Bobby Srinivasan

There are a lot of NPCs in venture capital.  "Our proprietary deal flow enables us to invest in category-defining companies that reshape the world, blah blah blah". It is our opinion that many pre-seed investors don’t understand the triggers that tell them to “Go!” on deals. Sure, they have some canned answers, but the truth is that most VCs just fall in love with a founder for a variety of reasons, subconsciously decide to invest, and work backwards to write the investment memo. Like a Texas landman wildcatting for oil by drilling on random spots that “feel good”. And ego too… it's natural to want to beat a competitive firm chasing a deal, but it's not investing. This is not us. We're drawn to a patch of land that we uniquely understand, and we’re punching all our holes in it.  Going into our 4th full year, we have a machine that's dialed to self-select, filter, and repeatedly invest in a distinct breed of person. We don't care what other investors are doing, I assure you we aren't paying attention. absolute focus nuclear work ethic obsessive curiosity delusional conviction radical agency undeniable competence highly opinionated etc. Then we bring them to an island where they race to $1m+ in revenue with 60+ other portco founders for 75-100 weeks. Hyperbolic and extreme? Yes. But this founder profile sure has created a lot of +500X outcomes. Jason Calacanis will opine on All-In about the reasons he “knew” Uber would become a $100B company when he invested on a $5m cap. In private, I bet he’d admit he had no idea. He just knew Travis Kalanick was a killer. Jason wasn’t right about Uber; he was right about Travis, and got lucky when Uber had legs. The idea has to be huge, but we think pre-seed investors who continue to try and pick companies will struggle going forward. Technology is changing too fast for snapshot opinions to be relevant in 10 years. People don't change though. This is why we invest in Travises, not Ubers. Since 2022, we’ve offered 56 term sheets and 53 have accepted. That's a pretty high hit rate given our odd and inconvenient terms. Last year we saw 3,500 decks and applications. This year we processed just shy of 10,000 and will be raising our third fund to do it all over again. We are going to be the best at attracting and picking extreme & competent founders at pre-seed, and betting on getting absurdly, outrageously lucky on more companies because of it. justlaybrick.com

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The AI boom is driving unprecedented material and process complexity in semiconductors. Even as fabs and toolmakers approach physical limits, chip complexity keeps rising—pushing yields down and downtime up. Yet root cause analysis (RCA) in fabs remains largely manual. Engineers still spend 20–40 hours per incident, stitching together data across disconnected systems to understand why a tool or process failed. That changes now. ThirdAI Automation is building an RCA copilot for high-complexity manufacturing—using causal AI to move teams from detection to diagnosis to resolution, faster and with far greater precision. The opportunity is massive: $1.5T+ in annual downtime across semiconductor fabs, utilities, and advanced manufacturing. Super excited to back ThirdAI on this bold mission to redefine advanced manufacturing. Thrilled to be partnering with Capria Ventures on this journey. Raghav Gupta | Sateesh Andra | Surya Mantha | Ankur Dubey | Medha Kannapally Ministry of Electronics and Information Technology Department for Promotion of Industry and Internal Trade #startupindia #Semiconductors #Manufacturing #AI #RCA #FrontiersOfEngineering #EndiyaPartners #ThirdAI https://lnkd.in/gCb4HdMy

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The next great AI race has begun, and it’s not about compute or talent. It’s about data. We spent $400 billion this year on GPUs and billions more competing for AI talent. But the investments that matter most now are in data quality. The difference between economically useful AI and “slop” isn’t teraflops or PhDs. It’s expertise. Early AI models scraped the open internet, learning from keyword-stuffed posts and half-truths. They became impressive generalists but poor economists, consultants, or engineers. Real economic value comes when models learn from experts: the people who actually know how to make things work. That’s where Mercor comes in. Mercor works directly with experts to learn how they think and make decisions, and then trains AI on that knowledge. It’s transforming lived expertise into structured learning for machines. The shift is already massive. The enterprise SaaS market is worth $1.5 trillion, but even that’s just a fraction of corporate operating spend. AI is poised to double or triple that number by making labor (roughly 80% of business costs) more valuable and scalable. Mercor is rapidly becoming the place where AI learns to do real, economically valuable work. The first ten-trillion-dollar company may well be built not on silicon, but on expertise. Mercor crossed a $500M revenue run rate this September and is profitable, with zero enterprise churn. We at Felicis are thrilled to invest in their Series C because they are building the next generational platform technology company. Mercor is re-defining the horizon of expertise: unlocking not only corporate value, but potentially the next chapter of human progress. Read more from Mercor: https://lnkd.in/gPpqCrBq More from me on X: https://lnkd.in/gc-2w_6h

