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Enterprise AI doesn’t fail because models are weak.  It fails because data pipelines break, permissions drift, costs explode, and governance is treated like an annual checkbox instead of everyday operational discipline. In this episode of Intelligent Indians!, Vikram Vaidyanathan sits down with Rohit Choudhury, Founder & CEO of Acceldata, for a deep conversation on how enterprise data systems actually behave at scale and why AI makes every weak foundation visible. Rohit’s founder journey mirrors the last 15 years of data infrastructure: from scaling InMobi in the pre-cloud era, to the Hadoop days at Hortonworks, to pioneering data observability, and now pushing into Agentic Data Management as enterprises move from AI pilots to production. The conversation goes deep into: 1. Why AI POCs often work in one team but collapse at enterprise scale 2. The real cost of fixing data late, and why ingestion is the only place to fix it3. How AI agents change the stakes: real-time failures, hallucinations explainability and ai updates4. A practical playbook for CIOs and CDOs: SLAs, rapid experimentation, and leadership upskilling5. Why cost control is becoming existential, from reruns to cloud bills to private/hybrid AI The “India edge” in building global enterprise companies, and what DPI × AI could unlock next Watch the full episode to understand what it really takes to make AI work at enterprise scale. Chapters: 00:00 Why AI POCs Fail at Scale in Enterprises02:30 Introducing Rohit Choudhury (Acceldata Founder)05:30 Early Days at InMobi & Rewriting the Stack 3 Times08:30 First Startup Failure & Learning Customer Obsession11:30 Birth of the Data Observability Category14:30 How Enterprise Data Pipelines Really Work (1000+ Apps)18:00 Why Enterprises Need End-to-End Data Visibility21:00 COVID Story & Missed Investment Moment24:00 Why hallucinations are dangerous for enterprises27:00 Governance in AI30:00 How Banks Should Prepare for AI at Scale (4 Steps)33:30 Why Leaders Must Learn AI Themselves (Cursor Story)36:30 From Tool Sprawl to One Agentic Platform39:30 Cost Explosion: Cloud vs Private AI42:30 Self-Healing Data & Preventing AI Hallucinations45:00 – Future of Enterprise AI & Closing Thoughts Follow Z47 Website - https://www.z47.com/Instagram - https://www.instagram.com/z47.vc/LinkedIn - https://www.linkedin.com/company/z47-vc/ #podcast #podcasts #future #ai #aiindia #aicourses #aiupdates

India’s AI moment will be defined by architecture. In our new series Intelligent Indians! we sit down with the builders, policymakers, scientists, and founders shaping India’s AI decade as it unfolds. In the first episode, Avnish Bajaj and Vikram Vaidyanathan sit down with Abhishek Singh, CEO of the IndiaAI Mission, to unpack how India is designing AI as infrastructure, not experimentation. The conversation goes deep into what Abhishek describes as “DPI to the power of AI” for an ai learner in India extending India’s proven Digital Public Infrastructure (Aadhaar, UPI, DigiLocker, Account Aggregator and more) into the AI era and India AI Mission. The discussion covers how population-scale systems, once thought impossible, are now becoming India’s execution advantage. This conversation is a must watch for anyone curious about: 1. How affordable compute, open datasets, and India-native foundation models remove first-order constraints 2. The seven pillars shaping of the IndiaAI mission:from compute and data to deployment, trust, and governance 3. Why AI in India must work across languages, income levels, devices, and real-world conditions 4. How the India AI Summit is a milestone in India's longer-term systems build Chapters  00:00 Trailer1:57 Introduction2:36 What is India AI Mission4:30 Nandan Nilekani's advice5:03 What is DPI in AI 6:22 7 pillars of Indian AI10:30 Rs. 65?12:00 Developing applications that will benefit people 14:20 Knowledge and access to AI 15:25 AI helping Indian farmers16:48 India's consent architecture18:54 Where is AI helping?21:00 DeVc AI founders22:15 Is data labs a government initiative23:50 Tools to code faster25:24 Why is AI failing at production scale26:16 What is free AI commission27:41 Should data monetisation be banned28:31 Is govt doing AI startup financing31:20 Conclusion  Also watch:  India's secret advantage in AI - https://www.youtube.com/watch?v=qqvInPvPkhQ&t=1430s AI Committee Report - https://kpmg.com/in/en/insights/2025/08/rbi-free-ai-committee-report-on-framework-for-responsible-and-ethical-enablement-of-artificial-intelligence.html Follow Z47 Website - https://www.z47.com/Instagram - https://www.instagram.com/z47.vc/LinkedIn - https://www.linkedin.com/company/z47-vc/ #podcast #podcasts #future #ai #aiindia

