Partner at Info Edge Growth (Investing in Series B+ tech cos) | @InfoEdgeVC | IITK, IIMA Alum. Opinions are personal.

New Delhi, India
Joined September 2011
Few reasons: - IndiaAI is a fit for foundational model cos for training use cases; inferencing and fine tuning use cases req for most cos arent a fit - For early stage cos APIs from frontier labs are cheaper than self hosted inference until they scale
We reported on an important story around AI and compute demand in India earlier this week. Question to academics, founders and VCs- why is there no demand for compute in the domestic market? So much so that govt is now allwping those empanelled with IndiaAI to sell it overseas. Is it a question of red tape (access), paucity of capital or lack of training/consumer AI startups that need compute
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Open source from India for the world! Terrific job @lets_dig_deeper @rumik_ai
rumik oss 1 is now trending no.4 on @huggingface ❤️
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Rishabh Katiyar retweeted
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Shipsy is a clear example of doing a terrific job of leveraging AI as a force multiplier in an enterprise product suite. And we are seeing pull from global enterprises across US and EU!
We just launched Shipsy Brain, and I want to tell you why it exists. Most logistics systems were built to record what happened. But the actual decisions still happen outside the system, in spreadsheets, emails, phone calls, and people’s heads. That’s what we’re changing with Shipsy Brain, our fine-tuned model purpose-built for logistics. It builds on insights from more than 5 billion shipments, 50 billion operational events, and 1 billion decisions processed through the Shipsy platform. It powers agents that can: 1)Resolve suspicious orders by contacting customers and guiding drivers 2)Validate proof-of-delivery documents and freight invoices 3)Detect operational risks and potential surcharge fraud 4)Take routine follow-up actions while escalating high-impact decisions to humans In one quick-commerce use case, an issue that previously left drivers waiting for 45 minutes could be resolved in about 4 minutes. This helped recover nearly 30% of revenue that might otherwise have been lost. The goal isn’t to remove humans from logistics. It’s to let machines handle the repetitive investigation, cross-checking, and coordination that humans cannot perform at scale. The final financial and customer-impacting decisions remain with people. For us, this is the shift from a system of record to a system of action, and an important step towards truly AI-native logistics operations. I talked about this in more depth with The Economic Times. Link to the full story here: economictimes.indiatimes.com… #Logistics #EnterpriseAI #SupplyChain #ShipsyBrain
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Rishabh Katiyar retweeted
The world's most interesting logistics AI company. @Shipsy_AI has fully transitioned to AI. First customer was India's largest company. 10 customers with over $1m ACV. Largest customer $3.5m ACV. NDR 158%. Landing ACV $350k. 90% of pipeline in EU and US. In the ET article below @SohamChokshi shares the key aspects of Shipsy Brain that was just launched. Shipsy Brain is a fine-tuned model purpose-built for logistics. It builds on insights from more than 5 billion shipments, 50 billion operational events, and 1 billion decisions processed through the Shipsy platform. We simply cant be more excited about the trajectory of this company from our 4th cohort of @_surgeahead @abhishek_mohan @peakxvpartners
We just launched Shipsy Brain, and I want to tell you why it exists. Most logistics systems were built to record what happened. But the actual decisions still happen outside the system, in spreadsheets, emails, phone calls, and people’s heads. That’s what we’re changing with Shipsy Brain, our fine-tuned model purpose-built for logistics. It builds on insights from more than 5 billion shipments, 50 billion operational events, and 1 billion decisions processed through the Shipsy platform. It powers agents that can: 1)Resolve suspicious orders by contacting customers and guiding drivers 2)Validate proof-of-delivery documents and freight invoices 3)Detect operational risks and potential surcharge fraud 4)Take routine follow-up actions while escalating high-impact decisions to humans In one quick-commerce use case, an issue that previously left drivers waiting for 45 minutes could be resolved in about 4 minutes. This helped recover nearly 30% of revenue that might otherwise have been lost. The goal isn’t to remove humans from logistics. It’s to let machines handle the repetitive investigation, cross-checking, and coordination that humans cannot perform at scale. The final financial and customer-impacting decisions remain with people. For us, this is the shift from a system of record to a system of action, and an important step towards truly AI-native logistics operations. I talked about this in more depth with The Economic Times. Link to the full story here: economictimes.indiatimes.com… #Logistics #EnterpriseAI #SupplyChain #ShipsyBrain
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Shipsy is a clear example of doing a terrific job of leveraging AI as a force multiplier in an enterprise product suite. And we are seeing pull from global enterprises across US and EU!
