Anand Gopal

Eggoz Raises $20 Mn Series C Led By Gaja Capital; Claims to hit its EBITDA Breakeven In Q4 FY25, Revenue Up 76% YoY To INR 130 Cr FY25 https://lnkd.in/gd9j68rp Abhishek Negi Aditya Singh #Eggoz #funding #D2Cbrands #Agritech #EBITDAbreakeven #Revenuegrowth #Indianstartups #consumerbrands #GajaCapital

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Natasha Malpani Unveils Rs 200 Cr Boundless Ventures to Support AI Startups Former Kae Capital partner Natasha Malpani founded Boundless Ventures, an early-stage fund worth Rs 200 crore that supports Indian AI-native startups. Finance Outlook India READ MORE: https://lnkd.in/gTK4FxKD

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With the advent of AI tech challenged PMs will soon become history and esp. important is to understand constructs like **Tokenization** which are important for all language models be it the classical NLP models or the LLM models. It also helps understand why **byte pair encoding** works for tokenization for pre-training of LLMs. The idea behind this is simple, for any algorithms to process natural language which is continuous and unstructured, you need to divvy up this text into more atomic units. If you don't split this into more atomic units, how will you convert it into structured inputs to run any statistical analysis/more advanced ML/AI. Eg. take the simple case of sentiment analysis through bag of words. Take reviews of a movie "I love this movie" → Positive (1) "I hate this movie" → Negative (0) "This movie is great" → Positive (1) "This movie is bad" → Negative (0) Unless you split this corpus of text into say individual words and attach some numerical representation to the individual words how will you represent the sentiment analysis problem as a mathematical problem which then machines can solve. This entire process is called tokenization. So in this case you can build the master vocabulary as no of unique words in the text corpus. So you divvy up the words and find unique words. Vocabulary = ["I", "love", "hate", "this", "movie", "is", "great", "bad"] Then you represent each sentence as a simple vector in which the words are features. (1s and 0s features represent whether word is present or not) Vocab I love hate this movie is great bad Review I love this movie(1) 1 1 0 1 1 0 0 0 I hate this movie(0) 1 0 1 1 1 0 0 0 Now it becomes a classical regression (logistic regression) problem where sentiment is 'output variable' and the unique words represented as 0/1 (feature vector) become input variables and you can fit a curve. Thus it becomes a mathematical problem similar to classic regression where let's say you are trying to predict house prices basis pin code, house size, type of house, amenities etc. Now the fun bit is the same tokenization construct is used when doing the pre-training for LLMs. ***The only difference is the tokenization happens at a subword level and uses an algorithm called byte pair encoding.***** Why subword because to be able to understand and practically speak like humans, LLMs need to understand that "boy" and "boys" are not necessarily different words or "tokenization" and "memorization" uses the same root suffix "ization" Idea remains the same you split the words at subword level and attach mathematical representations and then convert these tokens to embeddings for LLM pre-training. Follow me https://lnkd.in/gHweK_q7 AND https://lnkd.in/ggUKftFq For tech mastery - https://lnkd.in/gR2fnkAa

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We've spent 18 Months building what every travel agency can't afford. Travel companies have teams of engineers dedicated to one thing: knowing which airline, hotel, and route is best for each customer. Meanwhile, 3,00,000 independent travel agencies across India are making the same decisions blind. 🎯 That changes now. What's the ONE thing you wish you knew before booking a flight for a customer?

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It’s becoming harder to justify a product manager who can’t vibe code. I regularly prototype full functionality when communicating requirements to my engineering stakeholders. Tools like Cursor, Bolt, and Claude allow me to go beyond words and show what’s possible. I now even push code to GitHub, giving my engineering partners visibility into what’s under the hood. These prototypes aren’t final designs, they’re agile artifacts that provide higher context and clearer solution intent. Vibe coding empowers product managers to move faster, give engineers clarity, and ultimately spend more time with customers. And that’s always a win for products, users, and builders alike. #ProductManagement #VibeCoding #AI #Engineering #Prototyping #Agile #Leadership #ProductDesign #Innovation

