Neetika Bansal

Tradecraft AI feels like the most practical evolution of AI I've seen in a while. Instead of building yet another chatbot, this approach embeds real expertise into tools that actually understand the job they're doing. It’s like giving software the instincts of a seasoned pro. Especially in finance, this could seriously cut the noise and boost precision. #AI #Fintech #TradecraftAI #VerticalSaaS #Automation #Productivity https://lnkd.in/e7CgDiTc

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DeepSeek’s rise has been wild to watch. From hedge fund roots to topping app store charts, all while running models cheaper and faster than the big names? That’s no small feat. Their focus on reasoning models is especially interesting and definitely shaking things up in the AI space. #AI #DeepLearning #Innovation #TechTrends #MachineLearning #Startups https://lnkd.in/dDfmM99D

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If you’re a CEO/founder or PE operator and your tech org is: ❌ Shipping late ❌ Stuck in reactive mode ❌ Struggling through the Series A/B jump, or post-acquisition integration or after a major release It’s not “a delivery problem.” It’s an operating model problem. At the inflection point, the old playbook breaks: what worked at 10 engineers fails at 40… and breaks again across multiple products/teams. That’s where I come in as a fractional CTO: - Spot platform and delivery risks before they hit revenue and retention - Rebuild execution rituals (prioritization, ownership, release confidence) without derailing the roadmap - Align architecture + team structure to your value-creation plan (growth, margin, or exit timeline) If you’re feeling the strain of growth, I can help you turn “busy and reactive” into predictable shipping, and a platform that scales. #CTO #SaaS #StartupScale #PrivateEquity #TechStrategy

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Intuit Delivers Explosive Growth, But Why Did Shares Fall? Intuit’s Q2 FY25 numbers paint a strong picture. Revenue came in at $3.83B, up 20.3% YoY, outpacing both consensus ($3.74B) and its own prior quarter growth (15.1%). The company expanded gross margin to 78% (+130 bps YoY), while keeping SG&A (44% of revenue, -280 bps YoY) and R&D (21% of revenue, -190 bps YoY) under control. The result? Operating income surged +372% YoY to $340M, lifting operating margin to 9%. Net income exploded +2005% YoY, translating into EPS of $1.35 (+1787% YoY). On a segment basis, momentum is broad-based: Global Business Solutions: $2.23B revenue (+20.2% YoY), EBIT margin 74%. Credit Karma: $649M revenue (+33.8% YoY), EBIT up 109% YoY, margin expanding 15.5 pps to 43%. Consumer: $137M revenue (+21.2% YoY), EBIT margin +500 bps YoY to 43%. ProTax: Modest at $32M revenue, flat EBIT growth with margin compression (-2.6 pps). EBITDA grew to $660M (17% margin, +960 bps YoY), while Free Cash Flow was $356M (9% margin), though notably down 430 bps YoY. Guidance landed roughly in line: FY25 revenue of $21.0–21.2B vs. Street’s $21.1B, and EPS of $22.98–23.18 vs. $22.99 expected. So why the stock’s -5.6% reaction post-earnings? Valuation might be the sticking point. Despite the growth, Intuit still trades at 10.2x EV/Sales and a 38.4x forward P/E—lofty multiples in a market increasingly sensitive to cash flow. The drop in FCF margin this quarter didn’t help either, leaving investors questioning sustainability. Takeaway: Intuit is firing on nearly all cylinders—margin expansion, operating leverage, and standout Credit Karma growth. But at a premium valuation, even stellar execution is not immune to market skepticism when FCF trends wobble. #Intuit #Earnings #Finance #SaaS