Future Signals is Z47's view on where the next category-defining companies are likely to emerge, and the spaces we’re actively tracking alongside the founders building them. For Indian startups, 2026 promises to be an inflection year where experiments from previous years create a foundation on which the future is built.Market-defining investing rarely arrive fully formed. They are assembled, piece by piece, by founders who are willing to do the unglamorous work: shaping behaviour, stitching infrastructure, earning trust before scale shows up in a spreadsheet.  Over the last two decades, we’ve observed India’s technology story unfold in phases. First, we built capability, then scale. And finally, the confidence to build world-class businesses and public-market-ready companies. We are now moving into the next phase, where founders start redefining how value is created: by owning outcomes instead of features, and by building systems that work at population scale and travel globally.These are the Future Signals that will define 2026 and beyond.Follow Z47Website - https://www.z47.com/Instagram -   / z47.vc  LinkedIn -   / z47-vc  Chapters00:00 Introduction01:49 Are founders creating markets?05:23 Evolution of AI 7:59 How is AI changing consumer companies?10:25 Disruption via Agentic AI 12:01 Fin Ops is largest SaaS opportunity 14:28 Speed takes over startups 16:46 AI in Intelligent hardware 18:42 AI in banking and finance20:48 Consumer companies making AI personal22:43 The next META/ Binance?23:59 What does India need? 28:21 Commerce is being re-built30:03 Indian companies going global32:47 SaaS products scaling globally36:11 Manufacturing goes global38:03 Co-pilots in businesses41:43 2026 is the year of deployment#podcast #podcasts #future #ai #aiindia

Between overprotective term sheets and oversimplified ones lies the real market. On this episode of #ZeroToInfinity, Avnish Bajaj and Tarun Davda, are joined by Archana Rajaram to weigh in on one of the most misunderstood parts of startup building: the term sheet. As the founder of River Law (formerly Rajaram Legal), Archana’s work has quietly shaped the “market standards” that nearly every Indian SHA now follows.In this episode, they break down the real-world changes in Indian term sheets post-2021:from liquidation preferences and reverse vesting to governance, exits, board control, and how AI is already reshaping negotiations. The way a founder negotiates a term sheet often foreshadows how they’ll build their company, handle conflict, and navigate hard decisions later.Tune in for a rare inside look at venture’s most misunderstood document01:23 Introduction to the Z47 podcast 02:52 Handshake deals to hyper-detailed clauses: how India’s term sheets have evolved05:55 Why written guardrails matter, even in trust-based relationships?08:48 Between one-pagers and legal novels lies the real market standard10:38 Founders lose leverage the moment they sign without counsel12:42 What founders miss at incorporation, they pay for at IPO16:20 You can’t switch from founder-led to board-led overnight28:02 In India, governance runs through SHAs, not boards34:07 Every founder’s dilemma: what terms really matter in your term sheet?37:39 Event of Default: India’s most controversial term and why it exists49:49 How you negotiate your first term sheet predicts how you’ll scale59:57 AI may change diligence, but judgment still writes the rulesFor more insights, revisit the related Z47 episode:   • The Terms of Term Sheets    • Hard clauses in a term sheet    • From (Z)omato to (A)ther: India’s Tech IPO...  Article:   / calculating-liquidation-preference  Follow us on: Website: https://www.z47.com/LinkedIn:   / z47-vc  X: https://x.com/z47_vcInstagram:   / z47.vc  

