Krishna Kumar Tiwari

Krishna Kumar Tiwari

Co-Founder & CTO

Whilter AI

Krishna Kumar Tiwari is the Co-Founder & CTO of Whilter AI, a GenAI-powered platform transforming hyper-personalized marketing at scale. Named among the 40 Under 40 Data Scientists by Analytics India Magazine in 2020, Krishna brings over 15 years of experience in building AI-first products and platforms.

He has held key roles at leading global organizations including IBM Research, Oracle, Samsung R&D, InMobi, and the Jio AI Center of Excellence, contributing to impactful innovations in enterprise AI and digital ecosystems.

An alumnus of IIT Guwahati, Krishna also serves as a Mentor of Change with the Atal Innovation Mission, NITI Aayog, supporting India’s next generation of tech innovators.

Generative AI is moving fast — and it’s no longer just about writing ad copy or creating visuals. We’re now entering an era where AI agents can think, decide, and create at scale, transforming how brands connect with their customers.

In this talk, We’ll see how we can use GenAI and agentic systems to take marketing from one-size-fits-all to millions of personalized creatives, tailored for individual personas, channels, and moments — all in real time.

In this session, we will walk through:

  • What makes an AI “agentic” — and why it matters for marketers
  • How CRM data + LLMs = dynamic storytelling for every user
  • The tech stack powering autonomous campaign generation and optimization
  • Real-world results from brand campaigns — what worked, what didn’t
  • Practical challenges like latency, hallucination, and control — and how to tackle them

Whether you are building AI products, running marketing ops, or just GenAI-curious — this session will leave you with real-world insights, architectures, and ideas you can take back to your team.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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