The Ravit Show

Ravit Jain

The Ravit Show aims to interview interesting guests, panels, companies and help the community to gain valuable insights and trends in the Data Science and AI space! The show has CEOs, CTOs, Professors, Tech Authors, Data Scientists, Data Engineers, Data Analysts and many more from the industry and academia side.

We do live shows on LinkedIn, YouTube, Facebook and other platforms. The motto of The Ravit Show is to the Data Science/AI community grow together!

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October 8 · 15 min
From AI Pilots to Production: How Salesforce and Altimetrik Are Scaling Agentic AI
Last week Dreamforce was a blast!!!! 2024 was the year of AI pilots. 2025 was the year agents went into production. 2026 is about scaling them. That framing came up in my Dreamforce conversation with Vala Afshar, Chief Digital Evangelist at Salesforce, and Gurvinder Singh Sahni, CMO at Altimetrik on The Ravit Show. Some numbers from the discussion: Dreamforce 2026 brought 50,000 people in person from 150 countries, with 10 million tuning in for the keynote. The event generated over $100 million for the San Francisco Bay Area economy and supported 60,000 local jobs. Across 400 early adopting companies studied over 15 months, average agent usage grew from 5 to 15, deployment time dropped to under a week, and utilization went up 4x. What stayed with me: Vala called AI the most important technology of our lifetime, the electricity of the 21st century. The pressure on businesses now is to actually become agentic enterprises, not just talk about it. The hard part is what comes next. Going from dozens of agents to hundreds or thousands turns governance, security, compliance, accountability, and trust into the real problem to solve. On keeping up with the pace, the advice was to stay a beginner. Ask questions, be radically transparent, and build a culture where people can experiment without fear of being blamed when something does not work. And none of this happens alone. Strong engineering partnerships and ecosystem collaboration, like the one between Salesforce and Altimetrik, are what make these transformations land. Full interview below. Thank you Vala and Gurvinder for a great conversation. #dreamforce #salesforce #agentforce #altimetrik #ai #aiagents #enterpriseai #theravitshow
October 7 · 10 min
From AI Ambition to Business Outcomes | Emerson & Altimetrik on Enterprise Transformation
Most enterprises have already spent heavily on Salesforce. The real question now is how to get more value out of it. That was the heart of my conversation at Dreamforce with Bryan Schappell from Emerson and Nripendra Tamang from Altimetrik. We got into what it actually takes to move Salesforce and Agentforce from pilots into production, and where AI is starting to change how companies engage customers and run their operations. A few things worth watching for: - How Emerson's Salesforce journey has evolved, and the business priorities shaping what comes next - Where Agentforce and generative AI can create real impact in their industry, beyond the hype - The capabilities that matter most when taking AI initiatives out of experimentation and into day to day operations - How Altimetrik brings together Salesforce expertise, AI native engineering, industry knowledge, and its partner ecosystem to help enterprises get there. And Bryan's 1 recommendation for business leaders planning the next phase of their Salesforce and AI journey. If you are leading a Salesforce or AI initiative right now, this one is worth your time. Thank you Bryan and Nripendra for a great conversation. #dreamforce #salesforce #agentforce #altimetrik #emerson #ai #enterprise #theravitshow
October 6 · 15 min
How Salesforce Is Building the AI-Ready Data Foundation for Agentforce
Most enterprises are not held back by AI models. They are held back by their data. That was the focus of my conversation with Savinay Berry, EVP and GM of Data Management, at @Salesforce’s Dreamforce. #AdSavinay also highlighted common data readiness mistakes. We explored what AI-ready data means for Salesforce customers and why enterprises need an AI infrastructure layer built to outlast today’s AI wave. As Savinay put it, “I don't know the agents that our customers are going to create in the future, but I want a canvas, a platform, a harness that I can have which is truly compliant, secure and protected. And there's probably going to be only two or three of them as it happened in case of the cloud.” We closed with an example of AI-ready data driving change at scale. Worth a listen for teams getting started. Thank you, Savinay. Full interview is live. Watch it and tell me what stood out to you. #df26 #salesforce #datacloud #agentforce #datamanagement #aireadydata #data #theravitshow #SalesforcePartner
October 2 · 9 min
How SharkNinja Is Transforming Customer Experience With AI, Salesforce and Agentforce
