Ashay Tamhane

History has a pattern most people ignore. When two brothers commit to one mission, outsized things tend to happen. These two just put India on the global AI product map. Mukund Jha and Madhav Jha didn’t start Emergent to “democratize coding.” They started it because app building was fundamentally broken. Especially for people building real businesses, not weekend side projects. Most people think building apps is hard because they’re non-technical. That’s a convenient myth. The real problem is the process. Too many tools. Too many handoffs. Too many systems that work fine until real users show up. And that’s exactly when founders can’t afford failure. So instead of hype decks and loud launches, these two brothers did something rare. They went quiet. No buzz. No flashy demos. No “AI will change everything” noise. Just months of grinding through the system, removing friction where it actually breaks. What came out of that effort became Emergent. Now here’s the part that should make you pause 👇 In just 7 months: 5M+ builders 6M+ real apps powered $50M ARR Users across 190 countries That kind of growth doesn’t come from hype. It comes from reliability. Which is why serious investors noticed. Early backing from Google AI Futures Fund, Gossamer Ventures, and SoftBank, followed by a $70M Series B. Not because it sounded exciting. But because it didn’t break when real users arrived. While most AI app builders stop at prototypes, Emergent goes end-to-end: Backend. Frontend. Database. Cloud. Payments. Deployment. Production-ready from day one. So what does this mean for the Indian Tech & IT landscape? This is the real shift 👇 1️⃣ India moves from services to software leverage When app creation becomes this fast and reliable, Indian founders can own products and IP, not just deliver projects. 2️⃣ Pressure on traditional IT models Billing hours and large dev teams won’t disappear overnight, but their dominance will shrink. Outcome-driven software beats manpower-driven software. 3️⃣ Faster startup cycles Founders no longer wait months to validate ideas. They can go from idea → production → users in days, not quarters. 4️⃣ Talent expectations will change The new edge won’t be “knows React or Java.” It’ll be: can you design systems, workflows, and outcomes? 5️⃣ India becomes a serious global builder, not just an executor Tools like this quietly shift India’s role from “cost-efficient tech” → product-first innovation hub. This isn’t just a startup success story. It’s a signal. Software is shifting from: "writing code to describing outcomes" And the teams that win won’t be the loudest. They’ll be the ones whose products don’t fail at scale. 👇 Let’s open this up Do you think tools like Emergent will disrupt Indian IT services or force them to reinvent themselves faster than ever? #IndianTech #IndiaAI #ArtificialIntelligence #AIProducts #StartupIndia #ProductManagement #ProductManager #ProductStrategy #AIInProduct #TechLeadership

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I've been following the context graphs debate with great interest. Jaya Gupta and Ashu Garg's "trillion-dollar" thesis, Prukalpa ⚡'s perspective on heterogeneity, Arvind Jain connecting it to transactive memory systems. These are important ideas. And the problem they're highlighting is real. Enterprises desperately need better ways to capture how decisions get made. Having spent years trying to build exactly these systems, I want to share what I've learned about why this is so hard. Lesson 1: You Need to Own the Interface At Nutanix, I worked on Intentful APIs for cloud infrastructure. The idea was to capture not just what changed, but why. We learned something fundamental: this only works if you own the entire interface. Kubernetes can do this because everything flows through its control plane. But enterprise environments are messier. States get mutated directly in underlying layers. Someone SSHs into a box. A quick database fix happens at 2am before a board meeting. This isn't bad behavior. It's reality. But it means any context graph is working with incomplete information. Lesson 2: Decisions Bottom Out at Human Judgment At Kognitos, I built exception handling systems which captured "decision traces". Decision-making is deeply recursive. Ask why a discount was approved. Then why that exception exists. Keep asking why. Eventually, you arrive at: "Because someone with experience made a judgment call." That's not a failure of documentation. That's how organizations work. Decisions happen under time pressure, with incomplete information, based on a team's evolving understanding. Lesson 3: The "How" is Often Private Recently at SoraNovaAI, I built knowledge base systems for a venture capital firm. What I discovered is that people often keep the "how" of their decision-making private. Not out of malice, but because it's genuinely their edge. A senior investor's pattern recognition. A sales leader's intuition about which deals to push. These are competitive advantages tied to personal goals. Context graphs assume everyone wants their decision-making captured. The reality is more nuanced. I believe in the vision of better decision capture. The pain is real. Organizations lose institutional knowledge constantly, decisions get relitigated, context disappears when people leave. But different people always see different parts of the same decision. That's not a bug. It's how organizations work. What I've learned: - Design for incomplete context, not perfect capture - Focus on domains where incentives align with transparency - Accept that the last mile is human judgment - Celebrate progress over perfection The real world is messy. The opportunity is in building systems that embrace the mess and still make things better. I'd love to hear from others who've navigated this. What's worked? What hasn't? #contextgraphs #enterpriseai #aiagents

