सSaral Shiksha Yojna
Courses/Technology Product Entrepreneurship

Technology Product Entrepreneurship

CS9.424
Ramesh Loganathan + Prakash Yalla•Monsoon 2025-26•4 credits
Sample Papers/TPE end-sem mock paper (200 marks) · Paper 6

TPE end-sem mock paper (200 marks) · Paper 6

Duration: 180 min • Max marks: 200

Part 1 — Higher-Order Concepts (40 Marks Total, @5 each)

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  1. 1.Customer Segments for an SMB Cybersecurity Tool A startup is building a "security-in-a-box" tool for small businesses (10–50 employees) that monitors endpoints, blocks phishing, and runs auto-remediation without a dedicated IT team. Identify: a) One specific target customer; b) One key problem they face; c) Why they may pay for this solution; d) One way to test interest quickly.5 m
  2. 2.Problem–Solution Fit for an AI Audio-Dubbing Tool A startup wants to help YouTube creators and OTT content producers automatically dub their videos into 10+ Indian and global languages using AI voice cloning. a) Who is the customer; b) What is the main problem; c) What is one simple MVP they can test first; d) What concern might *creators* have that the founders may underestimate?5 m
  3. 3.Idea Hexagon — Robotics Using the Idea Hexagon, generate six startup ideas in the robotics space (e.g., industrial automation, service robots, autonomous vehicles, surgical robots, agricultural robots). Each idea must clearly specify the target user and the core problem being solved.5 m
  4. 4.Business Model Basics — Maritime Shipping Tech A startup is building a predictive-analytics platform for shipping companies that uses real-time weather, port congestion, and AIS data to recommend optimal routes, saving fuel and avoiding delays. Identify: a) One target customer segment; b) The core value proposition for this segment; c) One possible revenue model; d) One channel to reach customers.5 m
  5. 5.Customer Segments for a Lab-Automation Robot A startup is building a tabletop robotic system that automates routine pipetting and sample handling tasks in diagnostic and research labs, replacing 3–5 hours of manual work per day. Identify: a) One specific target customer; b) One key problem they face; c) Why they may pay for this solution; d) One way to test interest quickly.5 m
  6. 6.Problem–Solution Fit for a Teen Mental-Wellness App A startup wants to help teenagers (13–18) manage daily stress, social anxiety, and exam pressure through a peer-supported, anonymous community app moderated by counsellors. a) Who is the customer; b) What is the main problem; c) What is one simple MVP they can test first; d) What concern might *parents* have that the founders may underestimate?5 m
  7. 7.Idea Hexagon — Space Tech Using the Idea Hexagon, generate six startup ideas in the space-tech space (e.g., small satellites, launch services, in-space manufacturing, satellite data, space-based services). Each idea must clearly specify the target user and the core problem being solved.5 m
  8. 8.Business Model Basics — Vocational Edutech A startup is building a vocational-skilling platform that trains rural youth in skills like data labelling, digital marketing, and customer support via a mobile app, then places them into remote-work jobs. Identify: a) One target customer segment; b) The core value proposition for this segment; c) One possible revenue model; d) One channel to reach customers.5 m
  9. 9.Your TPE Startup Idea (Bonus 5 Marks) Write down a one-line ad-lib for your idea as per the framework shared in class. State: a) Target user; b) Main customer pain; c) Product benefit; d) One gain for the customer. ---

Part 2 — Advanced Framework Application (80 Marks total, @20 each)

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  1. 1.Case: GuardScan Cybersecurity SOC GuardScan is building a managed SOC (Security Operations Centre)-as-a-service for mid-market Indian enterprises (₹100–500 Cr revenue). The product combines AI-driven threat detection with a small offshore SOC team. CIOs are interested but want price points lower than ₹50 lakh/year. Large incumbents (Wipro, TCS) sell similar services at ₹2–5 Cr/year, primarily to Fortune 1000. Questions: a) Identify the beachhead customer segment (5) b) List two customer pains and two gains the product addresses (5) c) Explain how GuardScan can differentiate from the large incumbents (5) d) Suggest one go-to-market strategy for early adoption (5)20 m