AI is moving out of the back office and into the products people use every day. That was the thread running through my conversation with Velia Carboni, CIO of SharkNinja, here at Dreamforce on The Ravit Show. #sponsored SharkNinja is taking AI beyond internal productivity and into its products and customer experiences. That shift changes what a CIO is responsible for. Technology leadership now sits much closer to the customer. We talked about where Salesforce and Agentforce fit into the way SharkNinja connects with its customers. We also got into speed. SharkNinja launches new products across global markets, and technology has to keep pace with that. And of course, what Velia is hearing on the ground at Dreamforce this year. The energy here is unreal. Conversations like this one are exactly why I love being at Dreamforce. Thank you, Velia, for a great conversation. #dreamforce #salesforcepartner #salesforce #agentforce #sharkninja #ai #cio #customerexperience #theravitshow
October 1 · 32 min
What Is CUBIG? The AI-Ready Data Operating Layer
Most companies don't have a data problem. They have a data readiness problem. They already have plenty of data. What they don't have is data an AI task can actually use. I sat down with Ho Bae, CEO of CUBIG on The Ravit Show, to talk about what it takes to make enterprise data ready for AI. Here are my takeaways: 1. Having data and having AI-ready data are 2 different things. The right information may already be in your systems, but it often needs to be prepared, changed, or validated before AI can use it. 2. "AI-ready" depends on the task. Data isn't ready in general. It's ready for a specific job. 3. Real enterprise data is messy in ways sample data isn't. Circuit IDs, project codes, and node names only make sense because of how they connect inside the business. 4. Removing too much data can break the AI. If you strip out the relationships, the model can't tell that 2 records belong to the same asset or event. 5. The answer should come back inside the workflow. LLM Capsule sits inside the tools teams already use and returns results with the real business values, ready to act on. 6. You can keep your current model. The model stays your choice. The data layer around it is what changes. 7. Start with 1 blocked AI task, not a product checklist. Know what you want to run, what's stopping it, and what success looks like. Ho also walked through a live demo, showing how 2 network alerts get traced back to the same device without the model ever seeing the original device name. If your AI works on sample data but struggles on real data, this one is worth your time. CUBIG will also be at Oracle AI World 2026 in Las Vegas, October 25 to 28. #data #ai #enterprisedata #dataengineering #genai #llm #cubig #theravitshow
September 28 · 59 min
Progress Data Platform: Building the Foundation for Trusted AI
What does it actually take to make AI work inside the enterprise? I recently sat down with Philip Miller from Progress to talk about the challenges companies are facing as they move AI from experiments into real production environments. We talked about Progress Data Platform, PDP Studio, context layers, AI guardrails, data governance, AI agents, cost optimization and what enterprises need to build AI they can actually trust. We also discussed a real-world use case where report-writing time was reduced from around 90 minutes to just minutes. One thing that stood out to me: AI is not just about the model. The data, context, governance and infrastructure around the model matter just as much. #Progress #EnterpriseAI #AI #Data #AI #API #Agents #GenerativeAI #theravitshow
September 25 · 3 min
How Equinix Is Building the Interconnection Layer for the AI Era
Last week, I had a blast at 1st Equinix Horizon. Sat down with Paul Dehnert, Vice President of Sales at Equinix on The Ravit Show. He runs the global sales team behind Equinix's interconnection portfolio, the internal wiring inside data centers that quietly makes up the network of networks we call the internet. Sharing the takeaways while they are fresh. The theme running through this entire event is open neutrality, and Paul connected that directly to what is happening in AI and inference right now. Together AI is bringing its inference platform onto Equinix to make open source models easier to run. Some hyperscalers are rolling out new interconnection capabilities on Fabric One that let developers connect directly using natural language instead of manual network configuration. The line that stood out to me most was about enterprise spend. A lot of companies burned through their token budgets earlier this year and are now actively optimizing. Paul's read is that enterprises are getting more mature about this. They are realizing they do not need the frontier model for every use case. The real skill now is picking the right venue and the right model for the job, not defaulting to the biggest one available. That is a quieter shift than the big keynote announcements, but it might matter just as much for how enterprise AI actually gets budgeted and run. More conversations from the summit floor coming soon. #equinix #equinixhorizon #ai #interconnection #enterpriseai #cloud #digitalinfrastructure #openneutrality #theravitshow
September 24 · 9 min
How Equinix Is Helping Enterprises Build AI That Is Secure, Scalable and Cost-Effective