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CraftifAI co-founded by Pratik Sharda and Yashwant Dagar, secured $3M in seed funding led by Ankur Capital to scale its GenAI platform. Targeting IoT, robotics, and automation, CraftifAI's silicon-agnostic platform accelerates embedded product development for edge devices. Their GenAI and Agentic AI workflow optimizes model deployment, supporting frameworks like GStreamer and ROS2. The funds will expand engineering and global reach. Pilot programs are underway with Indian OEMs in robotics, drones, and AI cameras, plus a US semiconductor firm, streamlining hardware development and reducing time to market. #GenAI #EdgeAI #Robotics #DeccanFounders #CraftiAI #AnkurCapital

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#AI #governance #framework is crucial for guiding #responsible AI #development and use, mitigating risks like #bias, #privacy #breaches, and #security #threats, #ensuring #ethical #alignment (#fairness, #transparency, #accountability), building public #trust, and ensuring #regulatory #compliance, all while balancing #innovation with societal #safety and #human #rights. It provides structure to manage AI's inherent complexities and potential harms, fostering #trustworthy AI systems that benefit #society. #better #future #safe #india

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If Surge Pricing Is Accepted Everywhere… Why Not Value Teachers the Same Way? We accept higher pay for complexity everywhere. Senior lawyers charge more for complex cases. Specialist doctors charge more for high-risk surgeries. Consultants earn lakhs for strategy. Tech architects earn more than junior coders. Nobody calls it greed. We call it expertise. But when it comes to teachers, society becomes sentimental. “Teaching is noble.” “Teachers shape the nation.” “Teachers are like God.” Beautiful words. But respect without compensation is exploitation wrapped in poetry. A teacher doesn’t just teach a subject. They shape thinking, confidence, discipline, emotional intelligence, and career direction. One sentence can change a life. One bad experience can damage it for years. And teaching today is not simple delivery. Handling 45 students. Managing board pressures. Preparing for NEET/JEE. Dealing with anxious parents. Creating digital content. Counselling teenagers in crisis. That’s not “just teaching.” That’s strategist + psychologist + mentor + evaluator + content creator — in one role. Complexity exists in education too. Coaching rank holders. Supporting slow learners. Designing curriculum. Building future-ready systems. If complexity deserves higher pay in medicine and law, why not in education? Here’s the hard truth: If we want world-class students, we must attract top 5% talent into classrooms. And top talent does not choose low-paying professions. Right now, brilliant graduates choose corporate jobs — not because they don’t respect teaching, but because society rewards corporations financially and teachers emotionally. You cannot build a world-class nation on gratitude alone. Yes, Agreed! Teaching is a service. But so is medicine. So is law. So is governance. Calling it service should not mean underpaid, overworked, and financially insecure. When teachers are stressed..... They burn out; They take excessive tuitions; They leave academia; Standards fall; And.....society complains. Higher pay doesn’t just reward teachers. It raises entry standards, accountability, professionalism, research, and student outcomes. A surgeon saves one life. A teacher shapes thousands over decades. Teaching has compound interest impact on society. If we understand ROI in finance, we must understand ROI in education. Underpay teachers — you attract mediocrity. Value teachers — you attract excellence. If we are willing to pay surge pricing for convenience, urgency, and entertainment… Surely shaping a human mind deserves premium value. Teaching is not charity. Teaching is nation-building. And.... nation-builders should never be underpaid!!! #ValueTeachers #RespectTeachers #NationBuilders

