid AI

The “India edge” in building global enterprise companies, and what DPI × AI could unlock next

Watch the full episode to understand what it really takes to make AI work at enterprise scale.

Chapters:

00:00 Why AI POCs Fail at Scale in Enterprises
02:30 Introducing Rohit Choudhury (Acceldata Founder)
05:30 Early Days at InMobi & Rewriting the Stack 3 Times
08:30 First Startup Failure & Learning Customer Obsession
11:30 Birth of the Data Observability Category
14:30 How Enterprise Data Pipelines Really Work (1000+ Apps)
18:00 Why Enterprises Need End-to-End Data Visibility
21:00 COVID Story & Missed Investment Moment
24:00 Why hallucinations are dangerous for enterprises
27:00 Governance in AI
30:00 How Banks Should Prepare for AI at Scale (4 Steps)
33:30 Why Leaders Must Learn AI Themselves (Cursor Story)
36:30 From Tool Sprawl to One Agentic Platform
39:30 Cost Explosion: Cloud vs Private AI
42:30 Self-Healing Data & Preventing AI Hallucinations
45:00 – Future of Enterprise AI & Closing Thoughts

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