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The Partnership Paradox: Why Your 7 A.M. Uber Still Cancels It’s 7:15 AM. Laptop bag ready, coffee untouched, three apps open — Uber, Ola, Rapido. Not to see if a ride will come, but to see how long the circus will last today. The first request goes out. No one accepts. The app suggests a ₹50 “tip” to “increase my chances.” Ah yes — a modern-day bribe wrapped in UX design. Finally, a driver accepts. Then cancels. Then another accepts. Cancels again. By the third attempt, fares have surged, patience has vanished, and coffee’s gone cold. If you commute daily in India, you’ve lived this loop — frustration in 10-minute installments. And yet, this isn’t chaos. This is design. Or at least, a design that outgrew its discipline. The “Partner” Problem No One Talks About Uber doesn’t hire drivers — it creates partners. Sounds empowering, right? Except this freedom cuts both ways. When you make someone a “partner,” you share the profits — but not always the purpose. The driver becomes a free agent in a system built on shared responsibility. No surge? Cancel. Long pickup? Cancel. Low fare? Cancel. And just like that, a “partner model” becomes a permission model for unpredictability. The 6 A.M. Logic of Cancellation … What feels like unreliability to the customer is actually rational economics to the driver: ·        Low demand = lower fare per km ·        Empty runs = wasted fuel ·        No guaranteed minimum = every minute counts The system doesn’t reward showing up. It rewards waiting for better math. Freedom Without Feedback Is Friction … Rohan Ghorpade recently wrote about Uber’s “Driver Wanted” sign — how it was never about jobs but ecosystem design. He’s right. It was genius marketing — until governance stopped keeping up. Because here’s the thing: Freedom without feedback doesn’t create ownership — it creates optionality. And when everyone in a system optimizes for self, the system itself starts to fail. The Real Issue Isn’t Cancellation — It’s Calibration … Drivers aren’t the problem. Design is. Partnerships are supposed to mean shared upside and shared accountability. But when the structure is tilted toward autonomy, reliability becomes collateral damage. Uber built freedom as structure. Quick commerce built structure as control. And somewhere between the two, trust got lost in translation. The CalmOps Takeaway … Platforms love the language of “partners,” but partnership without rhythm isn’t freedom — it’s fatigue dressed as flexibility. Because in the end: You can scale people. You can scale profit. But if you don’t scale accountability, you’ll keep scaling chaos. #Leadership #SystemThinking #Operations #AutomationStrategy #CalmOps #Uber #Ola #Rapido #Governance #GigEconomy #StructureIsStrategy

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Why Flight Prices Change Every Time You Refresh the Page ✈️💸 | The Hidden World of Dynamic Pricing. By Nisha Jain, Founder | PNM TRAVELLING. Ever felt like flight prices are playing a game of hide-and-seek? You know, the moment you hit refresh and suddenly the price goes up or down without any explanation? 🤯 You're not imagining it – it’s all part of a complex system airlines use to optimize their revenue. Here's a peek behind the curtain at why this happens! 👀 🌍 1. Dynamic Pricing Magic Picture this: you’re browsing for flights, and suddenly, the price changes after a few seconds. This isn't a glitch – it’s dynamic pricing in action! Airlines use algorithms that adjust ticket prices in real-time based on factors like: Supply & Demand: Higher demand (more searches) = higher prices. It’s basic economics! Booking Trends: Prices tend to go up as the flight date nears, encouraging early bookings. Competitor Monitoring: Airlines are always watching each other, and if one adjusts its fares, others quickly follow suit. 🍪 2. Cookies Are Watching You Yes, cookies – the small pieces of data websites store about your activity. Airlines and travel sites may track your behavior and raise prices if they think you’re “on the hook” for buying. It's like a little nudge to get you to book before the price rises further! 📈 🌐 3. Real-Time Price Adjustments Airlines are constantly adjusting their prices based on real-time data. As more people book a specific flight, prices might rise due to limited seats. It’s a game of "Who can book it first?" with the prices climbing as you delay! ⏳ 🌎 4. Geography Matters Fun fact: your location can impact the price you see! Flight prices might vary depending on where you're browsing from. If you're in a high-income city, your ticket might cost more. It’s all about targeted pricing and market research. 🌍💡 🧑‍💼 5. Airline Revenue Management Ever wondered how airlines fill flights? It’s not random! Airlines use revenue management to determine the best price for each seat. The earlier you book, the cheaper the price might be (generally), but once you get closer to the departure date, prices are optimized to match demand. 🎯 🕑 6. When You Book Matters It’s not just about where you’re flying, it’s about when. Ticket prices often drop mid-week (Tuesday or Wednesday) and rise during weekends when people are more likely to plan their trips. Want to book at the lowest price? Avoid peak times and plan ahead! 📅✈️ To beat the system, track fares, use fare alerts, and don’t be afraid to refresh your browser (but maybe not too many times). 📉🎟️ Let’s face it – flying can be complicated, but with a little insight into the algorithms at play, you can be smarter about your booking strategy. And who knows? You might even catch the perfect price at the right time! 😉 #FlightPrices #DynamicPricing #TravelTips #AirlineAlgorithms #BookSmart #TravelHacks #FlightBooking #TechInTravel #PriceFluctuations #PNMTRAVELLING