  2. 2.Case: VoiceForge AI Dubbing for OTT VoiceForge is an AI tool that dubs OTT content into 12 Indian languages with cloned voices and lip-sync. OTT players (regional and national) are interested but worried about voice-actor union backlash and accuracy in emotional scenes. Two global incumbents offer similar tech but only for English-major dubbing. Questions: a) Identify two hypotheses the startup must test (5) b) Classify each as a problem risk or a solution risk (5) c) Suggest one MVP experiment to address the emotional-scene accuracy concern (5) d) Propose one revenue model that aligns content production economics with the AI tool (5)20 m
  3. 3.Case: RoboPalette Lab Automation RoboPalette builds tabletop robots that automate routine lab workflows (pipetting, sample prep, plate-handling) for clinical diagnostic labs. Big labs love the precision but resist deploying because the price tag (₹40 lakh) requires too long a payback. Small labs need it more but can't afford the capex. Two global incumbents (Hamilton, Tecan) dominate the high-end segment. Questions: a) Identify the beachhead customer segment (5) b) List two customer pains and two gains the product addresses (5) c) Explain how RoboPalette can differentiate from Hamilton/Tecan (5) d) Suggest one go-to-market strategy that handles the capex barrier (5)20 m
  4. 4.Case: SonarShip Predictive Maritime Analytics SonarShip provides predictive analytics for global shipping companies — weather, congestion, fuel optimisation. Mid-size shipping companies have signed pilots and reported 6–9% fuel savings. Big shipping liners (Maersk, MSC) have in-house teams doing similar things and won't engage. Port authorities have shown interest in the congestion data but as a one-time payment, not subscription. Questions: a) Identify two hypotheses the startup must test (5) b) Classify each as a problem risk or a solution risk (5) c) Suggest one MVP experiment to monetise port-authority interest (5) d) Propose one revenue model that can serve mid-size shippers and port authorities differently (5) ---20 m

Part 3 — Deep-Dive Case Studies (80 Marks total, @40 each)

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  1. 1.Case: NanoMint Biopharma Bioreactor NanoMint is developing a continuous-flow bioreactor that produces mRNA vaccines and therapeutics 3–5× faster and 40% cheaper than batch-based incumbent processes. The technology is patent-pending and has shown strong results in lab-scale prototypes. Initial pilots showed promising results. Two mid-size Indian pharma companies validated the technology but require 2–3 years of regulatory approval before commercial use. Contract Manufacturing Organisations (CDMOs) have asked to license the bioreactor technology to operate it on behalf of their pharma clients. Meanwhile, one giant global pharma has offered an exclusive licensing deal worth $25 million upfront + royalty in exchange for exclusivity in mRNA vaccine production. NanoMint is now considering three possible growth paths: - Sell **bioreactor equipment directly to pharma companies** as capital equipment, with ongoing service contracts - License the technology to **CDMOs** non-exclusively as a royalty-bearing technology platform - Sign an **exclusive licensing deal with the giant pharma** for mRNA, freeing NanoMint to develop other applications (gene therapies, biologics) with the upfront capital The startup has limited funding and only 15 months of runway. The founders must choose a path that balances impact, revenue, and scalability. a) **Strategic Evaluation (15 marks)** — Evaluate all three growth options using: a) Product–Market Fit; b) Revenue potential; c) Sales complexity; d) Scalability. b) **Recommendation (10 marks)** — Recommend the best path for NanoMint and justify your choice. c) **Business Model Design (10 marks)** — For your recommended option, define: a) Revenue model; b) Key partners; c) Distribution channels; d) Cost structure. d) **Risk Analysis (5 marks)** — Identify: a) One market risk; b) One technology risk; c) One adoption risk — and propose mitigation for each.40 m
