Origami \Japan

Origami was Japan's pioneering mobile payment platform, launched in 2012 to digitize cash-heavy Japanese commerce. The value proposition centered on enabling smartphone-based QR code payments at physical retailers, offering merchants lower transaction fees than credit cards (typically 3.24% vs. credit card's 3-5%) and consumers cashback rewards. The 'why now' was compelling: Japan had 94% smartphone penetration by 2015 but remained stubbornly cash-dependent (80% of transactions), creating a massive digitization opportunity. Origami positioned itself as the bridge between traditional Japanese retail and mobile-first commerce, partnering with major brands like Lawson, KFC Japan, and Aeon to build merchant acceptance. The platform processed payments through QR codes scanned at point-of-sale, with instant settlement and loyalty integration. However, Origami entered a market that would become brutally competitive, facing well-capitalized rivals like PayPay (backed by SoftBank/Yahoo with $1B+ war chest), LINE Pay (messaging app with 80M users), and Rakuten Pay (e-commerce giant with existing customer base). Despite $80M in funding from SoftBank and SBI, Origami struggled with the classic two-sided marketplace problem: merchants wouldn't adopt without users, users wouldn't download without merchant acceptance, and competitors were willing to burn billions on subsidies to win market share.

SECTOR Financials
PRODUCT TYPE Financial & Fintech
TOTAL CASH BURNED $80.0M
FOUNDING YEAR 2012
END YEAR 2020

Discover the reason behind the shutdown and the market before & today

Failure Analysis

Failure Analysis

Origami died from competitive asphyxiation in a subsidy-driven market war it couldn't afford to win. The mechanics of failure unfolded across three phases: (1)...

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Market Analysis

Market Analysis

Japan's digital payments market underwent brutal consolidation post-2018, with PayPay emerging as the dominant winner (55% market share, 60M users, 5M merchants as of...

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Startup Learnings

Startup Learnings

Two-sided marketplaces in winner-take-all categories require 'unfair advantages' beyond product quality—either (1) an existing user base to bootstrap one side (LINE's 80M users, Rakuten's...

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Market Potential

Market Potential

Japan's digital payments market remains massive and underpenetrated despite Origami's failure. TAM analysis: Japan's retail market is $1.4T annually, with cashless penetration reaching only...

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Difficulty

Difficulty

Building a mobile payment platform in 2024 is dramatically easier than 2012. Modern infrastructure like Stripe Connect, Adyen for Platforms, or Rapyd provides white-label...

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Scalability

Scalability

Mobile payment platforms exhibit moderate scalability with significant friction. Positive factors: digital product with near-zero marginal cost per transaction after infrastructure is built; network...

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Rebuild & monetization strategy: Resurrect the company

Pivot Concept

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AI-native B2B payment and cash flow management platform for Japanese SMEs, focusing on construction and manufacturing sectors where 70% of transactions remain paper-based. Unlike Origami's consumer focus, Kessai AI targets the $400B SMB payment market with embedded AI bookkeeping, invoice reconciliation, and predictive cash flow forecasting. The wedge: automated digitization of paper invoices using GPT-4 Vision + Japanese OCR, eliminating manual data entry that costs SMEs 15-20 hours/month. The moat: proprietary cash flow prediction models trained on industry-specific payment cycles (construction has 60-90 day terms), enabling embedded lending/factoring at better rates than banks. Revenue model: SaaS ($50-200/month for AI tools) + transaction fees (0.5-1% on payments) + lending spreads (3-5% on invoice factoring). Distribution: partner with industry associations (Japan Federation of Construction Contractors, 470K members) and accounting software providers (Freee, Money Forward) for B2B2B channel. The AI-native advantage: incumbents (PayPay, Rakuten) lack vertical-specific data models and SMB sales infrastructure; traditional factoring companies (MUFG, Mizuho) have 2-week underwriting cycles vs. Kessai's instant AI-driven approvals. This targets the market Origami ignored (B2B vs. B2C), leverages AI as a defensible moat (not just infrastructure), and monetizes high-margin services (lending, SaaS) rather than thin payment fees.