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Key takeaways: - The core shift is economic, not hype. LLMs turn “AI” into a recurring operating cost, so pilots quickly become capacity planning, governance, and ownership. - Power and timelines show up because production inference needs sustained compute capacity. When usage increases across the org, you run into procurement, security review, integration work, and infrastructure readiness challenges. - The winners are not the teams with the flashiest demos. The winners are the teams that convert compute into measurable outcomes inside a workflow with traceability and controls. - In 2026, “AI strategy” that is not tied to a specific workflow, KPI, and operating model is mostly theater. If you are building the application layer on top of the datacenter buildout, execution systems, workflow automation, data-layer tooling, compliance automation, I want to see what you are shipping. Founder intro: www.axion.ventures

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Let's invest in DeepTech! That's what ~most VCs are flooding discussion boards about across our whatsapp groups. There's a twofold reason for this. 1. People are actually interested in DeepTech : ~20% of the reason 2. LPs, from family offices to Govt Funds are focused here : ~80% of the reason But do folks actually understand what DeepTech is? I've seen messages flood groups on 'how do you value a DeepTech company' and it's hilarious. Let's be a bit real here. DeepTech is basically creation / optimization of hard or soft technology, mainly hard tech (physical machinery and technology, actual engineering). For decades, most engineers in India have effectively used engineering as a tool to get an MBA, then become a manager. STEM aptitude diverted to excel. That's because India largely skipped the manufacturing revolution that characterises developed economies. USA, Europe, Japan, Korea, all went through HUGE manufacturing phases, building IP and technological prowess. Our R&D spends have been abysmal across India Inc, just enough to keep uss going (with a healthy dose of import restrictions thanks to HUGE tariffs). Now, as we're poised to either make the next decade (and more) India's, or get left behind. To fully grasp our place on the world stage, we NEED to build. Battery technology, rockets, medical devices, new materials, machining tools, the list can go on and on. These companies require a lot more patience and capital to eventually grow into large businesses (with large outcomes). My framework for evaluating these companies is as follows - Greenfield : Absolutely new technology, or being built with TANGIBLE IP. I love younger teams for this, once you've qualified as an engineer and got a bit of experience, you can tackle hard problems. I recently met a graduate who spent their ENTIRE college time building a better flow battery stack (probably investing here) Brownfield : Very established space, here you need some work experience to be able to understand what other people are doing, and 'best practices', so you know what can be improved. A great example here would be the Founders of Skyroot, who worked at ISRO before moving out and building. Or AgniKul, where the Founders had this experience from their IIT Madras mentors. As far as valuations go, at the early stages you need to focus on the Founders, as well as levels of Technological Readiness (TRL, I've added a great guide in comments). Typically, TRL 3 to 4 is where you'd speak to investors. Ideally TRL 4 should be worked on in conjunction with a university lab/accelerator. #venturecapital #startups #deeptech

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AI is revolutionizing the VC/PE model in two different ways Venture Capital now leverages AI to surpass traditional networks by scanning vast data landscapes for early signals of promising startups. AI accelerates deal sourcing, compresses due diligence into hours, and replaces intuition-driven risk judgments with predictive analytics that uncover subtle patterns across markets and founders. This shifts power from serendipity to algorithmic foresight, enabling VCs to identify opportunities with unparalleled speed and precision. Private Equity pivots toward fortifying portfolio companies against AI disruption by investing heavily in data infrastructure and strategic acquisitions that enhance AI capabilities. PE’s focus rests on integrating AI to optimize operational efficiency and competitive positioning within mature firms. Due diligence deepens, addressing AI-specific risks such as intellectual property, data provenance, and regulatory compliance, requiring nuanced deal structures and specialized legal counsel. In summary, AI transforms VC into a proactive, data-centric hunt for emergent innovation, while PE refines post-investment integration and resilience-building amid rapid AI-driven market shifts. Both landscapes demand fresh competences and recalibrated strategies, reconfiguring capital flows and decision paradigms across the private investment spectrum. #startups #venturecapital #privateequity #investing Follow me on tiktok and X for more content on startups and venture capital Tiktok: https://lnkd.in/guzinMA9 X: https://lnkd.in/gyJjY4Yj