In this episode of Zero to Infinity, Rajat Agarwal and Chandrasekhar Venugopal sit down with Vivek Sinha, second-time founder and CEO of Emversity, to unpack one of India’s most urgent and least-discussed problems:👉 Why are millions of young Indians still unemployable after 16 years of education?👉 What will it take to build a truly job-ready workforce?👉 And why might the biggest opportunity in education lie in the “grey-collar” economy, not tech or test prep?Vivek takes us deep into the realities of India’s higher education system, the failure of legacy institutes to prepare students for real-world roles, and the massive talent gaps in healthcare, hospitality, construction, and manufacturing.He also breaks down Emversity’s groundbreaking model:On-the-job learning (not online, not offline)Industry-backwards curriculum co-designed with employersEarn-while-you-learn degrees that effectively cost students ₹0VR simulation labs and real-world skill training50%+ organic student acquisition driven purely by outcomesIf you care about India’s future workforce, social mobility, or building meaningful businesses at scale, this conversation is a must-watch.01:47 - Introduction to the Z47 Podcast02:52 - What really happened to India’s EdTech boom?03:25 - The three faces of EdTech: daycare, test prep & higher education06:24 - Intent and Outcomes:  the two pillars of real education10:51 - From disruption to discipline: building an education business that lasts15:47 - Emversity’s goal: make every student employable, not just enrolled18:59 - Bridging academia and industry through tech + training22:43 - Why Emversity bets on India’s grey-collar workforce27:55 - Earn while you learn: the zero-cost degree model31:24 - Tech-enabled, execution-driven: how Emversity scales smart37:27 - The long road ahead: can Emversity educate a million students40:30 - Changing lives, one job at a time42:00 - Building India’s next-gen education infrastructureFollow us on:Website www.z47.com/LinkedIn www.linkdin.com/company/z47-vc/Twitter: https://x.com/z47_cvInstagram: www.instagram.com/z47.vc/

70% of enterprise AI projects never reach production. The solution: Forward Deployed Engineers (FDEs).In this episode, Vikram Vaidyanathan and Rocketlane CEO, Srikrishnan Ganesan unpack the rise of the FDE model, from Palantir’s origins to how AI companies use it today to bridge the gap between prototypes and production.They discuss why traditional SaaS orgs break in AI, the governance needed to scale FDE teams, and why India is emerging as the global engine room for AI deployment.A crisp breakdown of the role shaping the future of enterprise AI.Chapters 00:01:29 -  Introduction to the Z47 podcast 00:04:16 - The 70% problem: Why enterprise AI fails to scale00:05:25 - The origin story: Inside Palantir, where it all began00:12:43 - Evolving from deployment to GTM engine00:14:23 - The Vision Selling era: from POCs to production ROI00:17:09 - What does a great FDE motion look like?00:18:59 - Building with FDE DNA: How Rocketlane practices what it preaches00:24:06 - Product, success, or stand-alone: Where should FDEs sit?00:26:22 - Scaling the FDE model: from speed to structured governance00:33:38 - Pairing on-site FDEs with India’s 24×7 talent engine00:34:21 - AI adoption as India’s next big export00:37:49 - FDEs: The human bridge between AI promise and delivery  

From a small apartment in Bengaluru to powering over 2 billion users across 60+ countries, MoEngage is one of India’s most quietly global success stories. This is more than a startup story — it’s a story about India’s rise as a product nation, and the founders who are reimagining how the world engages with technology.Raviteja Dodda, Narasimha Reddy and Yashwanth Kumar built a world-class customer engagement platform out of India — long before “SaaS for the world” became a buzzword.We trace MoEngage’s journey through its toughest pivots, its cultural DNA, and the product decisions that made it indispensable to brands like Airtel, Ola, Nestlé, and Samsung.In this episode of #ZeroToInfinity, MoEngage co-founders Raviteja Dodda & Narasimha Reddy join Tarun Davda & Pranay Desai of Z47. In a wide-ranging chat, they discuss the MoEngage journey - from initial failures to serving over 2Bn users. 