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Excited to share that Thread Factory has raised ₹1.5 Cr in pre-seed funding from ajvc to solve a massive problem in India’s fashion supply chain. We are building for a part of India’s $100B+ fashion market that is massive, but still works in a very old-school way. A lot of sourcing still happens through calls, WhatsApp messages, and scattered coordination between retailers and manufacturers. Which also means many buying decisions are still made without a clear sense of what is actually selling. And that gap becomes even sharper in the unorganised side of the market. While brands and online marketplaces have brought structure to one part of fashion retail, the remaining 65% still operates with very little technology. That is the part of the market we are building for. At Threadfactory, we are building a tech-first ecosystem for this supply chain – helping retailers source quality, trend-right designs more easily, while helping manufacturers plan production with better demand visibility. Our B2B platform allows retailers to discover products and place orders directly, while also capturing the commerce data needed to bring more intelligence into the system. Over the past year, we have seen early signs that this is solving something real: ~₹4 Cr ARR 60% average monthly repeat retailers 40+ manufacturers For me and Vivek, this round was a very meaningful milestone. We received a term sheet from Aviral within 10 days of our application, and that felt incredibly validating at this stage of the journey. This was when we did not even have a product, and were at zero revenue. What stood out even more was the clarity he brought to the conversation. He quickly understood the space and pushed us to think harder about what we are building and where it can go. The journey till here has not been easy. There has been a lot of learning, a lot of back-and-forth, and many moments of rethinking. But that process is also what made our conviction stronger. This round has helped us build a stronger team, improve the platform, and continue building for an industry that really needs better systems. Still early, but very excited for what lies ahead. 🚀 #threadfactory #ajvc #funding #b2b

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RIP Robotic text — I found a Humanise AI tool that makes everything 10x more natural! Introducing ChatLLM by Abacus.AI . All AI tools in one place! - http://chatllm.abacus.ai HERE ARE ALL THE FANTASTIC FEATURES 1. ACCESS TO ALL THE SOTA LLMs ChatGPT- 5 Pro, o4, Claude 4, Gemini 2.5, Grok 4.1, DeepSeek, and many more 2. IMAGE & VIDEO GENERATION Using the best AI models, FLUX, DALL-E, Ideogram, Kling, Runway, Luma & many other tools to turn your ideas into amazing pictures & videos. 3. BUILD APPS & WEBSITES A simple prompt instantly gives you live website previews. Edit everything—colors, text, layout—effortlessly! 4. AI PRESENTATIONS ChatLLM allows you to generate Word, Excel, or PowerPoint files quickly with a single prompt. It is quick, simple, and time-saving. 5. AI PERSONAL ASSISTANT - OPERATOR AI handles tasks for you 24/7, even while you’re asleep. It can search the web and take action, acting like a personal assistant that never needs a break. 6. CHAT WITH PDF You can work with any document, no matter how long or short & when things get complicated, get a quick summary that makes sense. 7. HUMANIZE AI CONTENT RIP AI Detectors. Generate your AI content. Just click the "Humanise" icon, choose your preferred tone, and the text will become human. 8. SEARCH THE WEB RIP Perplexity - ChatLLM gives real-time answers with trusted sources, images, and videos. 9. DEEPAGENT (To literally do everything) Create mobile Apps, Websites, Technical reports, presentations and much more with simple prompts. Make edits with simple english! 10. AI CODE EDITOR + ASSISTANT: CODELLM This AI Code Editor helps you write, run, and perfect your code with ease. Vibe Coding it is! This all-in-one tool can replace all your AI tools for just $10 a month! Give it a shot! http://chatllm.abacus.ai