  2. 2.Case: HeritageBuy Travel Platform HeritageBuy is an AI-powered platform that curates and books offbeat heritage-tourism experiences across India — restored havelis, monastery stays, village artisan workshops, hidden archaeological sites — places normally invisible to mainstream travel platforms. Initial pilots showed promising results. Tier-1 urban consumers (high-income, well-travelled) loved the curation, with average booking value of ₹35K per trip. However, consumer bookings are infrequent — once or twice a year — making consumer CAC hard to recover. Boutique hotels and curated tour operators have asked to use HeritageBuy as a distribution channel, since their inventory normally isn't visible on MakeMyTrip or Booking.com. Separately, two large travel aggregators (Cleartrip and a global OTA) have approached HeritageBuy to license the curation engine and embed "discover heritage" sections inside their apps. HeritageBuy is now considering three possible growth paths: - Run a **direct consumer travel platform**, scale via brand and content marketing - Pivot to **B2B for boutique hotels and tour operators** — a distribution channel for their otherwise-invisible inventory - License the **curation engine to large travel aggregators** as a SaaS / API The startup has limited funding and only 15 months of runway. The founders must choose a path that balances impact, revenue, and scalability. a) **Strategic Evaluation (15 marks)** — Evaluate all three growth options using: a) Product–Market Fit; b) Revenue potential; c) Sales complexity; d) Scalability. b) **Recommendation (10 marks)** — Recommend the best path for HeritageBuy and justify your choice. c) **Business Model Design (10 marks)** — For your recommended option, define: a) Revenue model; b) Key partners; c) Distribution channels; d) Cost structure. d) **Risk Analysis (5 marks)** — Identify: a) One market risk; b) One technology risk; c) One adoption risk — and propose mitigation for each. ---40 m
  3. 3.SMB Cybersecurity "Security-in-a-Box" **a) Target customer:** Founders or operations heads at 10–50 employee SMBs in regulated sectors (fintech, healthtech, edutech) where data-breach risk is high and there is no dedicated IT/security team. **b) Key problem:** They face the same cyber risks as enterprises (phishing, ransomware, endpoint malware) but cannot afford a SOC team or enterprise-grade security tools. **c) Why pay:** A single breach costs ₹20–₹50 lakh in remediation + reputational damage; ₹50K/year for managed security is a no-brainer ROI. **d) Quick test:** Free 30-day trial offering one specific high-pain feature (phishing-link blocking on company emails); measure click-through avoidance rate and willingness to renew at ₹4K/month.
  4. 4.AI Audio Dubbing for Creators **a) Customer:** Mid-tier YouTube creators (50K–1M subscribers) producing educational, comedy, or vlog content in one language who want to expand reach across India and global Indian diaspora markets. **b) Main problem:** Manually dubbing or subtitling each video costs ₹15K–₹50K per video and takes 1–2 weeks; without it, creators leave 70%+ of potential audience on the table. **c) Simple MVP:** Free one-video trial — creator uploads one 10-minute video; system dubs into 3 languages within 24 hours; measure willingness to pay for ongoing service. **d) Underestimated concern by creators:** Loss of authentic voice / personality. Creators have built their brand on their *voice*; an AI-cloned version that sounds 95% accurate but emotionally flat in the new language can dilute the very thing that built their following. Founders need to preserve vocal personality, accents, and timing — not just translate text.
  5. 5.Idea Hexagon — Robotics 1. **Generalize:** Warehouse-picking robot → extend to retail shelf-restocking, library book-sorting, hospital pharmacy automation. Target: facility managers; problem: repetitive labour cost. 2. **Fusion:** Robotics + insurance — a robot-as-a-service contract that includes guaranteed performance (penalties for downtime) and an insurance wrapper. Target: SMB manufacturers; problem: trust gap in adopting robots. 3. **Find the Nails:** A high-precision robotic arm with sub-mm accuracy → applicable to surgical assistance, electronics assembly, art restoration, semiconductor wafer handling, jewellery setting. 4. **Find the Hammers:** Eldercare loneliness problem → solutions include companion robots, smart-home presence detection, voice-call AI agents, peer-matching apps, virtual family hubs. 5. **Add an Adjective:** *Soft-grip* robots that handle fragile produce — agriculture-grade harvesting (mangoes, grapes). Target: large agri-exporters; problem: damage during manual harvesting. 6. **Do the Opposite:** Instead of bringing robots into factories, bring factories to robots — modular, mobile pop-up manufacturing units that can be deployed at point-of-demand (disaster zones, remote construction).