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Thrilled to announce that Together Fund is investing in Sentra, alongside a16z speedrun! You track results in Jira. Decisions in Notion. Conversations in Slack. But the reasoning, the debates, trade-offs, and context behind why you chose A over B, disappears into what we call "Dark Matter." A decision made in March looks insane by July because no one remembers the constraints that made it smart. I lived this firsthand at Twitter scaling from 800 to 8,000 employees, and at Google while launching AI Overviews to billions at planet scale. The problem isn't process. Process is compensation for something deeper: organizational amnesia. An organization’s "Systems of Record" doesn’t solve this, they encode it. They store what happened, never why. That's why we are investing in Sentra. Sentra is the always-on collective memory that eliminates organizational amnesia by maintaining accurate context for all members and agents, functioning as an operational nervous system. It connects to every channel where work happens, meetings, Slack, email, code commits, docs, calendars, and treats them not as artifacts to search, but as living signals to synthesize. The fleeting and the permanent, unified into a memory that understands. The founding team is built for this: - Jae Gwan Park (CEO): Product-first founder, memory systems research at UofT and MIT - Ashwin Gopinath (CSO): Former MIT professor, created "Reflexion" (NeurIPS 2023), agents that learn from mistakes, 2x founder - Andrey Starenky (CTO): Early Vapi engineer, ex-IBM, built to process enterprise-scale data firehose Together is an operator-led fund. We invest in problems we've lived. This is one of them. Many congrats Jae, Ashwin and Andrey, we are so excited to partner with you! Read the full thesis: https://lnkd.in/gixj9cE4 Book a demo: https://www.sentra.app/ #OrganizationalMemory #AI #Sentra #TogetherFund #a16z #ContextGraphs

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Anthropic's Claude Sonnet 4.5 is coming to Glean this week. In addition to being the leading model for coding, our evals show that the model delivers significant improvements over its predecessor by efficiently solving 10% of previously unanswerable enterprise queries, thanks to optimized parallel tool calling and fewer planning iterations. See more details on our blog: https://lnkd.in/gXUEvGKW

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Investors take note. The Sass-pocalypse is overly sold off. Innovation and AI adoption will look like this going forward. Time tested platform and capablities, like Intuit, combined with agentic orchestration. Both pieces create the big wins for customers.

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Most billion-dollar ideas die because the barrier to build is too high, Emergent changed that and hit $50M run rate in 7 months. How did we get here? It was a pleasure talking to Hemant Mohapatra on the Lightspeed India podcast about the Emergent story so far. We covered everything from technical moats to what it takes to build successfully in AI. Hemant and I share a great rapport, and I think it really shows in this conversation. Here are the some of the best takeaways: - Control the infra: Most AI startups are just a UI slapped on a 3rd party API. That's a feature, not a company. For production-grade software, we built our own container tech and coding agents from the ground up because if you don't control the infra, you don't control the feedback loop. - The 0.2s Rule: I tell my team AI is an "Olympic race". No one remembers the silver medalist who lost by 0.2 seconds. In a high-growth startup, every delay is a compounding loss, and we fight entropy every single day to save those fractions of a second. - Don't build moats around problems: Most founders build a "moat" around a hard problem just to avoid it. We do the opposite: identify the 1% of the problem that scares you most, whether it's deployment stability or eval quality, and pull the entire org toward it. Solve the hard thing first and the moat follows. - Launch before you're "ready": We sat on the world's best coding agent for 6 months. Big mistake. The moment we went live, I spent 24/7 on customer support. That raw, painful feedback loop is the only way to close the gap between a "cool demo" and a "must-have product". - AI is Bitcoin at $1: I've posted this before. The exponential curve is just beginning. Drop anything not related to AI. Start or join an AI-native company (we're hiring!). The cost of starting has collapsed, domain experts are the new architects. This podcast also brought out my startup motivations, my dynamic with Madhav Jha, and the resilience required to go from a plateau back to the drawing board. We also talked about the "whiteboard graveyard," the millions of ideas that die because the barrier to building is too high. The reason why Emergent exists. This is one of my favourite chats. Watch the full episode here: https://lnkd.in/gjFzKbY4 #aistartup #buildinginai #vibecoding

