Vivek Garg

15 Seed to Series B companies (including 4 YC companies) that have collectively raised over $307 million in funding are hiring in India - both onsite and fully remote: 1/ Track3D – AI Powered Reality Intelligence Platform for Construction Full-Stack Engineer [Hyderabad] 2/ SigNoz (YC W21) – Open source DataDog alternative Forward Deployed Engineer [Remote] 3/ Polybee – Turning unpredictable farms into data-driven factories with physical AI agents Backend Developer [Remote] 4/ Knowlify (YC S25) – Removing the pain of creating quality explainer videos Full-Stack Engineer [Remote] 5/ Exaforce – Agentic SOC Platform Software Engineer [Bengaluru] 6/ Senpi – Trade like the 1% Lead & Sr. Engineers, Mobile and Backend [Remote] 7/ faff – Your personal concierge AI Engineer [Bengaluru] 8/ indē wild – Difference Is Our Superpower Data Engineer [Mumbai] 9/ Lemnisk – Supercharge Marketing and Personalization Product Manager, Sr. QA Engineer [Bengaluru] 10/ Arango – Contextual Data Layer for Enterprise AI AI Forward Deployed Engineer [New Delhi] 11/ Flywl – Make cloud marketplace procurement fast, simple, and secure SDE 2 [Bengaluru] 12/ Tijoree – Empowering large Indian corporates to optimise their treasury SDE 2 [Mumbai] 13/ Cozmo AI (YC W22) – AI Employees That See, Speak, Think and Do for Enterprises SDE 4 Backend [Bengaluru] 14/ Yenmo (YC W24) – Secured consumer lending in India Head of Engineering [Bengaluru] 15/ Penguin Ai – Healthcare Native Intelligence Security Engineer [Hyderabad] Follow for more such weekly roundups of exciting engineering, product, and design openings.

44

1 Comment

This Bengaluru AI startup's headcount is smaller than single client accounts at Indian IT services firms, and still a $65M valuation. A 15-person team in Bengaluru just raised $21 million, or approx 200 crore. Their product does what most companies still pay a full department to do. 📍 Runable's agent, Grow, runs ads on Meta, Google, and ChatGPT without a media buyer. 📍 It cold calls and emails leads from a real phone number, without an SDR. 📍 It handles SEO and AI search visibility, without an SEO lead. So now the company has 15 people for 1.5 million users and over $65 million valuation, backed by titans like Susquehanna and Nexus Venture Partners.  That is a smaller team than most single client accounts at an Indian IT services firm. For 20 years, Indian tech scaled the same way.  A client had a problem We staffed people against it because more scope meant more headcount.  That math worked when execution was the bottleneck. Runable stands out because it is different.  > 1 agent owns the outcome end to end, building product, then running growth on top of it.  > Their team is at 15 because the work that needed 5 departments now needs maybe 5 prompts and one person watching the output. You can see the same shift inside GCCs right now.  Nasscom counted over 420 India-based GCCs whose parent companies make under $100 million in revenue, small teams, high leverage, built around ownership instead of scale.  Runable is that same logic, built as a product instead of a captive centre. I believe engineers should train for exactly this world and become people who own an outcome, not people who wait to be staffed onto one.  Because companies that used to need 50 people before to hit this scale now only need 15.  That is not a one-off. That is where hiring and the future of work in India is headed. Are you prepared for the new era?