  6. 6.Maritime Shipping Analytics **a) Target segment:** Mid-size container/bulk shipping companies operating 10–100 vessels (regional players, second-tier global liners), who lack the in-house data science teams of Maersk-scale incumbents. **b) Value proposition:** Reduces fuel cost by 6–9% via optimal routing + cuts port-delay penalties by 30%, paying back in 4–6 months. **c) Revenue model:** Per-vessel subscription (₹3L–₹6L/vessel/year) with annual contracts + a premium tier for "auto-routing" integration with the ship's bridge system. **d) Channel:** Shipping industry associations (FOSMA, INSA, BIMCO) for credibility + partnerships with ship-management companies who can resell to their fleet-owner clients.
  7. 7.Lab-Automation Robot **a) Target customer:** Operations head at a mid-size clinical diagnostic chain (5–30 labs, 500–5,000 tests/day) running molecular testing or biochemistry panels. **b) Key problem:** Manual pipetting consumes 3–5 hours of skilled tech time per lab per day; tech salaries in metros are climbing 12–15% YoY and skilled techs are increasingly hard to retain. **c) Why pay:** ROI in 12–18 months from labour cost reduction + improved test consistency (lower repeat-test rate); easier compliance with NABL audits. **d) Quick test:** 30-day on-site demo at one lab — robot handles 100% of pipetting for one assay; measure tech hours saved, test consistency, and willingness to convert to a 36-month rental contract.
  8. 8.Teen Mental-Wellness App **a) Customer:** Teens aged 13–18 in tier-1 and tier-2 cities are the *users*; parents (with monthly household income ₹50K+) are typically the *buyers* once a subscription model kicks in. School counsellors are a strong channel. **b) Main problem:** Teens face increasing exam stress and social-media-driven anxiety but feel embarrassed talking to parents, teachers, or therapists; existing apps feel "clinical" and unrelatable. **c) Simple MVP:** Free anonymous WhatsApp/Telegram peer-support channel moderated by counsellors; one-month pilot with 200 teens in one school; track engagement, self-reported mood change, and parent retention if a paid tier is offered later. **d) Underestimated concern by parents:** Loss of visibility / control. Many parents *want* to know what their teen is anxious about; an anonymous app can feel like the founders are helping their child hide things from them. Founders need to design *family modes* — letting parents see aggregate trends or set crisis-alerts without exposing individual messages — to bring parents along.
  9. 9.Idea Hexagon — Space Tech 1. **Generalize:** Earth-observation satellite analytics → extend to atmospheric monitoring, space-debris tracking, asteroid surveying, lunar exploration data products. Target: research institutions, insurance, mining. 2. **Fusion:** Satellite imagery + DeFi — geospatial-data-backed crop futures contracts on-chain. Target: agri-commodity traders; problem: untrusted yield data. 3. **Find the Nails:** A compact electric thruster for satellite station-keeping → applicable to small sats, deep-space probes, in-orbit refuelling, defence satellites. 4. **Find the Hammers:** Last-mile rural connectivity problem → solutions span balloons (Google Loon), low-earth-orbit satellite internet (Starlink), drone relays, fibre-optic mesh. 5. **Add an Adjective:** *Reusable* small-satellite launch vehicles — bring down per-kg launch cost to space. Target: small-sat operators; problem: launch costs eat 40–60% of mission budget. 6. **Do the Opposite:** Instead of operators owning satellites, satellites as a service — pay-per-image-or-pass model where customers never own hardware. Target: research institutions and one-off mission customers; problem: capital intensity of space.