We just launched Shipsy Brain, and I want to tell you why it exists. Most logistics systems were built to record what happened. But the actual decisions still happen outside the system, in spreadsheets, emails, phone calls, and people’s heads. That’s what we’re changing with Shipsy Brain, our fine-tuned model purpose-built for logistics. It builds on insights from more than 5 billion shipments, 50 billion operational events, and 1 billion decisions processed through the Shipsy platform. It powers agents that can: 1)Resolve suspicious orders by contacting customers and guiding drivers 2)Validate proof-of-delivery documents and freight invoices 3)Detect operational risks and potential surcharge fraud 4)Take routine follow-up actions while escalating high-impact decisions to humans In one quick-commerce use case, an issue that previously left drivers waiting for 45 minutes could be resolved in about 4 minutes. This helped recover nearly 30% of revenue that might otherwise have been lost. The goal isn’t to remove humans from logistics. It’s to let machines handle the repetitive investigation, cross-checking, and coordination that humans cannot perform at scale. The final financial and customer-impacting decisions remain with people. For us, this is the shift from a system of record to a system of action, and an important step towards truly AI-native logistics operations. I talked about this in more depth with The Economic Times. Link to the full story here: economictimes.indiatimes.com… #Logistics #EnterpriseAI #SupplyChain #ShipsyBrain
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Rishabh Katiyar retweeted
We just launched Shipsy Brain, and I want to tell you why it exists. Most logistics systems were built to record what happened. But the actual decisions still happen outside the system, in spreadsheets, emails, phone calls, and people’s heads. That’s what we’re changing with Shipsy Brain, our fine-tuned model purpose-built for logistics. It builds on insights from more than 5 billion shipments, 50 billion operational events, and 1 billion decisions processed through the Shipsy platform. It powers agents that can: 1)Resolve suspicious orders by contacting customers and guiding drivers 2)Validate proof-of-delivery documents and freight invoices 3)Detect operational risks and potential surcharge fraud 4)Take routine follow-up actions while escalating high-impact decisions to humans In one quick-commerce use case, an issue that previously left drivers waiting for 45 minutes could be resolved in about 4 minutes. This helped recover nearly 30% of revenue that might otherwise have been lost. The goal isn’t to remove humans from logistics. It’s to let machines handle the repetitive investigation, cross-checking, and coordination that humans cannot perform at scale. The final financial and customer-impacting decisions remain with people. For us, this is the shift from a system of record to a system of action, and an important step towards truly AI-native logistics operations. I talked about this in more depth with The Economic Times. Link to the full story here: economictimes.indiatimes.com… #Logistics #EnterpriseAI #SupplyChain #ShipsyBrain
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Beg to differ. Smart founders startup to solve customers problems AND they know that investors are running a sprint while they are running a marathon. They choose investors wisely, churn captable at right time to help early investors exit & get new ones who can support co further
The good founder starts up to solve customer problems. The smart founder starts up to solve investors’ (capital deployment) problems.
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Incredible! This is a really big leap for robotics
In-context learning for robotics is here. - Long-horizon tasks over 10 minutes long - Never seen during pre-training - Prompted with one video, no fine-tuning We are building intelligence from the foundations up, not from the top down.