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There’s a fundamental shift underway in how the physical world operates. For decades, industrial intelligence has largely been human-led, built on experience, intuition, and manual processes. What’s changing now is not just automation, but the systematic embedding of intelligence into machines, workflows, and infrastructure through software, data, and AI. This transition from human-centric decision-making to system-level intelligence feels similar to what software did for digital businesses over the last two decades. This time, it is happening across manufacturing, logistics, energy, and broader physical industries. The scale of impact is likely to be much larger. At Inflexor Ventures, we have been spending time thinking about this shift and where value will accrue. The idea of a “Physical Intelligence Stack” attempts to break this down, what layers exist, where defensibility lies, and how startups can build meaningful businesses in this space. Looking forward to discussing this in more depth at the launch of our latest thesis, in collaboration with Society for Innovation & Entrepreneurship -SINE IIT Bombay . If you are building, investing, or just curious about where industrial intelligence is headed, this should be a useful conversation. 📅 14 May 2026 ⏰ 4:00 PM – 6:00 PM 📍 SINE, IIT Bombay Register here: https://luma.com/s0tjy98k

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Fal operates in a highly competitive space but carved out a unique niche by focusing on generative media. Effectively creating a category of its own   From model creators to inference providers, cloud platforms to packaged solutions, many players in the space are both competitors and potential partners to fal   Their strategy of building an abstraction layer became a powerful wedge, helping them scale. As the market continues to swing with each new wave of innovation, Fal has been smartly placing bets across the value chain   The real key is maintaining a sharp tech edge. Staying ahead of major shifts, moved by big tech, and the next generation of models. Fal has consistently won by leapfrogging the competition 👇Hear it from Gorkem at e2vc Summit 💙

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We are pricing most AI startups wrong. SaaS era pricing model was based on the typical growth profile of a SaaS company: some R&D -> first sales -> early scaling -> more R&D investment -> enterprise scaling Where margins would eventually go up to 80% (with most of the cost being sales and marketing). AI startups are VERY different, since even the best ones pay the "Inference Tax" where 40-60 cents on a dollar would go to infra, foundational models, cloud, silicon, and power. Everyone's margins within the AI stack are akin to industrial manufacturing - thin and scale dependent (with the notable exception of Nvidia which has a 75% margin and readily invests the oversized proceeds into the application layer or lends to hyperscalers to spend on more of their equipment) The value accumulates in owning users' behavior, being their first or second screen, and everything else upstream is increasingly commoditizing. We are in a market where 16 companies have a frontier model. More companies can serve inference in a cloud. More companies can find lots of land with water and electricity for datacenters. Jensen put it nicely - think of a datacenter as a modern day's factory: electron go in, intelligence goes out. So your "AI agent for banking AML and compliance" is not a SaaS. It's a silverware customization service trying to build a brand. But the spoons are mass produced in a factory from sheet metal, coming from mills, which get ore from mines. Metal and electricity come in - consumer emotions (and some utility) come out. However, this doesn't mean that paying a SaaS multiple for an application layer/agentic AI startup today is wrong. But let's be honest - we're not paying a premium for the huge terminal value (as with SaaS), but rather for a much bigger revenue growth potential. Never before in history we saw a 5 people team in SF routinely challenging $100B incumbents disrupting their business models and offering the market expansion to $500B. Never before we've seen companies like Perplexity adding 50% to $300M ARR in a month or Higgsfield growing to $300M from 0 in a year. They don't build their spoon factories or steel mills. They deliver customer value expanding their markets tenfold. So for now SaaS multiples would do. The "bubble" will not burst (although some pockets of overpricing might correct). But we should change the way we approach valuations for the future. (read about this and more in our annual State of AI report) here: https://lnkd.in/dApmrJ2C

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You can have groundbreaking science, elegant engineering, and even early customer interest. But without this one thing, you don't have an investable business. After two decades running IP strategy projects and now leading UC Berkeley's Deep Tech Innovation Lab while building the Berkeley Gateway Accelerator, I've watched countless brilliant technologies die in the valley between breakthrough and business. The problem isn't what most technical founders think it is. It's not about having better tech. It's not about getting more funding. It's not even about finding product-market fit. The fatal flaw shows up much earlier—and it's almost always the same mistake. In my latest piece, I answer questions from a Taiwanese entrepreneur about what really separates deep tech ventures that scale from those that stall. Including one provocative suggestion for Asian ecosystems that has nothing to do with technology. Read the full conversation, linked below. #DeepTech #Innovation #Startups #IntellectualProperty #VentureCapital