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As AI lowers the barrier to coding, more people are becoming developers—and more developers are getting into programmatic trading—so we’ve launched a powerful, modern trading API. Many of Public’a existing customers are developers or technical. To them, having seamless API access to your own brokerage account seems obvious. But almost no brokers offer this. We talked to a lot of these customers. Turns out most brokerage APIs are decades old. Clunky. Not up to modern standards. And not listening to dev feedback. A good signal of PMF is how passionate people are about the feedback they’re giving you. You quickly know if you’ve hit a real pain point. In our case, what we got back was 10-page documents; 60-page slideshows; just tons and tons of feedback complaining about existing solutions and offering suggestions on how to improve. That lead us to build something better: The Public API. Clean. Modern. Designed for today’s builders and programmatic traders. You can use it for: - Stocks (with top execution quality) - Options (all levels, with rebates!) - Yield products (with industry leading yields) Coming soon: - Crypto - Bonds - Support for additional account types: corporate, IRA, etc Right now it’s by request only — but DM or comment if you want access and we’ll fast-track you.

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✨ A very fun start to Frameworks for Growth - a new video series from Vanta. "Frameworks" are core to who Vanta is: -We support 40 different compliance frameworks within the product -One of our principles is "Decide with frameworks," and a common question in meetings at Vanta is, "what framework did you use for deciding [x]?" -And now, we're hoping to provide founders and early operators with some frameworks *they* can use for scaling their businesses in the age of AI. Thanks to Garry Tan and Christina Cacioppo for this awesome first conversation. You should watch the whole thing, but tldw - one framework for growth: learn an industry deeply and vibe code towards something people want.

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late-breaking results: emergent capabilities after ~10M txs of pretraining! at Adyen we've been working on a unified approach to modeling based on foundation models: unsupervised pretraining and transformers. A textbook case of emergent capabilities? Or just an illusion of course-grained metrics? (Experiments by Hanna van der Vlis & Martin Iglesias)

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As always, Shawn Malhotra said it very well. On today’s earnings call, Varun Krishna and Brian Brown clearly outlined how we’re progressing against our mission. What’s exciting is seeing the ecosystem come together with search, origination, and servicing all powered by technology and AI at real scale. The real unlock is the platform work and collaboration behind it with architecture, data, and AI operating as one system. We’re not just improving individual moments in the journey. We’re building a connected homeownership platform designed to help everyone home. Proud of the teams turning vision into reality. #rocket #helpeveryonehome #earningscall #rocketpro #homeownership #missionfocus #Compass

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Excited to share that Olyv has raised $23M (~208 Crore) in our Series B funding round, a significant milestone for the platform we have been architecting over the years. My journey at Olyv has been closely tied to building and evolving our core systems through multiple lending cycles and regulatory shifts. From scaling our credit decisioning engine to strengthening distributed architecture, data pipelines, and real-time risk frameworks, the focus has always been clear — build infrastructure that is resilient, intelligent, and compliant by design. A major part of this evolution has been our in-house ML and Data Science capabilities. We have invested deeply in building proprietary models, optimising underwriting logic, and enabling continuous learning across the credit lifecycle. Working alongside our co-founders has reinforced the importance of long-term architectural thinking. Speed matters, but structural integrity matters more. Every system we have scaled has been grounded in that philosophy. As we enter this next phase, we will continue advancing automation, strengthening platform intelligence, and engineering systems that can responsibly serve millions at scale. Grateful to our technical leadership who turn complexity into momentum—through sound judgment, technical rigor, and execution excellence. Proud of the entire Olyv team for building with depth, rigor, and ownership. Onwards and Upwards. https://lnkd.in/gABaXZpg Rohit Garg Amit Chandel Jayant Upadhyay Vinay Kumar Singh Pradhuman Singh Abhishu Khekre Deepak Mishra Olyv

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Right in time for the holiday reservation rush, we’ve expanded agentic restaurant booking in AI Mode to more people in the U.S. – no Labs opt-in required! This is a major shift from the standard way of planning. Typically, for something like booking dinner plans, we’re used to searching step-by-step: find the restaurants, check the hours, then cross-reference availability. But what if you could do it all with a single query? ⚡️ With agentic capabilities in Search, you can take the busywork off your plate. Here’s how it works 🔍: The orchestrator: When you ask AI Mode for something like “dinner in Little Havana for 5 people with live music and outdoor seating”, our model creates a plan that identifies every dependency. Specialized agents working in parallel: Instead of linearly searching the way we would, the orchestrating agent delegates tasks to specialized agents that use a hybrid approach of live web browsing and tool integrations to find real-time availability. The result: You get suitable options that match your specific needs, so all you have to do is book and enjoy your meal.