104

12 Comments

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

1

#StartupMafia 3.0… and the Coming Startup Mafia 4.0 An interesting story in the The Economic Times today, by Disha Acharya & Puran Choudhary, highlights how companies like Razorpay, Cred, Meesho and PhonePe have become “founder factories,” spawning 200+ new startups. This phenomenon—often called the Startup Mafia effect—is actually a sign that an innovation ecosystem has matured. Silicon Valley saw it first with the PayPal Mafia. India is now seeing the same pattern. Startup Mafia 1.0 (2007–2016) The first wave came from pioneers like Flipkart and Paytm. This era built India’s consumer internet economy. Focus sectors included: • e-commerce • online marketplaces • payments • consumer apps This phase proved that large digital businesses could be built in India. Startup Mafia 2.0 (2014–2021) The second wave rode the mobile internet explosion after Jio. Companies like: • Ola • Oyo • Swiggy • Zomato trained a generation of founders who understood scaling operations, logistics and hypergrowth. India’s startup ecosystem moved from experimentation to unicorn-scale companies. Startup Mafia 3.0 (2018–Present) Today’s wave is driven by companies like: • Razorpay • Cred • Meesho • PhonePe The backbone of this phase is India’s digital public infrastructure: UPI Aadhaar cheap cloud infrastructure developer ecosystems Many new startups emerging from these companies focus on: • fintech infrastructure • AI-native software • cross-border payments • SaaS tools. Startup Mafia 4.0 (Emerging) But the next wave may look completely different. Instead of consumer apps, it will likely come from deep-tech ecosystems. Places like: • IIT Madras Research Park • semiconductor clusters in Bengaluru and Hyderabad • robotics and EV startups • industrial analytics companies. Here we see companies like: • Ather Energy • AgniKul Cosmos • Detect Technologies • Gyan Data training engineers who will eventually become founders themselves. The earlier waves digitised commerce and payments. The next wave may digitise machines and infrastructure. Instead of apps connecting people… we will see platforms connecting: • factories • vehicles • industrial machines • supply chains. This is where mechatronics + IoT + AI come together. If sensor data, embedded electronics and cloud analytics are layered onto these machines, they can become connected intelligent assets. Predictive maintenance, uptime guarantees and performance optimisation become possible—even for affordable machines. Industry 4.0 for the Real Economy Germany built Industry 4.0 for high-end factories. India may build Industry 4.0 for millions of machines used across the Global South. And that next generation of founders—Startup Mafia 4.0—may emerge not from consumer apps, but from mechatronics, IoT and industrial intelligence ecosystems. The next startup revolution may not just connect people. It may connect the machines that power the real economy.

5

𝗜𝗳 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮, 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 𝗔𝗻𝘆𝘄𝗵𝗲𝗿𝗲—𝗢𝗿 𝗗𝗼𝗲𝘀 𝗜𝘁? A founder pitched his SaaS Product. “India first, then global.” Investor: “Why India first?” “Indian customers are toughest. Enterprise quality at startup pricing. If we survive here, we win anywhere.” Investor nodded. Then: “Have you tried the US market?” Pause. “Not yet. We’re not ready.” — 𝗧𝘄𝗼 𝗩𝗲𝗿𝘀𝗶𝗼𝗻𝘀 𝗼𝗳 “𝗜𝗻𝗱𝗶𝗮-𝗙𝗶𝗿𝘀𝘁” One is strategic. One is avoidance disguised as strategy. ↳ Strategic: Building for extreme constraints creates innovation that scales ↳ Avoidance: Labeling difficult markets as “training” while dodging markets requiring different capabilities — 𝗧𝗿𝘂𝘁𝗵 #𝟭: Constraint-driven innovation works. Low bandwidth, price-sensitive customers, fragmented infrastructure—these force elegant solutions. WhatsApp succeeded globally solving for Indian internet. Zoom’s architecture came from emerging market connectivity. You can’t hide bloated features or mask poor UX. Legitimate strategy. 𝗧𝗿𝘂𝘁𝗵 #𝟮: Some founders confuse “hard to sell” with “makes us stronger.” What they’re avoiding: ↳ Enterprise credibility they haven’t built ↳ Regulatory expertise they don’t have ↳ Brand positioning beyond “cheaper alternative” ↳ Different buying experiences Western customers expect — 𝗧𝗵𝗲 𝗧𝗲𝘀𝘁 Your India strategy works if you’re: ↳ Building for constraints that create portable innovation ↳ Mastering unit economics that scale ($3 to $50 ARPU) ↳ Testing global markets early to learn what transfers It fails if you’re: ↳ Perfecting product before facing “sophisticated” buyers ↳ Avoiding markets requiring different sales approaches ↳ Assuming local credibility transfers globally — 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 Am I solving for constraints that create innovation, or avoiding markets that expose gaps? If I launched in the US tomorrow, what breaks—and am I building those capabilities now? That reveals whether “India-first” is strategy or stalling. — 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘀𝗰𝗮𝗹𝗲𝘀. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝘃𝗼𝗶𝗱𝗮𝗻𝗰𝗲 𝘀𝘁𝗮𝗹𝗹𝘀. #Leadership #Startups #ProductStrategy #GlobalExpansion #SaaS