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🚀 Collaborative Intelligence: Analyzing IndiGo's Crisis Impact on Interglobe Aviation IndiGo Airlines has faced a critical operational meltdown with over 1,000 flight cancellations due to pilot rostering failures. While headlines focus on disrupted schedules, a more critical question emerges: How does this translate into equity value impact for Interglobe Aviation? Why This Matters This crisis affects multiple dimensions: ✈️ Operational: Load factors, capacity utilization, recovery timelines 📋 Regulatory: DGCA probe outcomes and compliance implications 💰 Financial: Revenue erosion and quarterly earnings revisions 🎯 Market Sentiment: Investor confidence and analyst recommendations 🏆 Competition: Market share shifts to Air India, SpiceJet, Vistara The Solution: Collaborative Intelligence Individual analysis has limitations. What's needed is collective intelligence—combining crowd insights, real-time data, and structured frameworks to build comprehensive investment theses. Introducing: IndiGo Adverse Events - Interglobe Aviation Impact Space A Perplexity Collaborative Space enabling investors, analysts, and traders to: ✅ Track operational recovery metrics across 7 authoritative sources ✅ Analyze financial impact scenarios and earnings projections ✅ Monitor regulatory developments in real-time ✅ Compare competitive positioning ✅ Access structured research frameworks ✅ Collaborate with peers Key Features 🔗 7 Curated Sources: Times of India, NDTV, Economic Times, Reuters, Screener.in, Business Standard, PIB 🎯 Custom AI Instructions: Focused analysis on operations, regulation, financials, sentiment, and competition 📊 Structured Tasks: Three pre-defined research tracks 🤝 Open Contribution: Anyone can participate and contribute insights Who Should Join? Equity analysts, active traders, consultants, investment professionals, aviation experts, and researchers seeking real-time, multi-source market intelligence. Access the Space Ready to participate in collective intelligence analysis? 🔗 Join Here:  https://lnkd.in/g_KbNa7U No registration needed—open to anyone. Crisis creates clarity. This space represents a new approach to market analysis: transparent, collaborative, data-driven, and accessible. #Aviation #Investing #MarketAnalysis #IndiGo #EquityResearch #AIAnalysis #Perplexity #CollaborativeIntelligence #IndianMarkets