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Most VCs talk about “pattern recognition.” The best ones practice pattern resistance. Too much of the venture world rewards consensus and familiar playbooks. But if you're building—or backing—something exceptional, it won’t look obvious at the start. In my latest Groundwork essay, I break down why pattern recognition can become a crutch, not a compass, and why I believe investing is a game of outlier conviction, not checklist consensus. Read it here: https://lnkd.in/gbRyhR4T #venturecapital #startups #investing #Groundwork

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100 founders, builders, researchers, investors and thinkers. One big question: how to solve some of the largest challenges building at the frontiers of AI. From physical compute scaling constraints to applying AI to unlock civilizational progress from material science to drug discovery. Camps, hikes and heated debates on human x AI co-existence: this was Climbing Hard AI Peaks. Thanks to everyone for coming out and making it so special. We learned a lot from all of you. Big shout-out to our founders working on some of the biggest AI unlocks, the many researchers and builders from Center for Digital Technology and Management (CDTM), ETH Zürich, Stanford University and special guests from Anthropic, OpenAI, Google DeepMind and more. David Byrd Michael Wax Jason D. Whitmire Chad Fowler Jonah Anders Kaplan Christine Fuchs Sam Harrison Mehdi Ghissassi Antoine Moyroud Wulfie Bain Eva Spannagl Nathan Gruber Judith Dada Juan Benet Zoe Weinberg Haya Hanna Sophia Kalanovska, PhD Clayton Mellina Ricardo Sequerra Amram Evan Phoenix Hendrik Dietz Katie Hewitt Molly Mackinlay Omer Shlomovits Zied Bahrouni

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A single plugin can move markets. Models / APIs → Workflows / Skills → Solutions / FDEs Foundation model companies are no longer just “plumbing.”  When the model owner ships the workflow, value migrates up the stack. Anthropic’s launch of legal plugins for Claude wasn’t just a product update — it was a signal. Public legal and information-services companies sold off sharply, as investors repriced what had been viewed as “AI beneficiaries” into potential displacement targets. ▶️ So what changed? /review-contract /triage-nda /vendor-check  /respond Claude introduced legal-specific plugins that automate real workflows: contract review, NDA triage, compliance checks, legal research assistance. These aren’t demos — they’re opinionated, end-to-end workflows, shipped directly into enterprises and bundled with the core model subscription: ▶️ Why the market reacted? ➖ Proprietary data alone is no longer a sufficient moat when competition shifts to the workflow layer ➖ The “ChatGPT for X” playbook just got much harder if foundation model companies ship vertical products themselves ➖ Investors may be pricing in a future where model vendors move beyond APIs into full solutions — potentially even with forward-deployed teams For founders and VCs, this forces a reset. The key question is no longer “does this use AI?”.  It’s “what actually prevents the model provider from building this natively?” Overreaction — or the start of a structural shift? Do model companies stop at tools, or move decisively into solutions?

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India is not behind on talent. It is behind on risk appetite. Building LLMs or foundational AI infra is not about capability. It is about capital. Not just any capital, but capital that is comfortable with long timelines, unclear outcomes, and massive burn. Most of India’s funding still chases near-term traction. SaaS, fintech, consumer. Sectors where returns are easier to model. But AI infra does not play by those rules. It demands belief before proof. The Middle East launching a sovereign fund to accelerate AI through public private bets is not just money. That is intent. India will not build foundational AI by hoping startups will figure it out alone. It needs conviction at the system level. And a willingness to back uncertainty at scale.