756 of 1,009 rows displayed Submarine parts. Metro casings. Agricultural harvesters. Even bombshell casings for the defence sector. In this episode of the #ZeroToInfinity podcast, Ximkart founder explore how solving the “thinking loop” first makes it possible to deliver parts for any industry, anywhere. Once the right inputs hit the execution loop, size, sector, and material become irrelevant, it’s all just precision manufacturing at scale.

The US innovates. China industrializes. Can India deploy at scale and claim its right to win in AI?India’s edge has always been its people: engineers, data, and scale. But as LLMs automate language and logic, can that advantage still hold?In this episode of #ZeroToInfinity, Chandrashekhar Venugopal speaks with Avnish Bajaj and Vikram Vaidyanathan about India’s crossroads in the AI race and what will define the country’s next decade.The trio unpacks:The rise of Forward-Deployed Engineers (FDEs): India’s secret weapon in AI deploymentHow data, assurance, and governance could become India’s strongest moatWhy AI might finally flip India from back office to frontlineHow voice and vernacular interfaces could erase the digital divideAnd what government, investors, and founders must do to spark India’s inference leapWith Aadhaar, UPI, and consent layers powering population-scale systems, India has the infrastructure and now the opportunity to lead the world by deploying intelligence at scale.

In the near future, less than 10% of access will come from humans, the rest will be agents?What happens when your next user isn’t human?In this episode of the #ZeroToInfinity podcast, Pranay Desai sits down with Satya Devarakonda and Ravi Madabhushi, founders of ScaleKit, to decode a fundamental shift in software: the rise of agents as first-class users.Humans log in and out with predictable patterns. Agents are transient, transactional, and unpredictable, hitting systems hundreds of times a minute. ScaleKit’s modular approach is built for this new reality, where AI agents, not humans, dominate usage.Satya and Ravi bring rare scar tissue and foresight. From PipeMog in 2013, to FreshID at Freshworks, to now ScaleKit, they’ve spent a decade solving identity and access at scale, and are rethinking it for an agent-first world.The duo also shares what it means to build again as second-time founders: why distribution matters more than product, what they’ve unlearned from Freshworks, and how agents are evolving from assistants into colleagues who accelerate productivity.

Dive deep into the transformative power of AI in India’s consumer app landscape. In this episode of Zero to Infinity, Avnish Bajaj, Chandrasekhar Venugopal aka CV, and Kishan Kashyap break down: ✅ Why ChatGPT is AI’s MS‑DOS moment and what comes after ✅ How token costs dropped 1000x in a year (and why Sam Altman calls it the “new Moore’s Law”) ✅ The Disruptor–Digitizer–Enabler framework for founders building in consumer tech ✅ Why QuickCommerce for “X” (fashion, pharma, events & more) is the next frontier ✅ And why India’s rails: UPI, Aadhaar, WhatsApp—mean everything is aligned for foundersAfter tracing India’s growing consumer instinct in Part 1, this segment explores what happens when that intuition meets AI and what gets reimagined when UX, infra, and distribution evolve together, not sequentially.Whether you’re an early‑stage founder, an operator, or just AI‑curious, this conversation will spark ideas on what to build next.