  10. 10.Vocational Edutech for Rural Youth **a) Target segment:** Rural youth (18–28) in tier-3 and tier-4 districts of states with high youth unemployment (Bihar, UP, Odisha, Jharkhand) with at least 10th-pass education and access to a smartphone. **b) Value proposition:** ₹15K–₹35K/month remote-work job within 90 days of training, with skill content delivered in vernacular through a mobile-first app and a guaranteed placement model. **c) Revenue model:** **Income-Share Agreement (ISA)** — student pays zero upfront; after placement, pays 12–15% of their salary for 18 months. Aligns incentives with placement quality and removes the upfront capital barrier. **d) Channel:** Tie-ups with district-level NGOs, gram panchayats, and government skill-development schemes (PMKVY) for recruitment + partnerships with remote-work employers (BPOs, data-labelling companies, e-commerce CS centres) for placement.
  11. 11.Bonus — Ad-Lib for Your Own Idea *Sample format:* "For [tier-3 city diagnostic labs] who [lose 3–5 hours of skilled tech time per day to manual pipetting], our [tabletop lab-automation robot] is a [precision automation platform] that [delivers 12-month payback through labour savings and improved test consistency]. Unlike [Hamilton/Tecan's enterprise systems], our product [is priced for mid-market labs and rented on a 36-month OPEX-friendly contract]." **a)** Tier-2/3 city diagnostic-lab operations heads. **b)** Manual pipetting eats skilled tech time. **c)** Affordable automation with 12-month payback. **d)** Higher test throughput per tech, better NABL compliance.
  12. 12.GuardScan Cybersecurity SOC **a) Beachhead segment:** **Mid-market Indian fintechs and healthtechs (₹100–500 Cr revenue)** that have a compliance-driven cyber requirement (RBI guidelines, HIPAA-equivalent) but no in-house SOC. They're too small for Wipro/TCS to bother with, too risky to stay unprotected — perfect underserved middle. **b) Pains:** (i) Compliance audit failures threaten core business licenses (RBI, IRDAI); (ii) Cyber-insurance premiums escalate 30–50% without proven controls. **Gains:** (i) Lower insurance premiums and audit-clean compliance; (ii) Founder/CEO peace of mind without paying ₹2–5 Cr/year for enterprise vendors. **c) Differentiation from Wipro/TCS:** - *Pricing model* — ₹30–₹50 lakh/year vs ₹2–5 Cr; productised and standardised, not custom-built per customer. - *Speed of deployment* — operational in 2 weeks vs 2–4 months for enterprise vendors. - *Vertical depth* — domain-specific playbooks for fintech/healthtech (regulatory mapping, PCI-DSS, etc.) that incumbents would call "custom work." - *Defensibility* — proprietary mid-market threat-intelligence dataset over time (Tier-2 USP) + standardised audit-prep templates. **d) GTM strategy:** "Audit-ready in 30 days" — bundled offering where GuardScan guarantees a clean RBI/IRDAI cyber audit within 30 days of deployment, with a money-back clause if the audit fails. Distribute through compliance consulting firms (RGS, EY mid-market practice) who already advise the target customers and earn a referral fee on each closed deal.
  13. 13.VoiceForge AI Dubbing **a) Two hypotheses:** - H1: OTT players will adopt AI dubbing for *new content* but resist using it on their flagship/marquee shows because of voice-actor brand association and emotional fidelity risks. - H2: Voice-actor union resistance can be defused with a transparent revenue-share model where original voice actors are paid for the AI cloning of their voice. **b) Classification:** - H1 is a **solution risk** — the current solution (full AI dubbing) may be wrong for the highest-revenue use case (flagship content); a hybrid (AI-assisted human dubbing) might fit better. - H2 is a **problem risk** — the founders may have wrongly framed the union as a blocker; they may actually become an enabler if they share economically. **c) MVP experiment for emotional-scene accuracy:** Pick 10 emotionally varied scenes (drama, comedy, action). Dub each into Tamil and Bengali using AI. Run a blind A/B test with 200 native-speaker viewers — half see AI-dubbed, half see human-dubbed. Measure perceived authenticity, willingness to continue watching, and audience confidence. Result tells the team where AI is "good enough" and where human-in-the-loop is still required. **d) Revenue model:** **Tiered pricing aligned with content economics** — base AI-only dubbing for low-cost reality/documentary content (₹50K/episode); hybrid AI + human-actor review for premium content (₹2L/episode); revenue-share with voice actors whose voices are cloned (15–20% of dubbing revenue back to them). The model aligns content tier with economics + brings voice actors on board as economic partners rather than adversaries.