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Recently, I got the opportunity to be on Vishal Malhotra’s podcast to talk about AI. This wasn’t a basic “AI is the future” conversation. We discussed thing like: AI voice cloning scams AI leading to war AI replacing actors Students over-depending on tools where India stands in the AI race and even whether AI could develop self-preservation instincts you won’t just learn how you can integrate AI into your life. You’ll understand what it means for media, creativity, education, and human thinking. Because AI is not just a tech upgrade anymore. It’s a shift in how we live, create, and make decisions. Watch it now Link: https://lnkd.in/dB3dgtyp

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Nice essay on robotics promise vs reality. My 2p here (based on having spoken with 20+ robotics companies from India) - India advantage - combine frontier model progress from US with (commoditized) hardware innovation from the East and build applied robotics + R&D on edge cases by FDEs + Indian SI talent integrating on the floor. - Manufacturing would be the test bed - automotive being the biggest adopter. Home/on-road/warehousing will lag given low savings etc. - Low adoption of Robots 1.0 in India will actually be an advantage and see people leapfrogging to 2.0 (learning) robots. Indian cos. have traditionally been capex-sensitive and people-heavy, so RaaS will fit really well. Good part is we are already seeing many Robots 2.0 in production. - Production data capture is a real moat especially in niche industries e.g. hospitals, food (our portco Chef has data from 70M+ food servings to date) - Other scaffolding (observability, safety certifications) will likely evolve in the US (pushed to frontier labs) #ai #robotics #physicalai

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Smallest ai raises $8 mn seed round led by Sierra Ventures for expansion Udisha Srivastav reports https://mybs.in/2eqxbu7 #AI #artificial_intelligence #Startups #AIStartups

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The recommendations of the Why LLMs Hallucinate reminded me of Prof SAK’s course on Formal Methods of Program Verification when I was an undergrad at IIT Delhi. The content of the course was great fun to extremely mathematical people but the grading was hell. His surprise quizzes awarded +1 for the correct answer and -2 for the wrong answer. The people in the class mostly had top 150 ranks in the JEE, and very high GPAs. Most had spent their last few years obtaining extremely certain answers with perfect reasoning. These were not people you would accuse of confident guessing - the JEE used to be dominated long form symbolic and numerical problems. The highest score in one of the quizzes in this course was +2 out of 20. I write “+” because the average score was -2, minus two. Some students missed the quiz. They had the above average score of zero… Thirty years later, this course still features in our conversations. Excessive penalties on confident guessing make for great stories but rewarding abstention with above average scores is not a good idea. I suspect we will end up hobbling models into analysis paralysis.

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Now my Aai uses AI and understands it well. Not because she became tech-savvy overnight. Because Kruti uses Marathi to communicate with her. While everyone is busy comparing between Claude and GPT-4o, Bhavish Aggarwal quietly introduced something much more powerful for India. → Kruti by Ola The first agentic AI in India that can speak "your maa ki boli." Tamil, Marathi, Hindi, Kannada, and Bhojpuri. Sanskrit, too. Made in India and prepared for India. This agentic AI is made to fit the way we really talk, ask, and engage. This isn't just another helper. It accomplishes what the majority of global AI models fail to: - 11+ Indian languages are supported. - Assists with things like ordering food, paying bills, and scheduling taxis. - Provides contextual responses and comprehends voice, text, and images. Why is this important to me as a marketer? Because when your campaign speaks in English but your audience thinks in Tamil... You don't have an issue with messaging. Relevance is a problem for you. Tools like Kruti go beyond how we speak to our audiences. They push the boundaries of how many. In other words, increase the number of people we can talk to. And I think that’s what will work best for the Bharat of today. I'm keeping a careful eye on this, so should you. #KrutiAI #Aimarketing #MelangeDigital #globalgrowth