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At what point did customer service become so broken that even medical emergencies don’t earn basic empathy? Over the past week, we’ve shared our ongoing struggle with MakeMyTrip, and now also Etihad Airways and IndiGo (InterGlobe Aviation Ltd). Despite submitting MRI scans, two doctor’s prescriptions, and a medical unfit-to-travel certificate, the response has been silence, or worse, pointing to each other. It's dehumanizing. It's exhausting. And it raises a painful question for these brands: When did your KPIs change- 1. from customer satisfaction to customer helplessness? 2. and how fast can your executives buy a Lamborghini while customers are left without support during critical times? We’ve been loyal Platinum Black members at MakeMyTrip for years, but this has ended any trust we had. In fact, about 60 Black level members within our network have decided to start using alternate options over MMT as this has brought up their old struggles as well with these brands. If you too have experienced frustration with MMT, feel free to add your name in the comments. Maybe then, the volume of unheard voices will finally be impossible to ignore. MMT is celebrating 25 years—but based on our experience, it feels more like 25 years of hollow promises and hopeless service. To the brands involved: enjoy the profits, if they’re truly worth the cost of customer dignity. MakeMyTrip Care Parikshit Choudhury Saujanya Shrivastava - Your team has only 1 answer- we are trying. They are not willing to share in writing what they speak on phone. In-spite of multiple follow ups. ----------------------- Piyush Goyal these brands have gone unchecked for years. Before we speak of disruption or global leadership, we must first fix the basics—like accountability, consumer rights, and empathy. This is exactly why India often falls short of what we aspire to become. #CustomerExperience #MMT #Etihad #Indigo #TravelNightmare #BrokenSupport #MMTBlack #CustomerServiceFail #Escalation

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When AI Agents Learn to Pay — The Power of AP2 in Travel (Part 1) Imagine this: a major international airline announces a global fare sale from December 1. You tell your travel assistant: “Buy me a Business Class ticket from Dubai to Mumbai on December 21 if the price drops below AED 4,200.” Then you get back to work. When sales open, your AI agent checks fares through MCP server offered by the airline, finds a match, and pays—automatically—under your exact conditions. No frantic refreshing, no missed deals, no surprises. That’s the promise of AP2 (Agent Payments Protocol) — an open standard from Google and its partners that lets AI agents handle delegated, real-money tasks securely and verifiably. It’s anchored by cryptographic mandates that define who can act, when, and within what limits. How it works — five simple steps 1.Intent Mandate (you → your agent) You sign a digital mandate with your rules: route, date, cabin, max price (AED 4,800), sale window, and payment token. This is pre-authorized consent your agent can act on. 2.Search & Hold (agent → airline MCP) At sale start, the agent calls the airline’s MCP server, which exposes tools like search_fares and make_booking. The MCP connects to the airline’s retailing APIs, returning an offer that meets your conditions. 3.Cart Mandate (agent → airline MCP) The agent creates a Cart Mandate, referencing the Intent Mandate ID and the exact fare hash. The airline’s MCP validates and co-signs it—becoming the merchant of record for the transaction. 4. Settlement (airline MCP → PGSP) The MCP sends the signed Cart Mandate to the payment provider. The PGSP authorizes payment and returns a signed receipt for audit, refunds, and compliance. 5. Confirmation (airline MCP → agent → traveler) The MCP finalizes the booking, issues the PNR and e-ticket, and your agent delivers proof and receipt. This is agentic commerce in action—AI that doesn’t just answer but acts safely on your behalf. For airlines, travel agents, and partners, AP2 can streamline re-accommodation, upgrades, vouchers, and post-sales automation—all with transparency, traceability, and trust. The next evolution of digital commerce won’t be a new app—it’ll be autonomous agents transacting over trusted protocols like AP2 and MCP.

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How ixigo Quietly Became India's Travel Underdog Turned Titan In 2007, they were just a train schedule app. Fast forward to 2025 — they're India's 2nd largest OTA by GTV, and the fastest-growing among peers. What changed? They stopped being an app… and became an ecosystem. 🚆 Trains, ✈️ flights, 🚌 buses, 🏨 hotels — all stitched into one seamless platform. But that’s not even the real genius. ixigo’s magic lies in this: They turned utility into habit. → Missed train? They suggest alternate combos. → Waitlist? They’ll give 3x refund if it doesn’t clear. → Want food, cabs, travel insurance, visa help? All built-in. 📈 Their Q3 FY25 was their best ever: >GTV: ₹40,363 Cr >Revenue: ₹2,418 Cr >EBITDA up 2x YoY >480M+ Annual Active Users >92% queries handled by AI And here's the kicker: 👉 Their bus segment alone has a 66% contribution margin. That's insane operating leverage for an OTA. The insight💡 Don’t chase bookings. Solve problems at every step of the journey — before, during, and after. That’s how you build loyalty in a low-trust, high-churn market. ixigo didn’t just book tickets. It booked habits. #TheStartupTales #Briefcase09 #ixigo #StartupStrategy #AI #CustomerExperience #ProductThinking #LinkedInCreators #TravelTech