  14. 14.RoboPalette Lab Automation **a) Beachhead segment:** **Mid-size diagnostic chains (5–30 labs)** in tier-1 and large tier-2 cities — they have the volume to justify automation but cannot afford ₹40 lakh capex per lab. The deck's "underserved middle" pattern applies — too small for Hamilton/Tecan, too large to stay manual. **b) Pains:** (i) Skilled lab techs cost ₹40K–₹70K/month and are increasingly hard to retain; (ii) Manual pipetting variability causes 5–8% test-result inconsistency, eroding lab reputation. **Gains:** (i) 60–80% labour cost reduction on automated tasks; (ii) NABL-grade consistency improves customer confidence and unlocks premium B2B contracts (hospitals, insurers). **c) Differentiation from Hamilton/Tecan:** - *Pricing and form factor* — ₹40 lakh capex vs Hamilton's ₹1.5+ Cr; designed for the mid-market lab, not a research institute. - *India-grade engineering* — works with Indian voltage variance, dust, humidity; service network across India (Hamilton flies engineers from Singapore). - *Software experience* — modern web UI vs Hamilton's older PC software; integration with common Indian LIMS systems out of the box. - *Defensibility* — proprietary Indian-assay-specific protocols dataset (Tier-2 USP); the more customers run protocols, the better the library gets. **d) GTM to handle capex barrier:** **Pay-per-test pricing model** instead of upfront capex. Lab pays nothing upfront; RoboPalette charges ₹3–7 per automated test (vs ₹15–30 manual cost). Customer hits ROI in month 3 instead of month 18. Reduces sales-cycle friction by 70%. Once 100 labs are on the pay-per-test model, offer them a buyout at month 18 for a discount.
  15. 15.SonarShip Maritime Analytics **a) Two hypotheses:** - H1: Mid-size shippers will pay subscription pricing because their internal teams cannot replicate analytics in-house. - H2: Port authorities will pay subscription pricing (not one-time) once they see the congestion-data improving their port-call planning. **b) Classification:** - H1 is a **problem risk** — the founders may be wrong that mid-size shippers can't replicate this; many already have small data teams. - H2 is a **solution risk** — the *solution* (subscription) may not match port authority *procurement* norms (capex/grant-based), even if the value is right. **c) MVP experiment to monetise port authorities:** Pick 2 ports (one major, one mid-size). Offer a 90-day free pilot delivering congestion-and-call-time analytics. At pilot end, propose three pricing structures: (i) annual subscription; (ii) one-time licence fee; (iii) per-vessel-call fee from shipping companies (port authority gets revenue share). Measure which model port authorities are willing to formally sign. **d) Revenue model:** **Dual-channel pricing**: - *Shippers:* per-vessel SaaS subscription (₹3–6 L/vessel/year). - *Port authorities:* per-vessel-call fee paid by shippers using the port (₹500–₹2,000/call), routed to the port authority as data licensing revenue with SonarShip taking a 20–25% platform fee. This (i) matches subscription norms for shippers, (ii) keeps port authorities revenue-positive without subscription procurement friction, and (iii) creates a positive feedback loop — more shippers using SonarShip → more port revenue → more port adoption → better data for shippers.