Most startup journeys are told in hindsight, GreyLabs AI’s told in the middle of figuring things out.In this episode of the #ZeroToInfinity podcast, the founding team of GreyLabs AI reflect on what building actually looked like in year one: navigating a cofounder exit, cash running out, COVID hitting collections, and a work culture being built reactively. This conversation with co-founder Aman Goel is about what startup life feels like before structure, where ESOPs are misunderstood, leave policies don’t exist, and the only way to build trust with enterprise clients is to keep showing up.What started as a speech analytics platform for BFSI quickly turned into something more: a layer that could coach agents, surface cross-sell opportunities, and turn raw call data into revenue. But the real build wasn’t technical, it was emotional. GreyLabs was built without funding, a co-founder or a roadmap. Just presence, product sense, and a willingness to stay in the room longer than expected.Inside the team, hiring moved fast and policy came later. Hiring was led by instinct, and culture was shaped by how the team responded to mistakes, not how they talked about values. Who gets ESOPs? When do you make a leave policy? How do you scale trust without layers of management? When COVID hit: collections slowed down and revenue dried up, but even without clarity on survival, the team promised zero layoffs, and that appraisals happen. Because sometimes, the strongest signal a startup can send isn’t product, it’s how it shows up for its people.Why sustainable startups start with sustainable founders, only on the #ZeroToInfinity podcast.

We’re not seeing enough entrepreneurship in India, not for lack of talent, but because most founders haven’t yet internalized how quickly India’s consumption story is shifting. As GDP curves bend upward and new behavior patterns emerge across services, spending, and aspirations, in this episode of the #ZeroToInfinity podcast, Kishan Kashyap, joined by Avnish Bajaj & Chandrasekhar Venugopal, maps out the biggest trendlines shaping consumer India through the next decade. From rising discretionary income to evolving expectations around access, quality, and convenience, the Z47 team makes a case for how premiumization is no longer about luxury but about discernment, service, and experience. How full-stack brands are winning not by controlling supply, but by owning end-to-end experience and why India’s microtransaction economy is quietly creating massive consumer surplus in places few are watching. Tune-in

India’s largest creditworthy segment remains invisible to the lending ecosystem, not because of a credit problem, but because of a design problem.When every lender chased the top tier or flooded the informal market, Finnable saw an opportunity to build for the 60–70% in between: salaried, PF-backed Indians earning ₹15–40K a month.Join co-founders Amit Arora and Nitin Gupta on the Z47 podcast as they trace their vision behind building a full-stack, risk-first, and collections-led lending engine—designed to scale, but built customer-backward.The duo, in conversation with the Z47 team, reflect on the decisions that shaped the company: from making collections the third hire, to designing 200+ fraud algorithms rooted in field intelligence.Understand why their model went against the industry wave, offering longer-tenure, higher-ticket loans designed for credit discipline and repeatability.When COVID hit, their customers didn’t default. They returned. That resilience validated the model across cycles, powering a ₹3,000 crore book & pointing to a lending business quietly built for public markets.Listen to the latest #ZeroToInfinity podcast on building lending systems that stay with the customer.

India will see 100 tech IPOs by 2030. While Silicon Valley still waits for $5B+ exits, Indian founders are going public earlier - at $600 million.Why? Because market receptivity has fundamentally shifted. The buyers want this asset class.On the #ZeroToInfinity podcast, Avnish Bajaj and Rajinder Balaraman define what it actually takes to go public in India today. Through the lens of four distinct IPO waves, the duo traces how market expectations have evolved — from the early public-market pioneers, to the pricing missteps of 2021, and into a new phase where predictability and return on capital lead the way.The team argues that IPOs are no longer about valuation peaks but about narrative control, access to permanent capital, and owning the category early. For founders, that means shifting how companies are built, instead of chasing scale at any cost to designing for profitability, pricing discipline, and long-term market trust from the outset.This conversation unpacks the why, when, where, how of IPOs and the thresholds that separate companies that list from those that last. The team reframes the “To IPO or not to IPO” debate into a first-principles question for founders: Are you building with the intent to go public?India may be heading toward its own Nasdaq moment -  the first 100 tech companies that go public will reshape how founders raise, grow, and exit. See why India's IPO math has permanently changed and what it means to be IPO-ready in India only on the latest episode of the Z47 podcast.