How Freshworks Builds AI Agents That Actually Work

While most companies are stuck in AI proof-of-concepts (POCs), Freshworks has already deployed AI solutions to thousands of business customers. How did they do it?

In the premiere episode, Lior hosts Vishnu Parimi, Former Head of AI Products at business-software company Freshworks and a former leader at Microsoft Azure and AWS. Vishnu pulls back the curtain on the strategies and architecture that allow Freshworks to turn the promise of AI into an operational reality that generates a real return on investment (ROI).

In this episode, you will discover:

Vishnu’s view that AI is completely transforming internal support for IT and HR teams.
The method that allows AI to learn and improve without needing massive datasets.
The exact metrics Freshworks uses to measure tangible ROI from automation.
What the "AI Assembly Line" is, and why infrastructure is just as crucial as the models.
When it's the right time to use AI, and when it's better to pass (with a real-world example).

This is a must-listen episode for any manager, founder, or technologist who wants to move beyond the hype and understand how to make AI work in the real world.

00:00 Introduction to Vishnu Parimi & the AI Challenge
01:08 Vishnu's Journey from Microsoft & AWS to Freshworks
02:42 Will AI Replace Humans or Augment Them?
05:21 The Pressure to "AI-ify" Everything: How to Decide When to Use AI
06:46 Behind the Scenes: A Real-World Employee Support Use Case
10:22 The Architecture Under the Hood of Freshworks' AI
14:18 Building Guardrails: How the AI Handles Failure & Loops in Humans
16:56 The Bottom Line: How to Measure Real ROI from AI
20:13 The Human Factor: Driving Internal Adoption of AI Tools
21:30 The Biggest Barrier to AI Adoption (It's Not Technology)
24:23 From POC to Production: What It Really Takes
27:11 The "AI Assembly Line" Explained
34:33 Fireside Chat: Predictions on the Future of AI
35:09 When Will We See AGI?
35:53 What's Next for AI in the Enterprise?
39:21 Final Thoughts & Wrap-up

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