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Unicorn Scale Lab by 13ThrustVal Group is an exclusive capital-driven scale engineering platform designed for growth-stage companies targeting $1B+ enterprise valuation. This is not an incubator. This is a high-intensity ecosystem integrating private equity thinking, venture capital discipline, governance architecture, strategic expansion frameworks, and institutional capital readiness into one structured growth blueprint. Built for ambitious founders and emerging market leaders, the program supports companies from strategic diagnosis to global expansion, M&A readiness, IPO pathway structuring, and enterprise value maximization. Through the 9–12 month structured phases, participating companies gain institutional-grade governance, investor readiness, capital structuring capability, and a scalable roadmap to become credible global unicorn contenders. Unicorn Scale Lab is designed for founders who are serious about scale, discipline, execution, and long-term market leadership. #13ThrustVal #13ThrustValGroup #UnicornScaleLab #VentureCapital #PrivateEquity #StartupGrowth #ScaleUp #StartupScaling #UnicornStartup #FutureUnicorns #GrowthStage #GlobalExpansion #InstitutionalCapital #GrowthCapital #BusinessScaling #StartupFunding #EnterpriseValue #IPOReadiness #MergersAndAcquisitions #StrategicGrowth #FounderCommunity #StartupFounders #BusinessLeaders #InnovationLeaders #GlobalBusiness #FutureOfBusiness #AI #ArtificialIntelligence #DeepTech #FinTech #HealthcareInnovation #InfrastructureDevelopment #EmergingTechnologies #DisruptiveInnovation #InnovationDriven #CapitalMarkets #AlternativeInvestments #MultiAssetCapital #GlobalInvestors #InvestorReadiness #StartupEcosystem #InnovationEconomy #ExecutionExcellence #LeadershipDevelopment #StrategicPartnerships #GlobalNetwork #ValueCreation #MarketLeaders #CategoryLeaders #NextGenCompanies #GlobalScale #CrossBorderExpansion #BusinessTransformation #OperationalExcellence #Governance #BoardStructure #FinancialModeling #InstitutionalGovernance #RiskManagement #CrisisManagement #StrategicMAndA #AcquisitionStrategy #BusinessAcceleration #GrowthStrategy #StartupJourney #InvestorPitch #Fundraising #CapitalArchitecture #ScaleEngineering #InstitutionalBranding #GlobalPositioning #BusinessExpansion #LeadershipNarrative #EnterpriseGrowth #ScaleFramework #VCFunding #PrivateMarkets #StrategicExecution #GlobalPlatform #InternationalMarkets #BusinessStrategy #ScaleCapital #GlobalOpportunities #FoundersNetwork #Entrepreneurship #StartupLife #TechInnovation #DigitalTransformation #FutureLeaders #InvestmentStrategy #CapitalDeployment #GlobalVC #VCNetwork #InvestmentFirm #StartupOpportunities #InnovationHub #CapitalPartners #IndustryTransformation #GlobalImpact #FundingSupport #StartupSuccessJourney #InvestmentEcosystem #BusinessGrowth #GlobalMarkets #FutureEconomy #TechDriven #Leadership #CorporateGrowth #StrategicGrowth #StartupAcceleration #InvestingWorldwide #BuildGlobal #GrowthEngine #Innovators #EntrepreneurialMindset

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Most founders say “IPO” when asked about exit. The data tells a different story. According to PitchBook and NVCA, acquisitions have represented the majority of venture-backed exits by volume for decades. And in 2024, the contrast was stark. In the US, there were fewer than 50 VC-backed IPOs, versus over 1,000 venture-backed M&A exits. This is not a cycle anomaly. It is how venture exits have consistently worked across booms and downturns. Yet many founders still build as if IPO is the default outcome. That gap between reality and mindset matters. Because building for M&A requires a different way of thinking. You are not selling once. You are selling twice. First, to customers. They pay for usefulness today. Later, to a strategic buyer. They pay for relevance, leverage, and fit tomorrow. Most founders optimize only for customers. Very few design for buyers early enough. The strongest founders reverse engineer in two directions. They ask, what product will customers buy now? And at the same time, what kind of company would a strategic buyer want to own later? The product is built around customer pain. The company is shaped around future strategic value. In venture-backed companies, exits are not optional. They are how the system works. Build something customers want today. Build something someone will want to buy tomorrow. That is how most real exits happen. NUK! #venturecapital #founders #startups #m&a #ipo

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Sam Altman is killing Mckinsey , Deloitte, PWC, KPMG and Boston Research, etc. OpenAI just launched an enterprise consulting arm and it’s moving fast to capture a multi-billion dollar gap in the market. Here’s the playbook: 1. Consulting as GTM → Start with services, stay with product. 2. Force upfront commitment → OpenAI charges $10M to skip “pilot purgatory.” 3. Embed into workflows → Once AI runs inside your systems, it’s impossible to rip out. 4. Escape platform lock-in → Consulting revenue doesn’t flow through Microsoft. 5. Deploy experts fast → Engineers inside client orgs, like Palantir’s FDEs.

