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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Friday · 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
Thursday · 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
Wednesday · 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
Tuesday · 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
Monday · 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
September 1 · 16 min
What Every AI Developer Should Know Before Building at Scale
What does it actually take to build AI products at scale in India? That was the focus of my conversation with Shrey Batra, Head of Engineering, Platforms at HROne and Founder of Cosmocloud, during MongoDB.local Bangalore. We started with the latest MongoDB announcements, including voyage-context-4, Hybrid Search, Native Reranking, and the expansion of Search and Vector Search. But the discussion quickly moved beyond product launches. Shrey shared what it looks like to build and run production systems in India, the infrastructure challenges that most teams underestimate, and why getting the data layer right matters long before AI agents enter the picture. We also talked about: -- Building Cosmocloud and the lessons from running it in production -- Why Indian AI founders have a unique opportunity right now -- What being a MongoDB Champion really means -- Why developers should join MongoDB User Groups -- How HROne and Cosmocloud use MongoDB today -- Where AI agents are headed -- One technology trend that's overhyped and another that's not getting enough attention It was a practical conversation with someone who is building every day, not just talking about AI. #data #ai #mongodb #mongodblocal #theravitshow
August 31 · 15 min
India Needs 2 Million AI Builders. Here's MongoDB's Plan
Last week at MongoDB.local Bangalore, I sat down with Basavadarshan G N, or Darshan as most people know him, Senior Academia Partnership Manager for APAC at MongoDB, on The Ravit Show. Darshan sits at the intersection of two things I care a lot about. Developer education, and India's push to actually build for the AI era instead of just consuming it. His work with MongoDB for Academia is quietly one of the more important programs happening in Indian tech right now. Here is what we got into. - We started with the basics. What MongoDB for Academia actually is, who it reaches, and how it fits into the broader Indian developer landscape. If you have not looked at this program closely yet, this part is worth the time - We talked about the 650,000 students the program has already reached since 2023. That is not a small number. Darshan walked me through what has been driving the momentum, and why this moment felt right to double down and go bigger - We went into the big announcement from the event. MongoDB committing to upskill two million Indian builders by 2030. New curriculum in Kannada, Hindi, and Tamil. 1,500 plus institutions. 5,000 educators. I asked him what the actual roadmap to that number looks like, because two million is a promise that has to be earned, and he was clear about how they plan to get there - We spent real time on the language piece. Building curriculum in Kannada, Hindi, and Tamil is not a marketing move. It is an access move. Darshan's view on how big a barrier language has been for students outside the metros, and what opens up for them when that barrier drops, was one of the more grounded moments of the conversation. - We talked about foundational data skills too. The buzz right now is that AI is going to make technical skills more accessible. That may be true. But data skills are still the layer everything sits on, and Darshan made the case for why they are more important now, not less. - We got into the AICTE Virtual Internship Programme. What a student actually experiences, what they walk away with, and what they should be able to build after finishing it. A few things stayed with me. MongoDB is behind half of India's top 100 companies and 50 plus unicorns. Students trained on this stack are being prepared for the exact environment they will walk into on day one. The India AI story cannot be told without the education story. You cannot build two million careers on English-only curriculum. This is the real inclusion play. Two million by 2030 is not a slide. It is a plan with partners, institutions, languages, and a delivery model behind it. Worth watching. Full interview live now!!!! #data #ai #mongodb #mongodblocal #theravitshow
August 28 · 10 min
Everyone Is Building AI Agents Wrong. Here's What Actually Works
Last week at MongoDB.local Bangalore I sat down with Sejal Khanna, Senior Developer Advocate at MongoDB, on The Ravit Show. Loved hosting her!!!! Sejal spends her days helping developers move from AI curiosity to actually shipping. Workshops, hands-on sessions, product storytelling, community. She works at the layer where hype meets reality, which makes her one of the most useful voices to hear from right now. Here is what we got into. We started with what she is building and teaching right now. The volume of AI content out there is enormous. What she keeps finding herself filling in when she is working with builders directly is the gap between watching a tutorial and actually getting an agent to behave in production. We walked through the announcements from the day. voyage-context-4 GA. Hybrid Search GA. Native Reranking in public preview. Search and Vector Search shipping in Community Edition and Enterprise Advanced. Sejal broke down which of these she is most excited for builders to actually get their hands on, and why the Community Edition move might quietly be the biggest one for developers in India. We spent real time on her workshop, The A to Z of Building AI Agents. Reasoning, tools, memory, agent architectures, and then actually building one using MongoDB as memory, a Claude model, and LangGraph for orchestration. She walked me through what she hopes people walk away able to do, which is a lot more concrete than the average AI workshop pitch. #data #ai #mongodb #mongodblocal #theravitshow
August 27 · 19 min
AI Isn't the Problem. Your Data Architecture Is.