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pov: you’re asking if india is building the next chatgpt. short answer? probably not. and that’s okay. because the real value isn’t there. while silicon valley chases bigger models and parameters, indian builders are solving problems that actually need solving. a doctor managing 900 patients. a farmer waiting for advice in their own language. a founder dealing with regulations that don’t copy-paste from the west. this work isn’t glamorous. it’s vertical ai. and when constraints are part of the system, this is the only path that makes sense. look at the math. healthcare gaps keep growing. agriculture supports 42% of the workforce, with rising losses. vertical ai can unlock 25–50% of employee work value. generic platforms barely touch 1–5%. and it happens fast. weeks, not quarters. last year says a lot. 17 indic and vertical models. genai assistants grew 4x. services startups scaled 4.6x. yet we keep asking the wrong question. “can india build the next chatgpt?” the better one is simpler. can we build a few vertical ai companies that solve real problems and people are happy to pay for? you don’t need to own the foundation model. own the bridge between what models can do and what customers need. that’s the real advantage. and that’s where things get interesting…

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Bhavin Turakhia didn’t chase hype. He built profits. 🚀 Started at 17, built Directi created global SaaS products like Radix , Flock & Zeta and crossed $1B+ in exits — mostly bootstrapped. Key Learnings: • Profitability beats valuation • Build global, think long-term • Discipline > funding • You don’t need noise to win 📌 Quiet execution builds loud results. #BhavinTurakhia #StartupLessons #Bootstrapped #SaaS #Entrepreneurship

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Introducing DynaRoute - intelligent model routing for efficient LLM usage At Vizuara, we (Pritam Kudale, Pranavodhayam P R, Raj, Rajat) have spent the last few months studying how teams use large language models in production. What we found was simple but eye-opening. Most teams rely on one large and expensive model for every kind of query, whether it is a quick factual question or a complex reasoning task. This works, but it wastes a lot of money and computing power. That is what led us to build DynaRoute (https://lnkd.in/dGSx2KRq), an intelligent routing layer that automatically analyzes your queries and sends them to the most suitable model based on complexity, cost, and performance. You do not have to change your application or rebuild your backend. You can continue using your familiar OpenAI-style API, just by switching the base URL and key. From that point, DynaRoute handles everything intelligently behind the scenes. In our internal benchmarks, DynaRoute delivered remarkable results. Real-world employee questions achieved up to 92 percent cost savings, while code-related queries saw around 74 percent savings. All of this happened while maintaining nearly the same quality and accuracy as top-tier models. For teams that are running customer support systems, coding assistants, agents, or RAG-based pipelines, DynaRoute can reduce your inference bills drastically while preserving the quality your users expect. You can think of it as a smart, adaptive router that continuously learns how to choose between models like GPT-4.1-nano, Gemini 2.5 Flash, and Claude Sonnet, depending on the query. We also provide API access so you can integrate DynaRoute directly into your own platform or agent framework. The API is OpenAI-compatible and can be configured with just a few lines of code. Here are our current plans: Free Tier: ₹0 per month, includes up to 50,000 tokens, OpenAI-compatible endpoint, and access to premium models. Plus Tier: ₹888.30 per month, includes up to 3 million tokens, unlimited requests, access to premium models, and priority support. Enterprise Tier: completely custom implementation for companies that need dedicated support, detailed analytics, and on-premise deployment. We also provide custom routing models for enterprise-level optimization. We are currently offering Enterprise Plan API access to selected partners who wish to integrate DynaRoute into their platforms. If you want to optimize the cost and performance of your LLM-based applications without compromising output quality, give DynaRoute a try. 👉 Learn more and get your API key at https://lnkd.in/gTrs9NWA

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🚀 While the world argued over ChatGPT vs Gemini, India was laying the groundwork for an AI superpower. From Sarvam AI’s 70‑billion‑parameter LLM backed by ₹98 crore and 4,096 H100s, to a French business school adopting our low‑power model, the momentum is unmistakable. The real edge? Compute costs slashed to ₹115/hour — nearly 50 % cheaper than global rates — and AI agents now run banking workflows, voice assistants recover₹16 kcrore, and sovereign AI powers Aadhaar updates in 10 languages. Are we ready to turn this silent surge into a global advantage? Let’s discuss. #AI #IndiaAI #DigitalLeadership #FutureOfWork #SovereignTech #Innovation #ThoughtLeadership Reference: [https://lnkd.in/gjM8urfu] 🔄 Share 👍 React 🌐 Visit www.aravind-r.com #AravindRaghunathan