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It was a pleasure to sit down with Kirk Falconer at Buyouts for their “Off-Duty” series to reflect on the experiences that have shaped my journey in the PE industry.   From growing up in New York to building Brightstar Capital Partners alongside an incredible team, I’ve been fortunate to learn from great partners and take on the challenges that come with growing a firm from the ground up.   Along the way, I’ve come to appreciate the importance of staying curious, whether it’s thinking about how AI will shape the future, or importantly, making time for family, travel, and the interests that help keep perspective. 

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Engineers India Q3 FY26 Results: PAT Surges 219%, Stock Jumps 12% https://lnkd.in/gdv23K6d 📊 Key Financial Highlights (Consolidated) Metric Q3 FY26 Q3 FY25 Change Net Profit (PAT) ₹347.2 Cr ₹108.7 Cr ▲ +219% Revenue from Ops ₹1,210 Cr ₹764.5 Cr ▲ +58.3% EBITDA ₹352.2 Cr ₹97.9 Cr ▲ +260% EBITDA Margin 29.1% 12.8% ▲ +1630 bps EPS ₹6.18 — — Stock Price (NSE) ₹231.00 — ▲ +12.37% (1D 📈 Standalone Performance (Company Reported) Metric Q3 FY26 Q3 FY25 Change Revenue ₹1,194 Cr ₹750 Cr ▲ +59% PBT ₹395 Cr ₹118 Cr ▲ +235% PAT ₹302 Cr ₹88.1 Cr ▲ +242 🏭 Segment-Wise Performance Segment Revenue (Q3 FY26) YoY Change Contribution Consultancy ₹490.1 Cr ▲ +16.4% ~38% Turnkey Projects ₹720.1 Cr ▲ +109.7% ~58% 📦 Order Book – All-Time High Metric Value Order Book (Dec 2025) ₹12,538 Cr Current Order Book ~₹15,670 Cr (All-time high) Order Book Composition 60% Consultancy / 40% Turnkey Revenue Visibility Strong for upcoming quarters 🌍 Major Wins – Dangote Refinery Project Detail Information Client Dangote Group (Nigeria) Value $350–360 Million (~₹3,150 Cr) Nature Mega refinery expansion project Significance Largest overseas order ever 🌐 International Business Momentum · Overseas markets now constitute 65% of fresh order inflows (as of Jan 2026) · Domestic orders: 35% · Active in Middle East, Africa, South America --- 📉 Stock Performance Metric Value Current Price (NSE) ₹231.00 1D Change ▲ +12.37% 2-Day Gain ▲ +22% 52-Week High ₹255.45 52-Week Low ₹142.20 Market Cap ~₹11,344 Cr Volume Surge 13.9% equity traded in 2 days --- 🏦 Brokerage Views & Target Prices Brokerage Rating Target Price Key View Prabhudas Lilladher BUY ₹261 Consultancy core growth engine; strong prospects in domestic & international ICICI Securities — ₹215 achieved Expect 20% CAGR in consultancy revenue (FY26–29E); order book 4x TTM Historical Avg PE — 25x Currently trading at 34x FY27E core EPS --- 📌 Key Takeaways ✅ Stellar earnings growth – PAT up 219% YoY (consolidated), 242% (standalone) ✅ Margin explosion – EBITDA margin expanded 1630 bps to 29.1% ✅ Order book at all-time high – ₹15,670 Cr, providing strong revenue visibility ✅ Dangote win – $360M order from Nigeria, largest overseas contract ✅ International momentum – 65% of fresh orders from overseas ✅ Stock rally – up 22% in 2 days, 12% today, hitting 7-month high ✅ Brokerage bullish – Prabhudas Lilladher sets ₹261 target --- 📲 Follow Aditya Financial Insights Across Platforms 💼 LinkedIn Group: Aditya's Financial Insights Group 📱 WhatsApp Channel: Join Market Updates 📲 Telegram: @TelegramAdityaFinancialInsights 🤖 AI Analysis: aratt.ai/profile/aditya 📊 Trading Platform: Lemann Platform 🐦 Twitter/X: @adityafinsights 📸 Instagram: @adityafinancialinsights 🌐 Website: https://lnkd.in/gbHXFfW6 --- 🤝 Crypto Partners 🌐 Ubex: Sign Up (umxahxky) 💰 Bybit: Join with Benefits --- #EngineersIndia #EIL #Q3Results #PSU #Dangote #OrderBook #EngineeringStock #StockMarket #AdityaInsights

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