Great conversation from Equinix Horizon, this time with Kaladhar Voruganti, VP and Senior Technologist on the Global Value Advisory Team at Equinix on The Ravit Show. He runs field CTOs and 20 global solution validation centers, basically AI labs where customers and partners go to test what actually works. Sharing the takeaways while they are fresh. Most enterprises are not chasing exotic AI use cases. Coding, customer support, and employee productivity are where the real adoption is happening right now. And the architecture pattern Cal is seeing repeatedly is hybrid. Big cloud models for the hard problems, open models on private infrastructure at Equinix for everything else, mainly for cost and control. The part that stuck with me was how enterprises are managing AI spend and governance. AI gateways act as traffic routers, sending simple queries to simpler models and complex ones to the cloud. Semantic caching alone is knocking out 20 to 60 percent of redundant queries before they ever hit a model. And AI firewalls are showing up at neutral interconnection points to scrub traffic before it reaches applications. This is the unglamorous plumbing that decides whether enterprise AI is actually affordable. His advice for leaders was blunt in a good way. Centralized data lakes do not hold up for agentic AI, so shift to a federated approach that takes the query to the data instead of the other way around. Do not rush to prove ROI or cut headcount early. Let people get comfortable with AI first. And follow the sequence: experiment, then govern, then move to private AI clusters for cost control. Kaladhar and his team publish weekly on networking, data strategy, governance, and industry verticals. Worth a follow if you are building any of this yourself. More conversations from the summit floor coming soon. #equinix #equinixhorizon #ai #enterpriseai #datastrategy #aigovernance #digitalinfrastructure #equinix #theravitshow
September 23 · 3 min
How Equinix Is Building the Infrastructure for the Agentic AI Era
Always a blast chatting with Harmeen Mehta, Chief Digital and Innovation Officer at Equinix, live from Equinix Horizon on The Ravit Show. It is always fun meeting her and chatting with her, she is one of the best. Sharing the biggest takeaways while they are still fresh. Equinix Horizon itself is being positioned as an iconic customer event, built around co-creation, not just announcements. That framing matters and it showed in how Harmeen talked about the news. On Inference Exchange, the 360 degree partnership with Together AI and NVIDIA, her framing stuck with me. AI is not staying in a lab or a dashboard. It is going to meet us and come to work for us every single day, through agents exchanging intelligence in the background. That is a very different picture of AI adoption than most enterprises are planning for right now. On Fabric One, she broke it down simply. Anything to anything, anywhere to anywhere, connected in hours or minutes instead of weeks. When your infrastructure partner talks about connectivity in minutes, that changes what enterprise IT teams can actually promise the business. The line that stayed with me most was about velocity. Change across this industry is accelerating because of AI, and organizations have to evolve how fast they decide and execute, not just what they decide. Her advice to enterprise leaders was direct. Embrace the velocity. The decisions made today are what will separate companies tomorrow. Great conversation, great energy on the ground here at Equinix Horizon. More from the summit floor soon. #equinix #equinixhorizon #ai #leadership #innovation #digitalinfrastructure #cloud #aiagents #theravitshow
September 22 · 12 min
How DaVita Is Scaling AI in Healthcare With Glean
This one is so so important!!!! I spoke to with Madhu Narasimhan, CIO at DaVita Kidney Care, at Glean:GO 2026 in San Francisco on The Ravit Show. Most companies ask whether AI is good enough to scale. A healthcare CIO has to answer a harder question first. What happens when it is not. That difference ran through the whole conversation. DaVita operates at real scale in kidney care. When you are that close to patient care, the distance between a promising pilot and something you put in front of clinical and operational teams is not a technology gap. It is a trust gap, and healthcare defines that word more strictly than most industries do. Here is what we got into. - What has to be true before you scale AI across healthcare. Not the model benchmark. The conditions underneath it. This is the question I wish more AI conversations started with, because in a regulated environment the preconditions are the strategy. - Why connected company knowledge decides whether AI is useful at all. An assistant that cannot see how your organisation actually works will confidently give you an answer that is wrong in a way nobody catches. Scattered knowledge is not an inconvenience. It is the failure mode. - Where AI is creating real value at DaVita today. We stayed on what is actually running, not what is on a roadmap slide. That is a rarer conversation than it should be. - What role Glean plays in the broader AI strategy, and where a context layer sits relative to everything else being built. - What moves employees from trying AI to genuinely trusting it. This was my favourite part. Adoption is not a licensing problem or a training problem. People start trusting a system when it is right about something they already know the answer to, repeatedly, inside their own workflow. That is much harder to engineer than a demo. - And what will separate the companies compounding real value from the ones sitting on 40 disconnected pilots. My own view going in was that the dividing line is knowledge, not models. I came out more convinced. Madhu, thank you for the time at Glean:GO. Conversations with operators running this at real scale are the ones I learn the most from. Worth your time if you are trying to move AI out of pilot phase in a regulated industry. If you are inside healthcare, finance or another regulated environment, what had to be true before you scaled AI beyond a pilot? #data #ai #enterpriseai #healthcareai #cio #aiadoption #digitalhealth #aigovernance #theravitshow