Last week at MongoDB.local Bangalore I sat down with Pete Johnson, Field CTO for AI at MongoDB, on The Ravit Show. Pete is a good friend and one of the most honest voices I know in this space. We covered a lot of ground. We walked through the announcements from the day. - voyage-context-4 going generally available. - Native Reranking hitting public preview. - Hybrid Search now GA. - Search and Vector Search shipping in both Community Edition and Enterprise Advanced. Pete broke down why retrieval quality has quietly become the most important AI conversation in enterprises right now, and what a real improvement in retrieval actually changes for teams trying to move from pilot to production. We spent real time on agentic AI. What is working, what is still slideware, and why the teams shipping agents in production are almost always the ones who solved the data layer first. We got into the message Pete brought to the general session. AI in production is a data problem, not a model problem. Simple line. Explains why so many enterprise AI programs stall. #data #ai #mongodb #mongodblocal #theravitshow
August 26 · 12 min
What It Actually Takes to Win at AI Transformation
Wow!!!! Loved hosting Erica Volini, Chief Customer Officer at MongoDB, here at MongoDB.local Bangalore on The Ravit Show. Erica has had a front-row seat to some of the biggest enterprise shifts of the last two decades. Deloitte. ServiceNow growing from 1.5 billion to over 10 billion in revenue. And now MongoDB at the center of the AI moment. So when she talks about how companies actually navigate change, you listen. Here is what we got into. I asked her how this AI moment compares to the transformations she has seen before. Her answer was honest. Faster, messier, and the gap between leaders who are experimenting and leaders who are deploying is wider than people realize. We talked about what she is actually hearing from enterprise leaders right now. Where they are excited, and where they are stuck. The stuck part was the more interesting half. We spent real time on India. MongoDB is behind half of India's top 100 companies and more than 50 unicorns. I asked her what that signals about where India is headed as an AI market. Her read on the speed of adoption here was sharper than I expected. Her background in human capital is rare for someone in her role, and that came through. She thinks about AI as much through the lens of people and skills as she does through the lens of platforms. That framing showed up strongly when we got to MongoDB's commitment to upskilling two million Indian builders by 2030. She made the case for why the developer pipeline matters as much as the product itself, and I agreed with most of it. A few things stayed with me from this conversation. The companies winning with AI right now are not the ones with the biggest budgets. They are the ones whose people are ready to use it. India is not just adopting AI. India is shaping how AI gets built for the rest of the world. And the next two years will separate the enterprises that treated AI as a project from the ones that treated it as a rewiring. More conversations coming soon stay tuned!!!! #data #ai #mongodb #lmongodbocal #theravitshow
August 25 · 9 min
SAP Transformation in 2026: Migration, AI, and the Road Ahead
For many SAP customers, the next couple of years will be critical. Between the upcoming ECC end-of-support deadline, evolving API strategies, and growing interest in AI, organizations have a lot of important decisions to make. At Boomi World Tour London, I had a chat with Donna Matthews to discuss what all of this means for SAP customers and how they can prepare for what's next. One of the biggest takeaways from our conversation was that modernization isn't just about completing a migration. It's about building a foundation that allows organizations to move faster, integrate more effectively, and take advantage of AI as their business evolves. During our discussion, we covered: * What SAP's recent API policy means for customers * How organizations should be thinking about the 2027 ECC end-of-support deadline * Why integration plays a key role in a successful S/4HANA journey * How SAP customers can start realizing value from AI today instead of waiting until migration is complete * Practical advice for organizations that are still planning or early in their transformation If your organization is navigating its SAP roadmap, this conversation offers valuable insights into the challenges and opportunities ahead. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld #BoomiAmbassador #theravitshow

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