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GCCs in 2025: Scaling Headcount, But Struggling with Headway India’s 1,600+ GCCs are no longer just delivery engines, they’re expected to drive innovation, transformation, and AI-enabled growth. But here's the uncomfortable truth: 70% of GCC leaders say they’re under pressure to “do more” with fewer resources, rising expectations, and complex global alignment. (Source: Everest Group 2025 GCC Pulse) Having worked across the US, APAC, and now India, here’s what I’m observing on the ground: The 3 Pain Points Nobody Likes to Talk About: 1. Capability ≠ Readiness Teams are technically skilled, but not always business-ready. Many lack real exposure to the client context or the "why" behind what they build. 2. Leadership Layers Are Thin There’s a shortage of enterprise-aligned, globally seasoned leaders who can influence beyond delivery and drive strategic conversations with HQ. 3. Infra and Ops Still Playing Catch-Up AI, automation, cloud-first strategies, all promised, but legacy systems and siloed ops are slowing value realization. What Needs to Change: My View/my observation/my experience 1. Embed “Outside-In” Thinking GCCs need leaders who’ve seen scale and strategy, who’ve sat at global tables and understand the market, not just the metrics. 2. Shift from Role Filling to Role Reimagining Instead of hiring more program managers, let’s build capability pods with design, infra, data, and domain in one integrated view. 3. Move from Efficiency to Experience Infra and digital ops need to become proactive enablers, not reactive backbones. That means smarter automation, AI-led workflows, and self-healing systems. The potential is enormous but we can’t keep solving tomorrow’s challenges with yesterday’s models. India’s GCCs can absolutely lead from the front but it starts with rebuilding trust, rethinking org design, and empowering a new layer of leadership. And that’s where I believe leaders like us, who’ve navigated complexity globally, can truly add value. Would love to hear from others building or leading the next generation of GCCs what's your biggest challenge right now? #GCC #DigitalTransformation #Leadership #ReturnToIndia #InfraLeadership #GenAI #FutureOfWork #IndiaGCC #GCCReset #GCCs2030 #Hyderabad GCC Pulse™ nasscom ANSR Lalit Ahuja Achyut Menon "AK" Arindam Sen EY EY Technology Solutions Talentiser KPMG India Korn Ferry Vishnu BG Viswanathan K S Gopal A Iyer SHRM India Indian School of Business Vishwanadh Raju Zinnov Pari Natarajan Nilesh Thakker Pavan Kumar Bandaru Supriya Accenture in India

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[agentic browsers] ChatGPT's Atlas browser is out. I asked it for a simple task - go to MMT and find me a flight. it took 8 mins to come to the listing page and was still trying to close out their AI pop up myra.ai cos it was interfering in its flow (how ironic). Reminds me of this tweet from Andrej Karpathy (copy pasted below) - in the short term the effect of AI is hugely over-rated and agentic i think is one of them. while everyone speaks about AI writing 80-90% of the code, nobody is telling you the amount of guardrails and checks and balances in place to make that happen. I think we will all be short changed if we believe AI is going to change our life dramatically in the next few years. Yes a lot of repetitive, low value add tasks will be hugely at risk (like booking a flight ticket, search may move to AI browsers) - but more complex stuff , i think the jury is still out there. Thats why I think its a great time to be building as an incumbent cos our connectors (APIs, workflows etc) are in place and as long as we continue to be nimble and shit scared of being disrupted, we have a shot! how are you prepping your biz for AI ? ----- "On LLM agents. My critique of the industry is more in overshooting the tooling w.r.t. present capability. I live in what I view as an intermediate world where I want to collaborate with LLMs and where our pros/cons are matched up. The industry lives in a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. For example, I don't want an Agent that goes off for 20 minutes and comes back with 1,000 lines of code. I certainly don't feel ready to supervise a team of 10 of them. I'd like to go in chunks that I can keep in my head, where an LLM explains the code that it is writing. I'd like it to prove to me that what it did is correct, I want it to pull the API docs and show me that it used things correctly. I want it to make fewer assumptions and ask/collaborate with me when not sure about something. I want to learn along the way and become better as a programmer, not just get served mountains of code that I'm told works. I just think the tools should be more realistic w.r.t. their capability and how they fit into the industry today, and I fear that if this isn't done well we might end up with mountains of slop accumulating across software, and an increase in vulnerabilities, security breaches and etc" Sunel Tr Shashwat Ramesh Natarajan Ramachandran Santosh K Bharath Raj Utsav Rathi Surotham Suresh

