Airy Rooms \Indonesia

VALIDATION (Months 4-6): Convert 30 pilot hotels to paid 'Starter Plan' ($50/month): white-label booking engine (Next.js template with their branding), basic PMS (room inventory, calendar, manual pricing), payment processing via Xendit (we take 2.5%), and AI concierge handling FAQs. Onboarding is semi-automated: 2-hour Zoom call, then AI guides them through setup via WhatsApp. Success metric: Pilot hotels see 25%+ increase in direct bookings (vs. OTA reliance) within 60 days, measured by comparing pre/post revenue splits. Retention target: 80%+ after 3 months. Learning focus: Which features drive retention? Is $50 price point sustainable? Can we onboard hotels without field visits? Cost: $25K (2 engineers, 1 ops person, API costs).

GROWTH (Months 7-12): Scale to 200 hotels across 5 key Indonesian routes (Jakarta-Bali, Surabaya-Yogyakarta, Medan-Lake Toba, Makassar-Toraja, Bandung-Pangandaran). Launch 'Pro Plan' ($150/month): adds AI dynamic pricing (Llama model trained on local events, holidays, competitor rates), automated review responses, and guest segmentation. Introduce revenue share tier for hotels doing $10K+/month: 8% of direct bookings instead of flat fee (aligns incentives, increases LTV). Growth channels: (1) Content marketing—publish 'State of Indonesian Budget Hotels' report with data from our audits, get press coverage; (2) Referral program—existing hotels get 3 months free for each referral; (3) WhatsApp outreach—scrape Google Maps for budget hotels, send personalized AI-generated messages offering free audit. Goal: $25K MRR ($15K subscriptions + $10K payment processing), 70% gross margin. Cost: $120K (team of 6, scaled infrastructure, marketing).

MOAT (Months 13-24): Build defensibility through data network effects and financial services integration. Launch 'Nusantara Capital'—offer hotels instant payouts (get paid within 24 hours vs. 7-14 days from OTAs) for a 2% fee, funded by our credit line. This creates switching costs since hotels become dependent on cash flow. Introduce 'Travel Now, Pay Later' for guests (partner with Indonesian BNPL providers like Kredivo), capturing 3-5% merchant fees. Use our dataset of 200+ hotels to train proprietary models: (1) Demand forecasting (predict occupancy 30 days out with 85%+ accuracy); (2) Churn prediction (flag hotels likely to cancel, trigger retention campaigns); (3) Upsell recommendations (identify hotels ready for Pro plan based on booking volume). Launch marketplace for hotel services: connect hotels with vetted suppliers (linens, toiletries, maintenance) and take 10% commission. Goal: $100K MRR, 75% gross margin, sub-5% monthly churn, clear path to profitability at 500 hotels. The moat: our AI models get better with every hotel added, and financial services create 10x higher switching costs than pure software.