Suggested Technologies

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Stripe Connect (payment processing, KYB, multi-party payouts)Supabase (Postgres database, real-time sync, auth)GPT-4 Vision API (invoice OCR and data extraction)LangChain + Claude 3.5 Sonnet (cash flow forecasting, anomaly detection)Plaid (bank account linking for Japanese banks via partnership)Next.js + Vercel (web dashboard for merchants)React Native (mobile app for on-site invoice capture)Retool (internal ops dashboard for underwriting)Segment (analytics and customer data platform)AWS Lambda (serverless payment webhooks and batch processing)PostgreSQL + TimescaleDB (time-series cash flow data)Twilio (SMS notifications for payment confirmations)

Execution Plan

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Phase 1

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Step 1 - Wedge (Months 1-3): Build AI invoice digitization tool as free standalone product. Target 100 construction SMEs in Tokyo via industry association partnerships. Use GPT-4 Vision to extract line items from photos of paper invoices, auto-populate accounting software (Freee/Money Forward integration). Metric: 60% of users save 10+ hours/month, 40% conversion to paid tier. This establishes product-market fit for AI tooling before introducing payments.

Phase 2

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Step 2 - Validation (Months 4-6): Launch payment rails for digitized invoices. Offer 0.5% transaction fees (vs. bank wire's ¥500-800 flat fee) + instant settlement. Partner with 1-2 regional banks for white-label processing. Add AI cash flow dashboard showing 30/60/90-day receivables predictions. Metric: $500K monthly GMV, 25% of invoice volume processed through platform, NPS >50. Validate that users will switch payment methods for AI insights + cost savings.

Phase 3

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Step 3 - Growth (Months 7-12): Launch invoice factoring (advance 80-90% of invoice value for 3-5% fee). Use AI underwriting model trained on payment history to approve in <24 hours vs. banks' 2-week process. This creates viral loop: suppliers demand buyers use Kessai for faster payment, buyers adopt for cash flow visibility. Expand to Osaka and Nagoya via accounting firm partnerships (top 50 firms serve 80% of SMEs). Metric: $5M monthly GMV, 500 active merchants, 30% using factoring, 15% MoM growth.

Phase 4

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Step 4 - Moat (Months 13-24): Build proprietary industry-specific cash flow models (construction payment cycles, seasonal manufacturing trends) that improve underwriting accuracy to 95%+ (vs. 70-80% for generic models). Launch SaaS tier ($100-300/month) with advanced features: AI-powered payment term negotiation, supplier risk scoring, automated dunning. Integrate with ERP systems (SAP, Oracle) for enterprise upmarket move. Metric: $20M monthly GMV, 40% gross margin (SaaS + lending spreads), Series A readiness. The data moat makes this defensible—each transaction improves prediction models, creating compounding advantage incumbents can't replicate without years of vertical-specific data.

Monetization Strategy

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Hybrid model targeting 40-50% gross margins: (1) SaaS subscriptions ($50-300/month based on invoice volume): Free tier for <10 invoices/month with basic OCR; Pro tier ($100/month) adds cash flow forecasting and accounting integrations; Enterprise tier ($300+/month) includes API access and custom models. Target 60% of users on paid plans by Month 12. (2) Transaction fees (0.5-1% of payment value): Charged to payers for instant settlement vs. 30-90 day terms. Lower than credit card fees (3-5%) but higher than bank wires due to added value (AI reconciliation, instant confirmation). Target 40% of invoice volume processed through platform. (3) Invoice factoring/lending (3-5% of advanced amount): Advance 80-90% of invoice value within 24 hours, collect full amount at term. AI underwriting reduces default risk to <2% (vs. 5-8% for traditional factors). This is the highest-margin revenue stream (60-70% gross margin) and scales with trust/data. Target 30% of users adopting factoring by Month 18. (4) Data/API licensing (future): Anonymized cash flow benchmarks sold to banks, insurers, and industry associations. Blended revenue model: Year 1 = 60% SaaS, 30% transaction fees, 10% factoring; Year 3 = 30% SaaS, 30% transaction fees, 40% factoring. Unit economics: CAC $500 (via B2B2B partnerships), LTV $8,000+ (3-year retention, $200/month blended ARPU), LTV:CAC >15x. This avoids Origami's mistake of thin transaction fees by monetizing high-margin AI services and embedded lending.

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