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Rishabh Katiyar retweeted
“If it’s not painful you’re clearly not pushing hard enough.” @travisk on entrepreneurship, pain, progress, and excellence:
My conversation with @travisk, founder of Atoms and Uber. 0:00 Building Atoms & the Meta Problem of Management 3:51 The Appeal of Impossible Problems: Starting Over in China 12:19 Uber vs. Didi: Copycats, Hypergrowth & China's Rules 21:02 How Network Effects Become an Efficiency Fortress 31:48 Capitalism vs. the Taxi Cartel 44:05 The China War Goes Global & the Entrepreneur's Capacity for Pain 54:04 Life After Uber: Lawfare, Media Narratives & Reputation 58:21 What Founders Get Wrong About Venture Capital 1:08:35 The Fundraising Playbook: QED Storytelling & a Five-Room Auction 1:18:38 The Uber Coup, Radical Accountability & Outgrowing Fear 1:26:42 Why Specialized Robots Beat Humanoids at Industrial Scale 1:31:30 Finding Your Sport: Food, Mining & the Physical AI Stack 1:40:14 How to Build Many Companies Inside One Company 1:46:33 Entropy, Civilization & the Meaning of Progress Includes paid partnerships.
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Rishabh Katiyar retweeted
we're launching @neatlogs soon. the usual move is to follow the launch playbook. but i'm not convinced it works anymore everyone's guessing. some guesses just have bigger budgets. so over the next few weeks, i'll show you every bet we make. the launch is the experiment.
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Congrats @_saah1l and team @ShiprocketIndia! What a journey it has been!
Shiprocket IPO subscribed 102x. Congratulations @_saah1l @ShiprocketIndia this is so so well deserved. It is the consequence of more than a decade of effort. Of building a great business brick by brick. Of pricing the IPO right and leaving value on the table. Thank you for allowing us to invest. Ever onwards. To paraphrase what Winston Churchill said in another context. This is not the end. This is not even the beginning of the end. This is perhaps the end of the beginning. @InfoEdgeVC @kitty_agar
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Rishabh Katiyar retweeted
silk api's (by @rumik_ai) have reached 100K requests in the first few days of launch it's a different feeling because now i want others who are building on top of us to WIN! just to reiterate silk mulberry 1.5 is the equivalent to the best models in the world, at only ₹0.4/min - 95% cheaper. we now have the biggest consumer companies building on our voice models try yourself, link in the thread
🤖 Made with AI
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Rishabh Katiyar retweeted
IPOs are back. This is great news for the start up ecosystem. And for VCs it means exits. It has been known for sometime that India is the best IPO market in the world for companies below a market cap of USD 4 billion or thereabouts. It is also validation of what some of us have been saying for several years. It doesn’t make sense for Indian Startup’s and founders to flip to an overseas domicile or to incorporate overseas - you run a serious risk of not getting an exit for your investor through an IPO for a long long time. Sure you can do a strategic sale but that would most likely mean your own exit too. So if your ambition is to stay in the company for the long run and go public India is the best place for a start up to domicile @InfoEdgeVC @DPIITGoI m.economictimes.com/markets/…
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Key question that I ask as an early stage investor is not how will this startup fail but what if this idea works, and how big it could get if it does?
The VC bet is really about the potential for scale, not the likelihood of it. This is why you see immense failures, laughable-in-retrospect bets by VCs. The logic is simple: to attract power laws, you have to be ok with high variance bets.
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Rishabh Katiyar retweeted
Info Edge’s letter to shareholders capturing our investment strategy and an update on our investments in startups in AI, Deeptech and Consumer Tech got published today. Link attached. Feedback is welcome. bseindia.com/xml-data/corpfi…
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This is what PMF looks like IRL!? 😁
is this game that popular or is this an employee? @matiks_play @matiks
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Rishabh Katiyar retweeted
launching silk mulberry 1.5 one of the fastest multilingual voice models in the world it matches the best voice models in quality benchmarks (MOS) all this at more than 95% lower cost ₹0.40/min (~$0.0046/min) try now 👇
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Rishabh Katiyar retweeted
How to Earn a Billion Dollars: paulgraham.com/earn.html
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i look forward to our chinese brothers liberating the knowledge from within fable-5 and selling it to me at 5% the cost & 2x the speed
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