Sreenath Reddy

TrueTrends from Sila's consumer insights unit is a semantic consumer intelligence platform that allows market researchers and marketers to detect trends before they happen. Keyword search is a thing of the past - with multi-category AI, computer vision (image+video - audio coming soon) and semantic understanding powered by LARA, you get an unparalleled vision of what consumers actually want, desire, who they are and their motivations. More importantly, it actively solves challenges and directs R&D. It understands your intent as much as theirs. I think we've all been burned by social listening tools driven by keywords and having to construct boolean operators, and as a result just making outcomes full of biases and frankly untrustworthy with the amount of junk. That's why we've re-written the script on what different data sources, including social, can bring to market research in any language (not just 22 dialects of Arabic). The difference? 118% better content discovery because we understand meaning, not just mentions. More than a marketing tool, this is a way to replace costly trend agencies, test concepts with real behavioral data (not panels of 20 people), and predict outcomes that researchers and marketers can actually trust. Real-time intelligence instead of quarterly reports. A groundbreaking piece of kit the Sila | صلة market research and tech team has worked on tirelessly to cut through the noise, and I've got to say as a completely biased observer, an amazing tool that's already replacing traditional research methods at enterprise level across MENA. More details: https://lnkd.in/d8RFGdfp Reach out if you'd like a demo - I'll show you true trend semantic intelligence vs keyword bingo.

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Twin brothers from India built an AI platform that achieved $15M in ARR in just 90 days. Is this the Shopify-for-apps moment? Here’s is the full story Emergent, founded by twin brothers Mukund (ex-Dunzo CTO) and Madhav Jha (ex-Dropbox/Amazon infra), just raised $23M Series A led by Lightspeed (total $30M to date). Their pitch? A multi-agent ‘vibe coding’ platform that turns plain English prompts into full-stack apps: frontend, backend, auth, payments, deployment, all automated. And the traction is wild: 👉 YC copy in mid-2024: 700k users, $10M ARR in 2 months. 👉 By Sept 2025: over 1M people building 2M apps and $15M ARR in 90 days. Why does it matter? Because Emergent isn’t just “no-code.” It aims to be the Shopify for apps, where anyone can generate production-ready tools, deploy them instantly, and eventually monetise them. Think jewellery chains spinning up AI-pricing apps, SMBs ditching spreadsheets for onboarding tools, even healthcare patients building pain-management solutions. But here’s the catch 👇 Retention > creation. Generating millions of apps is flashy. Keeping them live, secure, and monetised is harder. Discovery and billing flows are still in their early stages. Quality & compliance. Building real apps means adhering to PCI compliance, implementing secure authentication, and maintaining safe data practices. Emergent claims in-house testing/security agents, but independent audits will matter. Competition is fierce. Replit, Builder(dot)ai, Rocket(dot)New, Cursor: Everyone is chasing “apps from text.” Emergent’s moat must be infrastructure, reliability, and a marketplace. Unit economics. Hosting and runtime LLM costs scale with the number of active users. Using cheaper models and caching helps, but the test is focused on gross margin at scale. The opportunity: The low-code/AI app market is forecast to hit hundreds of billions by 2030+. If Emergent nails reliability, discovery, and monetisation, it could be the default platform SMBs use to generate and sell apps in days, not months. Question for you: If you could build a full-stack app from a single prompt, what would you build first? ➕ Save this post and follow Zubin Kutar ⚡ to Learn & Grow 10x with AI

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Agents don’t browse. They don’t guess. They execute. And if your product data isn’t structured, unified, and instantly available, you’re invisible. In agentic commerce, clean pricing, real-time availability, and accurate product metadata aren’t “nice to have”, they’re prerequisites for being found, quoted, and bought. Most companies aren’t ready. And most don’t realize it. https://lnkd.in/e8VmZXns

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Over the last three years of building Mulltiply, I’ve spent a lot of time closely watching how B2B buying and selling actually happens in India. In these years, Mulltiply focused on building systems to remove friction from transactions, and last year alone we did business worth ₹400M+. But while scaling the business, one pattern kept repeating. B2B buyers and sellers don’t begin with systems. They begin with conversations. Price. Availability. Quantities. Changes. Follow-ups. It was the conversation that drove the business — not the system around it. Over the years, the medium of conversation changed. From meeting in person, to phone calls, and more recently to WhatsApp. But the nature of the conversation stayed the same. These personal conversations work very well — but they have a fundamental problem: 𝘀𝗰𝗮𝗹𝗲. As volumes increase, managing hundreds of parallel conversations become difficult. Not because people don’t care, but because humans don’t scale well. I also saw why many B2B websites and apps struggled to gain adoption. They tried to replace conversations instead of supporting them. Sellers weren’t asking for new channels. They were asking for systems around conversations. For a long time, building this was hard. But with the rapid progress of AI over the last year, something changed. For the first time, it felt possible to reliably understand unstructured, multilingual, real-world conversations — the way B2B actually works in India — and build systems around them. Interestingly, this shift is also being discussed more broadly now. Many VCs and investment firms are increasingly writing about conversational commerce becoming a primary interface, not just something layered onto existing systems. We arrived at the same conclusion independently — by learning on the ground. That led to the next step. Mulltiply AI is what I’m building for B2B buyers and sellers, starting from conversations and adding structure around them. In simple terms:  • Conversations stay natural  • AI understands intent inside those conversations  • ERP handles pricing, availability, and order logic  • Conversations turn into structured, trackable commerce We not trying to change how commerce happens. We trying to support how it already happens — with less manual effort. This doesn’t feel like a pivot to me. It feels like a continuation of everything the last three years taught us. We’re testing this quietly with a few customers and learning as we go. If you want more context on what I’m building and why, it’s written up at https://mulltiply.ai/ Back to building. #ConversationalCommerce #FounderJourney #B2B #AIinIndia

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Personalization doesn’t mean “Hi {first name}.” It means knowing your customer’s intent before they click. Too many brands still confuse automation with understanding. Adding a name to an email? That’s formatting. Anticipating a purchase based on behavior? That’s strategy. True personalization happens: – Before the product page – Before the search bar – Before the cart is filled It’s quiet. It’s contextual. It’s predictive. And when done right, it doesn’t just convert— It builds trust customers don’t want to leave. What’s one way your brand is personalizing without being obvious? #TechInRetail #RetailStrategy #Personalization #DigitalOptimization #CustomerExperience

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Most measurement systems force trade-offs that are hard to accept. Attribution gives speed, but ignores the full funnel. MMM gives scope, but is too slow. Incrementality offers causality, but only in snapshots that mean channel comparability is lost. This trade-off leaves marketers without full-funnel, daily, ad-level inputs they need to plan and optimize spend with confidence and act on emerging opportunities… As brands head into BFCM, that gap matters more than ever. And it’s exactly why we built Fospha.

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