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I did not read the paper in detail yet, but this seems like a big leap in benchmarking for the relatively low resource Indian languages. "a human-curated benchmark of over 13,000 multiple-choice questions covering 11 such languages (Nepali, Gujarati, Marathi, Odia as low-resource; Dogri, Maithili, Rajasthani, Sanskrit, Bodo, Santali, Konkani as extremely low-resource) plus Sanskrit-English code-mixed set". The dataset is publicly available on HuggingFace, and the paper is on ArXiv. I just skimmed through and noticed that Gemini is missing in their choice of proprietary models - In my experience with Telugu, it has been the best model so far. I will try to run this benchmark through Gemini and see how it compares [Update: I did run with gemini-2.5-flash, and accuracy is 10% higher than the best reported model in the paper i.e., GPT5 across languages - details in this blog post: https://lnkd.in/gJTRSFss ]. What does low-performance on this benchmark mean [Note that the evaluation setup was basic zero-shot prompting, so one may get better results in some other setup]? What is the bigger picture? The paper doesn't seem to harp on that much - it would be a good discussion to have to get a broader context. https://lnkd.in/g7kAgqtn https://lnkd.in/g-fEj4kY #aibenchmarking #indianlanguages #multiplechoice #bharatgen

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This is a turning point for us and possibly for how India prepares for competitive exams. Until last week, our system used Bayesian Knowledge Tracing (BKT), the same technique used by many adaptive learning platforms for over a decade. It worked. But only to a point. BKT could track if a student had likely mastered a concept. But it couldn't answer why they were stuck, or how one gap was silently affecting everything downstream. So we rebuilt our engine. From the ground up. Last week, we launched our Graph-based Knowledge Tracing (GKT) engine across NeoAI pilots. And the results? - Over 30% of students were attempting advanced topics like Trigonometric Identities without mastering prerequisites like Ratios. - Many were stuck repeating questions, not because of the question, but because they missed a concept 3–4 layers earlier in the graph. - Triggering a "review prerequisite" flow led to a 21% improvement in mastery within just 3 days. - Most students didn’t know why they were stuck until GKT showed them the actual conceptual block. This is the difference between linear chapter-based learning and graph-powered adaptive diagnosis. Traditional coaching adds content. We’re building a system that removes confusion. GKT doesn’t just track answers. It maps your thinking. It tells you what to learn, when, and why now, all based on how knowledge flows. And what worries us most? Many students, especially in Tier 2 and Tier 3 towns, are blaming themselves for being "slow" when it’s simply the wrong path, not the wrong brain. The problem isn’t effort. It’s the map. We're building a better one.

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100K active users in just 7 days + 12K paid conversions in week 1. That's NOT normal for a fresh AI platform — especially one building sovereign tech in India. Atomesus AI launched Jan 12, 2026 and the numbers already scream product-market fit. Here's what stands out for builders & founders 👇 🔥 Unlimited free tier (chats + image gen) hooks mass adoption fast 🔥 ~12% paid conversion right out of the gate — rare in AI land (most wrappers stay under 5% even months in) 🔥 Android live now, iOS coming soon, daily pushes from an ISRO-rooted team 🔥 Sovereign roadmap: hybrid today, full proprietary model ahead → real data-in-India + better Indic language handling This proves a few things we obsess over in 2026: → Generous free access + natural upsell beats forced subscriptions → Local talent + sovereignty focus builds actual moats in emerging markets → Iteration velocity > starting with the biggest model As someone tracking India's deep-tech moves, this milestone hits different because the monetization speed is what VCs chase in AI-native bets right now. Have you tried the app yet? → www.atomesus.com or search "Atomesus" on Play Store What's the real unlock for Indian AI teams? -Beating globals on cost + speed? -Sovereign data edges in a world of export controls? -Or hitting 10%+ paid conversion this early? Curious what devs, founders, and product folks think — drop your honest take below. Tag someone grinding on Indic AI or sovereign infra. Let's talk execution realities. 🚀 #IndiaAI #SovereignAI #DeepTech #AIStartups #ProductLedGrowth #MakeInIndia #AtomesusAI

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