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87% of YC companies are enterprise. Now it makes complete sense that AI is starting in enterprise. The models are expensive to run, and enterprise lets you automate workflows and vertically integrate. But to reach billions of people in their daily lives. The real prize is still consumer. A lot of founders are scared of consumer right now. At any point, OpenAI or Google can vertically integrate and kill your company. But you can make a similar argument for enterprise applications. The other part is that consumer is genuinely hard. Distribution is more mature, the App Store feels saturated, and there aren’t obvious new channels. At the same time, we are in the midst of a revolution. Interfacing with the world will be more than just a handful of chatbots. If you pay attention to daily life, to your parents, your kids, anyone close to you, you’ll notice how many small, annoying things still exist. Yes, the App Store is mature. But the top apps right now are AI apps. If something is truly great, innovative, or fun, people will go out of their way to find it. Menlo Ventures analyzed routine tasks of US adults. The Takeaway: Fewer than one in five people actually use AI for their day-to-day tasks. Most people stick with whatever system they already have, even if it’s annoying. At Hands, we're building with these 3 rules in mind: 1. Meet people where they already are 2. Make it easy to try. 3. Prove the product's value almost immediately

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Ask AI how OpenClaw can automate workflows in your business, then send me a message and I'll get that all setup for you. Setting up an agentic system can be tricky. Northstar AI Labs has been deep in the weeds, developing AI solutions. If you have heard of AI automation, but you don't want to DIY, a hassle free installation is just 1 message away.

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Just downloaded Tesla Robotaxi in SF, a real threat to Uber, Lyft, and Grab? Most experts measure Robotaxi by today’s metrics: cost of operations, active users, global adaptability. By that logic, scaling looks impossible. But Tesla flips the script. Unlike Waymo’s costly retrofits, a Tesla only needs a software update to become a Robotaxi. With 1.7M+ Hardware 4.0 vehicles already on the road, the scale potential is unmatched. Cars shift from being liabilities to income-generating assets overnight. This isn’t just product innovation, it’s a redefinition of value. At ElasticDash we’re doing the same for QA. Today, QA costs are labor-driven, with humans building and maintaining tests. Our AI agents change that: they build and adapt end-to-end tests automatically as your app evolves. Just as Tesla is rewriting mobility economics, we’re rewriting QA economics. ElasticDash is on the way.

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If you're keen on real AI use cases, this is actually quite powerful and I encourage you to read further (and help us build: https://lnkd.in/gsYnAfKK) At Hex, we've found our Threads product (https://lnkd.in/gJKCurJB) is the best way to summarize customer support load, because we're not limited to what's in the customer support portal - we can ask in terms of active users, customer size, feature adoption, and still get great answers. This is based on a strategy of keeping your data warehouse as a source of truth, and all related data work in one place - Hex - so that AI understands concepts in your business and makes smart connections between things. Arguably, ALL data analysis for a particular vertical is eventually best achieved in a tool like Hex, connected to curated data in your data warehouse. Wait - can't I do this directly in the customer support tool? Can't I connect an MCP server and get a functionality like this in one of my other AI enabled products? Maybe, but you have 100+ SaaS apps, so play this scenario out: - How many places are you going to sync your core data sets? - How many cross product integrations are going to exist, and how many of them are you going to enable? (100 choose 2 is ~5000) - In how many of these products will all users have seats? - In how many of these products will these seats have permissions to use AI features? (Our support tool just told me I don't have permission to ask the agent questions, probably because I don't have the right seat type, which means more $$) My bet is a on a world with ONE place for all analysis.

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