September 21 · 19 min
Glean’s Product Strategy: Building AI That Understands Your Company
Your dashboard tells you revenue dropped 8% last quarter. It cannot tell you why. The why is sitting in a Slack thread, 3 support tickets, and a deal review nobody wrote up properly!!!! That split is the most underrated problem in enterprise AI, and it is what I got into with Jayanth Mysore, Product Manager at Glean, at Glean:GO on The Ravit Show in San Francisco. Jayanth co-founded Iris, an AI analyst that sat directly on the cloud data warehouse and answered questions in plain language with SQL behind it. Before that he was an early product leader on Google Analytics and VP of Product at Sigma Computing. He has spent a career on the structured side of this problem. Iris is now part of Glean, whose strength has been the opposite half, the documents, conversations, and tickets where the reasoning lives. Most enterprise AI still picks one side. You get a tool that queries your warehouse but has no idea what your company decided last month, or a tool that reads every document but cannot count. Neither one answers the question an executive actually asks, which is what happened and why. We talked about what it really takes for AI to understand a company rather than generate plausible answers, where enterprises still underestimate context, how to decide what AI automates versus where a human stays in control, and what customers are asking for now that they were not asking for a year ago. #glean #enterpriseai #ai #dataandai #artificialintelligence #analytics #cdo #theravitshow
September 18 · 17 min
The Next Era of Enterprise AI: Context, Agents and Glean Tau
The model is not the expensive part of your AI stack. The missing context is. That is the argument at the centre of everything Glean announced this week, and I got into it with Emrecan Dogan, Chief Product Officer at Glean, on site at Glean:GO on The Ravit Show in San Francisco. Here is why it matters beyond one vendor's launch. When AI does not know how your company works, every task starts from zero. It hunts for files. It asks for background. A person stops what they are doing and feeds it the same context again. Glean's research puts that at 6.4 hours a week per worker, which is most of a working day spent supervising a tool that was supposed to save time. That is also why the productivity numbers keep disappointing executives. 75% of workers say AI makes them faster. Only 13% say their company is performing better because of it. Individual speed is not organizational output, and no amount of model upgrade closes that on its own. So the interesting question is not which model is smartest this quarter. It is what your AI already knows about your business before you ask it anything. Emrecan and I covered where that leaves enterprise buyers, what happens to AI spend as agents start doing multi step work, and how teams keep a growing pile of AI tools from turning into sprawl. #data #ai #glean #enterpriseai #ai #artificialintelligence #cio #agenticai #theravitshow
September 17 · 41 min
The Great AI Re-Architecture: Why Every Enterprise Needs to Rethink Data for AI
AI adoption is moving faster than most enterprise data architectures can handle. I just sat down with Sergio Gago, CTO, Cloudera on The Ravit Show to discuss what their latest global survey reveals about the state of enterprise AI. The survey covers 1,500 enterprise architects, cloud infrastructure leads, and data architects across 9 markets. A few findings stood out: * 77% of organizations are already using AI * 72% say their current data architecture needs a significant overhaul to meet their AI goals * 95% have delayed or cancelled AI projects because of data governance, compliance, or regulatory challenges * 84% say AI workloads have increased infrastructure costs * 66% have moved AI workloads from public cloud back to private cloud or on-premises The bigger story is that AI is forcing enterprises to rethink where data lives, where AI workloads run, how governance works, and how to balance cost, performance, security, and flexibility. That's what Sergio and I discuss in this conversation. The interview is now live across all channels. #data #ai #cloudera #architecture #theravitshow
September 11 · 14 min
Artemis in Action