8

Excited to share my latest project, CartSaver AI, built for the Razorpay AI Buildathon 2026 (Track 3: AI Revenue Recovery)!! Every day, thousands of checkouts fail due to bank timeouts, card declines, or failed OTPs, leaving massive amounts of revenue on the table. While most recovery tools rely on generic, one-shot messaging, CartSaver AI is an autonomous multi-step agent that closes the loop end-to-end: score, prioritize, diagnose, escalate, stop, and log. *Key Architectural Highlights: ML Recovery & LTV Scoring: Predicts recovery likelihood via scikit-learn models and combines it with customer LTV scores so efforts target high-value customers first. *Google Gemini Diagnosis: Uses Gemini to analyze why a payment failed and dynamically draft context-specific recovery messages. *Deterministic Safety Boundary: A core philosophy of this build—AI Proposes, Deterministic Policy Decides, Bounded Simulator Executes. The LLM never decides when to stop contacting a customer; compliance-critical limits like maximum attempts and score thresholds are strictly hardcoded in plain Python. *Transparent Audit Trail: Every decision, reasoning path, and outcome is written to a full audit ledger viewable via an interactive operations dashboard. Across a 300-cart batch: ₹16.3L at risk → ₹5.36L recovered (27.33% recovery rate) — calibrated to real-world benchmarks, not an inflated demo number. Tech Stack: Python, FastAPI, Streamlit, scikit-learn, Google Gemini API, SQLite, and pytest. Explore the live project and code: 🚀 Live Streamlit Dashboard: https://lnkd.in/gCSZQSC6 🔌 Live Backend API: https://lnkd.in/gN7vYp2g 📂 GitHub Repository: https://lnkd.in/gU8PeerH Built with rigorous safety controls and a focus on measurable impact. I would love to hear your thoughts and feedback! #RazorpayAIBuildathon #AI #MachineLearning #FastAPI #Streamlit #GoogleGemini #RevenueRecovery #Python

7

2 Comments

The Hidden Complexity Behind a Simple UPI Transaction. Most users see a UPI transaction as a simple three step process: Open App --> Enter UPI ID / Scan QR --> Enter PIN Money moves in seconds. What users don't see is the sophisticated ecosystem working behind the scenes to make that experience possible. A single UPI transaction typically involves: • The customer application (PSP App) • The remitter bank • The beneficiary bank • NPCI's UPI switch • Fraud and risk monitoring systems • Device binding and authentication services • Reconciliation and settlement systems • Dispute management frameworks Within a few seconds, multiple API calls are exchanged, validations are performed, fraud checks are executed, account balances are verified, and transaction statuses are synchronized across participating entities. What makes this even more challenging is the expectation of near perfect availability. Users expect payments to work instantly, whether they're paying ₹10 for tea or ₹1 lakh for a business transaction. Behind every successful UPI payment lies years of infrastructure investment, regulatory compliance, operational monitoring, and collaboration between banks, fintechs, and technology providers. As digital payments continue to grow in India, the focus is no longer just on enabling transactions but it's about delivering speed, reliability, security, and trust at scale. The next time you hear the familiar "Payment Successful" notification, remember that a complex network of systems worked together in milliseconds to make that happen. #UPI #DigitalPayments #FinTech #ProductManagement #Banking #Payments #NPCI #Technology #IndiaStack #ProductLeadership

44