I had a blast chatting with Jithendra Vepa, CTO and Co-founder of Observe.AI, on The Ravit Show at MongoDB.local Bangalore. Jithendra has a PhD in speech technology and has spent years deep in speech recognition, NLP, and voice AI. He is not someone who got into AI because it became trendy. He has been building domain-specific AI systems long before the current wave, and it shows in how he thinks about the problem. Here is what we got into. -- We started with Observe.AI itself. What they are building, what problem they set out to solve, and why it matters for enterprises dealing with customer conversations at scale. Observe.AI is a contact center AI platform that helps businesses analyze customer interactions, coach agents in real time, and improve performance across support and sales. More than 300 organizations use it. They process millions of support touchpoints daily. -- That volume is where the database conversation gets real. I asked Jithendra what specifically made MongoDB the right fit for that kind of AI and data workload. When you are running models on millions of unstructured conversations every day, the database decision is not theoretical. His answer was practical and specific.We talked about what changes as enterprises move from AI pilots to real deployment. What MongoDB made easier for the Observe.AI team and for their customers that would have been much harder otherwise. This part is useful for anyone trying to figure out the gap between a working demo and a working product. -- We got into the wins and patterns that have stood out as Observe.AI has scaled. Customer outcomes, operational improvements, how teams are actually using the product once it is embedded. The patterns here tell you a lot about where enterprise AI is actually delivering value versus where it is still a slide deck. -- Jithendra gave the keynote at the event. I asked him what the biggest takeaway he wanted the room to leave with was. His answer came from someone who has built a 40 billion parameter contact center LLM and trains domain-specific models instead of relying on generic ones. That distinction matters more than most people realize. -- We closed on the signal versus hype question. His advice for founders and enterprise teams trying to decide where to place their bets right now was grounded in years of shipping, not months of experimenting. A few things stayed with me. Generic AI is not enough for enterprise. Domain-specific models built on domain-specific data is where the real moat lives. The companies winning in AI are not the ones with the most models. They are the ones with the most structured access to the right data at the right moment. Contact centers are one of the first places where AI is delivering measurable ROI at scale. What is happening there is a preview of what is coming for the rest of the enterprise. #data #ai #mongodb #mongodblocal #theravitshow
I had a blast chatting with Jithendra Vepa, CTO and Co-founder of Observe.AI, on The Ravit Show at MongoDB.local Bangalore. Jithendra has a PhD in speech technology and has spent years deep in speech recognition, NLP, and voice AI. He is not someone who got into AI because it became trendy. He has been building domain-specific AI systems long before the current wave, and it shows in how he thinks about the problem.
Here is what we got into.
-- We started with Observe.AI itself. What they are building, what problem they set out to solve, and why it matters for enterprises dealing with customer conversations at scale. Observe.AI is a contact center AI platform that helps businesses analyze customer interactions, coach agents in real time, and improve performance across support and sales. More than 300 organizations use it. They process millions of support touchpoints daily.
-- That volume is where the database conversation gets real. I asked Jithendra what specifically made MongoDB the right fit for that kind of AI and data workload. When you are running models on millions of unstructured conversations every day, the database decision is not theoretical. His answer was practical and specific.We talked about what changes as enterprises move from AI pilots to real deployment. What MongoDB made easier for the Observe.AI team and for their customers that would have been much harder otherwise. This part is useful for anyone trying to figure out the gap between a working demo and a working product.
-- We got into the wins and patterns that have stood out as Observe.AI has scaled. Customer outcomes, operational improvements, how teams are actually using the product once it is embedded. The patterns here tell you a lot about where enterprise AI is actually delivering value versus where it is still a slide deck.
-- Jithendra gave the keynote at the event. I asked him what the biggest takeaway he wanted the room to leave with was. His answer came from someone who has built a 40 billion parameter contact center LLM and trains domain-specific models instead of relying on generic ones. That distinction matters more than most people realize.
-- We closed on the signal versus hype question. His advice for founders and enterprise teams trying to decide where to place their bets right now was grounded in years of shipping, not months of experimenting.
A few things stayed with me.
Generic AI is not enough for enterprise. Domain-specific models built on domain-specific data is where the real moat lives.
The companies winning in AI are not the ones with the most models. They are the ones with the most structured access to the right data at the right moment.
Contact centers are one of the first places where AI is delivering measurable ROI at scale. What is happening there is a preview of what is coming for the rest of the enterprise.
#data #ai #mongodb #mongodblocal #theravitshow