We built a working AI agent in 5 minutes. Not a demo. Not a prototype. A live agent managing my inbox. I sat down with Akshhat at the Kore.ai office in Hyderabad, and one thing became very clear. The prototyping era is over. Organizations in healthcare and banking, some of the most regulated industries on the planet, are now deploying 50 to 100+ agents to run complex, real-world workflows. This is production, not experimentation. But here is what surprised me most. You do not need to be a massive enterprise to do this. As a content creator, my biggest bottleneck is a flooded inbox. So Akshhat challenged me to build an Inbox Assistant Agent on the new Kore.ai Agent Platform, the Artemis edition. Here is how we did it in 5 minutes with zero code: - We started with Arch, the AI agent architect. Plain natural language commands. No coding. - We uploaded my existing SOP document directly into the chat. The platform ingested it, broke down the requirements, and structured the architecture on its own. - It designed a multi-agent topology. An Inbox Agent to read and draft responses. A Reviewer Agent to enforce quality control before anything goes out. - Governance was built in from the start. Deterministic guidelines and custom guardrails keep the agents from hallucinating or going off-script. - Before deployment, the platform automatically ran 100 test conversations to benchmark safety, accuracy, and responsiveness. Evaluation first, deployment second. - We connected my Gmail securely in seconds. The agent went live in the background. This is why analysts are paying attention. Kore.ai was just named a Leader in the 2026 Gartner Magic Quadrant for Conversational AI Platforms and a Leader in The Forrester Wave for Conversational AI. Very few vendors hold both. The paradigm has shifted. We are moving from test-driven development to autonomous execution with human escalation built in. If you can write out your business process, you can build an agent to run it. That is the takeaway. Thank you Akshhat and the Kore.ai team for the walkthrough. Are you integrating agentic workflows into your daily operations yet? Let's discuss in the comments. #aiagents #agenticai #koreai #enterpriseai #conversationalai #generativeai #dataandai #theravitshow
September 10 · 4 min
Inside Kore.ai's Agentic AI Architecture: A Hyderabad Office Visit
900 people. Two floors. One question I kept asking everyone at Kore.ai's Hyderabad office: what happens when the AI is wrong. The answer I got back, again and again, is why I think this company is built differently. Most companies bolt AI features onto old infrastructure. Kore.ai didn't. Santhosh Kumar Myadam, who has been there 9 years, told me they rebuilt the entire stack from scratch to stay model ready. Product owners get an AI architect. Developers stay inside their own coding tools using MCP. CXOs get one screen to see every agent running across the company. Sriharsha Nalluri showed me Arch, their AI co-pilot for building agents. You can describe what you want in plain English, or hand it an SOP document and let it work from that. It runs its own testing loops. Simple workflows hit 90% production readiness in 10 to 20 minutes. I built one myself. It was easier than I expected. Prathyusha G. and Spandana Kodali walked me through the harder problem: getting AI to work in regulated industries. Their answer is what they call governed autonomy. A reasoning engine handles the thinking. A separate deterministic engine enforces the rules. That combination is what convinces banks and hospitals to trust AI with real decisions. Girish Ahankari talked about what actually breaks agent projects at scale: prompt chains that grow to 400 lines and become impossible to audit. Their blueprint language compresses that down to 50 lines anyone can read. Built in PII redaction and bias checks come standard. Deployment timelines drop from months to weeks. Abhijit Mhetre summed up why the company has lasted. 12 years in this space, named a Leader in the Gartner Magic Quadrant four times running. The lesson from this visit: the companies winning in agentic AI aren't the ones with the most features. They're the ones who rebuilt their foundation early enough to keep up. #data #ai #enterpriseai #agenticai #generativeai #aiorchestration #koreai #theravitshow
September 9 · 1 hr 26 min
Arun Jain on Enterprise AI, Purple Fabric, Banking and Building a Global Product Company
Very few people in the world can say banks trust them with their core. Arun Jain is one of them. He is the man behind Intellect Design Arena, the force who helped put India on the global fintech map, and one of the most influential product minds this country has ever produced. Founders study his playbook. Banking leaders across continents run on his technology. And an entire generation of Indian entrepreneurs builds on the path he cleared decades ago. I sat down with him at Intellect’s Chennai headquarters. The full conversation is now LIVE on The Ravit Show. In this conversation, he breaks down: - Why most enterprise AI stays stuck in pilots, and the operating model shift that fixes it - Business Impact AI, measured in outcomes, not demos - How Purple Fabric embeds AI into the core of regulated banking, not on top of it - The thinking behind eMACH.ai and why composable architecture is no longer optional - Why India’s next decade belongs to product builders, not service providers - The leadership mistakes that changed him, and what success means to him now There are very few people who have seen every technology cycle in banking and stayed ahead of all of them. Arun Jain is one of them. This one is for the builders. #data #ai #purplefabric #intellectdesignarena #theravitshow