  16. 16.NanoMint Biopharma Bioreactor **a) Strategic Evaluation ** | Option | PMF | Revenue potential | Sales complexity | Scalability | |---|---|---|---|---| | Direct equipment sale to pharma | Validated technically, blocked by regulatory clock (2–3 yr approval) | **High** per deal (₹20–₹50 Cr) but very few buyers | **Very high** — regulatory + procurement + service contracts | Low — bespoke, multi-year cycles | | License to CDMOs (non-exclusive) | Strong interest; faster path | Moderate per CDMO (royalty 4–8% of CDMO revenue × multiple CDMOs) | Moderate — 3–6 month licensing cycles | **High** — many CDMOs, repeatable contracts | | Exclusive licence to giant pharma | Validated buyer; lock-in is the risk | **Very high** upfront ($25M cash) + royalty | Low — single counterparty | **Moderate** — exclusivity caps total mRNA opportunity, but frees resources for other applications | **b) Recommendation (10 marks)** With 15 months of runway, the **Exclusive licence to giant pharma** is the right primary path — accept the $25M upfront, retain rights to non-mRNA applications (gene therapy, biologics), and use the capital to build out those applications. Reasoning: - *Runway alleviation*: $25M upfront removes the runway constraint entirely, giving NanoMint 4–5 years of cushion to develop adjacent applications. - *Validation by a sophisticated buyer*: the giant pharma's willingness to pay $25M is itself proof of the technology's value; further proves the patent's commercial worth. - *Optionality preserved*: NanoMint keeps non-mRNA rights, which are arguably the bigger long-term market (gene therapy, biologics). - *Avoids capex trap*: direct equipment sales are bespoke and capital-heavy; CDMO licensing is good but slower-compounding. - *Strategic-fit risk*: there's a chance NanoMint regrets the lock-in if mRNA proves bigger than expected; this is mitigated by negotiating royalties on *all* mRNA volumes the buyer produces using the tech, not just the headline products. - *Pattern from cases*: this is analogous to founders accepting a strong strategic offer over chasing more capital independently — Rolocule's situation but with much better economics; the right call here is "take the cheque." **c) Business Model Design (10 marks)** — for the exclusive-licensing route - **Revenue model:** $25M upfront + tiered royalties (5–10%) on the licensee's mRNA-product revenue + milestone payments tied to commercial launches + retained R&D rights for non-mRNA applications, where NanoM15 m
  17. 17.HeritageBuy Travel Platform **a) Strategic Evaluation ** | Option | PMF | Revenue potential | Sales complexity | Scalability | |---|---|---|---|---| | Direct consumer travel | Strong love for curation, but low repeat (1–2/yr) | Moderate per booking (₹35K × take rate 10%) but high CAC | Low (B2C) but high marketing cost | Moderate — limited by CAC efficiency | | B2B for boutique hotels & operators | Plausible — they're already approaching | High per partner (₹2–10L/year subscription + bookings) | Moderate — 1–3 month cycles | High — playbook scales across India | | License curation engine to aggregators | Validated by 2 large players | **High** per deal (₹50L–₹5Cr/year), few deals | Very high — 6–12 month enterprise cycles | **Very high** — instant reach via aggregator scale | **b) Recommendation (10 marks)** With 15 months of runway, the **B2B for boutique hotels and tour operators** route is the right primary path, with one aggregator-licensing conversation pursued in parallel as a potential force-multiplier. Reasoning: - *Runway fit*: B2B sales cycles (1–3 months) fit the runway; aggregator licensing (6–12 months) may not. - *Cash flow timing*: subscription + commission revenue compounds monthly; direct-consumer travel has high CAC and slow LTV recovery (low repeat frequency). - *Asset utilisation*: HeritageBuy's curation engine is the asset; selling it as a distribution channel uses that asset for many partners simultaneously vs winning one customer at a time. - *Defensibility*: a network of boutique hotels and tour operators all using HeritageBuy creates a moat — the more sellers using it, the better the buyer-side curation gets, and the more sellers want to join. - *Aggregator licensing as upside*: pursue 1 of the 2 aggregator deals as a non-exclusive partnership; the marketplace volume can supercharge B2B growth in year 2. - *Pattern from cases*: this mirrors HubSpot's "win the high-LTV, sticky segment first, then expand reach via channel partners using accumulated assets" — and avoids Eventbrite's TAM-sizing problem of betting on a fragmented long-tail B2C market when capital is tight. **c) Business Model Design (10 marks)** — for the B2B beachhead - **Revenue model:** Per-partner monthly subscription (₹15K–₹50K/property) + 8–12% commission on bookings routed through HeritageBuy + premium analytics tier for partners managing 5+ properties. - **Key partners:** (i) Boutique hotels, restored heritage properties, monasteries, tour operators (the su15 m

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