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Most people exploring AI aren’t struggling with prompts anymore. The real challenge is knowing when to build agents. Here’s a simple mental model using a travel agent example 👇 Environment = booking system Sensors = flight prices, hotel availability Actuators = booking confirmations Different types of agents unlock different capabilities: Simple reflex → forward complaints Goal-based → plan a full journey (car → flight → hotel) Multi-agent systems → coordinate flights, hotels, and cars together 🎯 Agents make sense when: problems are open-ended (can’t pre-program every path) workflows are multi-step (require chaining tools/services) systems improve with feedback (learning loop) I broke this down with examples in a Twitter thread 👇 👉 https://lnkd.in/gnUS-VmP If you want to learn more about AI agents, follow me on Twitter where I share deep dives & examples regularly. 💡 Your turn: what use case are you considering where an agent could help? Drop it in the comments — I’d love to see what you’re exploring.

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90% of OTA founders are not scaling. They are bleeding ₹3–10 lakh/month… and most don’t even realize it. They blame: Low traffic Bad marketing Conversion issues But the real problem? Their Travel API system is breaking at the exact point where money is involved. Here’s where your revenue is silently leaking: 1. Booking confirmed, ticket not issued Customer reaches airport → no ticket One incident = permanent trust loss 2. Payment captured, booking failed Now you’re not running an OTA You’re running a refund operation 3. Fare mismatch at checkout User feels cheated → never returns 4. Duplicate bookings due to retry logic Same user → double charged → escalation 5. Selling inventory that doesn’t exist Supplier mismatch → your credibility gone 6. No fallback API system One airline fails → your entire platform stops 7. No real-time booking visibility Customer confused Support clueless System silent 8. Manual refund handling Cash stuck → frustration high → chargebacks begin 9. Support team breakdown under pressure Agents managing anger, not solving problems 10. Public complaints + payment gateway risk One issue → multiple posts → long-term damage Last month, we audited one OTA doing decent volume. They were losing ~₹4.2 lakh/month just from: Duplicate bookings Failed ticketing Refund delays They didn’t have a traffic problem. They had a system leakage problem. You don’t have a growth problem. You have a Travel API architecture problem. And this is where most founders fail: More bookings on a weak system = faster financial damage. Who we work with: OTA founders who are already getting bookings… but losing control at: API layer Payment layer Post-booking operations What we actually fix: ✔ Revenue leakage at API level ✔ Multi-supplier fallback routing ✔ Fare mismatch & booking failure loops ✔ Automated refunds & faster settlements ✔ Reduced dependency on support teams Because real scaling is simple: Even when the system fails… you don’t lose money. Reality check: If you’re facing even 2–3 of these issues, you are already losing money every single day. You just haven’t measured it yet. Let’s make this practical: Comment “API” or DM “LEAK” I’ll break down exactly where your system is losing revenue (based on your current setup, not theory) No pitch. No fluff. Only clarity. If you're building an OTA platform or travel marketplace and want to discuss API architecture or scaling challenges, you can book a short discussion here: https://lnkd.in/ggXmJEV3 #TravelAPIs #FlightBookingAPI #OTAInfrastructure #TravelPlatformScaling #TravelDistributionSystems

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