September 8 · 38 min
The Future of the AI SOC: What Black Hat Revealed | Monzy Merza
What happens when the AI SOC becomes more expensive, less private, and harder to control? We talk a lot about AI transforming cybersecurity. But there are some questions that don't get enough attention. Where does AI genuinely add value in the SOC? Does the cybersecurity industry actually have a talent shortage, or a productivity problem? What happens when security teams start paying for every alert and every token? And perhaps most importantly, should sensitive security telemetry really be sent to third-party AI models and cloud providers? I sat down again with Monzy Merza, CEO and Co-Founder of Crogl on The Ravit Show, to unpack these questions and discuss what he saw at Black Hat. We also went deeper into the future of the AI SOC and Crogl's new Sovereign AI SOC, including the trade-offs between AI capabilities, cost, telemetry, and data sovereignty. This is not just a conversation about AI in cybersecurity. It's about what the next generation of the SOC actually looks like. #data #ai #aisoc #soc #cybersecurity #api #crogl #theravitshow
September 7 · 44 min
Can AI Really Solve Alert Fatigue? | Monzy, CEO & Co-Founder of Crogl
Can AI actually solve alert fatigue, or are we expecting too much from it? Every security team wants faster investigations, fewer false positives, and less manual work. AI promises all of that. But the reality inside the SOC is far more nuanced. In my latest conversation with *Monzy, CEO and Co-Founder of Crogl*, we discuss: * What is actually working with AI in the SOC today * Why alert fatigue continues to overwhelm security teams * Whether AI is reducing the problem or simply changing it * How the role of security analysts is evolving * Where human judgment still matters in an AI-powered SOC * Why Crogl launched a free enterprise-grade AI SOC platform If you're leading security, building AI products, or simply trying to understand where AI is creating real value in cybersecurity, this conversation is worth your time. #data #ai #security #crogl #theravitshow
September 4 · 14 min
Why Generic AI Isn't Enough for Enterprise | Observe.AI CTO
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
September 2 · 21 min
The Future of Coding? Build Apps with AI No Code
Sat down with Mukund Jha, Founder and CEO of Emergent, on The Ravit Show at MongoDB.local Bangalore. Mukund is not new to building. He was part of the team that built Dunzo, founded startups before that, and has deep technical roots in ML and NLP. What he is doing now with Emergent is one of the most interesting vibe-coding stories happening right now. The company recently crossed $100 million in ARR, raised close to $200 million from Creaegis, Amazon, Ranjan Pai's Claypond, and others, and the numbers underneath are just as real as the funding. Here is what we got into. We started from the beginning. What Emergent actually is, the problem that made him want to start the company, and what it looks like in practice today. You describe what you want in plain English, and autonomous AI agents build, test, and deploy the full-stack app for you. Frontend, backend, database, hosting. All handled. The scale is hard to ignore. 10 million apps built across 190 countries. Deployment rates doubled in three months. Two thirds of power users are now taking complex apps live. This is not a demo product. This is a company that hit $100 million ARR eight months after public launch. We got into the database decision. Emergent tested PostgreSQL early on and ran into schema migration loops as agents tried to adapt apps while users kept changing requirements in real time. Mukund walked me through why MongoDB Atlas became the default for every app on the platform, and why the flexible document model maps naturally to how agents actually work. We talked about what is happening as more of these apps move from prototype to production. What MongoDB made easier that would have been much harder otherwise. And the patterns emerging in what people are building, which tell you something about where software is headed. Mukund gave the keynote at the event. I asked him what the one thing he wanted the room to walk away with was. His answer was clear and specific, and worth hearing from a founder who has already shipped at this scale. We closed on India. What the Indian builder community means to him, and why. A few things stayed with me. - The vibe-coding wave is not a toy. When your platform has 10 million apps live and the company just crossed $100 million ARR, the conversation shifts from whether this works to how it scales. - Schema flexibility is not a nice-to-have for AI-native products. It is the reason the agents can actually function when users change their minds every five minutes. - Some of the most interesting software being built right now is being built by people who do not call themselves developers. That changes things. #data #ai #mongodb #mongodblocal #theravitshow

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