// Projects
Built to Solve Real Problems
Tools, analytics, and knowledge base built from 12+ years of global operations experience.
cs-ai-dashboard
Production AI-powered CS operations dashboard. FastAPI + Vanilla JS SPA integrated with Zendesk API. AI draft generation with Claude SDK, 9-market multilingual RAG, 12-pattern spam detection, 6-dimension quality scoring. Phase 1 Draft+Review operational.
toomics-ai-agent/dashboard/
├── main.py ← FastAPI app
├── routers/
│ ├── tickets.py
│ ├── actions.py
│ ├── drafts.py
│ ├── auth.py
│ └── auto_response.py
├── services/
│ ├── zendesk_client.py
│ ├── draft_generator.py
│ ├── claude_service.py
│ ├── rag_service.py
│ ├── spam_detector.py
│ ├── category_classifier.py
│ ├── language_detector.py
│ ├── quality_scorer.py
│ ├── killswitch.py
│ └── auto_responder.py
├── knowledge/ ← 9 lang RAG
├── static/ ← SPA (JS+CSS)
└── tests/ ← 1422 tests
├── main.py ← FastAPI app
├── routers/
│ ├── tickets.py
│ ├── actions.py
│ ├── drafts.py
│ ├── auth.py
│ └── auto_response.py
├── services/
│ ├── zendesk_client.py
│ ├── draft_generator.py
│ ├── claude_service.py
│ ├── rag_service.py
│ ├── spam_detector.py
│ ├── category_classifier.py
│ ├── language_detector.py
│ ├── quality_scorer.py
│ ├── killswitch.py
│ └── auto_responder.py
├── knowledge/ ← 9 lang RAG
├── static/ ← SPA (JS+CSS)
└── tests/ ← 1422 tests
Key Features
AI Draft Generation
95/5 rule — 95% source-based + 5% format only. Claude SDK/Proxy dual mode
9-Market Multilingual RAG
EN + FR/DE/ES/JP/PT/TH/CN/IT keyword-based section routing, EN fallback
Spam Detection P1~P12
Phishing, legal impersonation, social media scam, security/sales spam
4-Stage Category Pipeline
Intent → Content Scoring → Consensus → Post-correction (B2B detect)
🤖 L3 Auto-Response
Phase 1: 60s polling → 8-gate filter → AI Draft → Approve/Edit/Reject
PythonFastAPIClaude APIZendesk APIRAGJWTVanilla JSSPA
🔴 PROBLEM
1,600+ monthly tickets manually handled across 10 markets, 10 languages
No AI assistance — agents write every response from scratch
🟠 STRATEGY
Built production AI dashboard — agents review AI drafts, not write from zero
Invisible AI: customers never know AI exists. Zendesk UX unchanged
Phase 1 (Draft+Review ✅) → Phase 2 (Auto Send) → Phase 3 (Advanced)
🟢 RESULT
Phase 1 live in production — 1422 tests, Phase 1 Draft+Review operational
6-dimension quality scoring (Template/Safety/Action/Relevance/Complete/Length)
Live Dashboard Screenshots
Dashboard — Ticket List + KPI Cards + Charts
🤖 AI Draft Ready — Approve / Edit / Reject
L3 Settings — Master Control + Category & Market
2,100+Tests
8Pipeline Stages
10Languages
9Markets
csy-daily-platform
Daily BizOps operations platform. AI chat agent (Claude Opus), meeting recording + Whisper transcription + auto minutes, task kanban with dependencies, weekly planner with AI retro, chargeback anomaly detection, Slack slash commands, Google Calendar integration, unified search, reactive agent. 110+ API endpoints, 18 routers, built solo in 2 weeks.
daily_ops/
├── app.py ← FastAPI main
├── routers/
│ ├── auth.py
│ ├── chat.py ← AI agent chat
│ ├── briefing.py
│ ├── tasks.py
│ ├── meetings.py
│ ├── weekly.py
│ ├── search.py
│ ├── calendar.py
│ ├── slack_bot.py
│ ├── chargeback.py
│ ├── memory.py
│ └── ... (18 total)
├── services/
│ ├── claude_client.py ← Claude Opus
│ ├── transcriber.py ← Whisper Large
│ ├── search_engine.py
│ ├── agent_memory.py
│ ├── reactive_agent.py
│ ├── slack_notifier.py
│ └── calendar_client.py
└── static/js/ ← 12 frontend modules
├── app.py ← FastAPI main
├── routers/
│ ├── auth.py
│ ├── chat.py ← AI agent chat
│ ├── briefing.py
│ ├── tasks.py
│ ├── meetings.py
│ ├── weekly.py
│ ├── search.py
│ ├── calendar.py
│ ├── slack_bot.py
│ ├── chargeback.py
│ ├── memory.py
│ └── ... (18 total)
├── services/
│ ├── claude_client.py ← Claude Opus
│ ├── transcriber.py ← Whisper Large
│ ├── search_engine.py
│ ├── agent_memory.py
│ ├── reactive_agent.py
│ ├── slack_notifier.py
│ └── calendar_client.py
└── static/js/ ← 12 frontend modules
Key Features
AI Chat Agent (Opus)
Context-aware conversations, memory persistence, tool-use capable
Meeting → Minutes
Record → Whisper Large transcribe → Claude summarize → action items → tasks
Task Kanban + Dependencies
Drag-and-drop kanban, task dependencies, priority management
Reactive Agent
30-min scan → anomaly detect → auto-act → Slack alert
Weekly Planner + AI Retro
Weekly planning, retrospective generation with AI insights
PythonFastAPIClaude OpusWhisperSlack APIGoogle CalendarSQLiteVanilla JS
Live Platform Screenshots
Overview — AI Briefing + KPI + Calendar + Alerts
💬 AI Chat Agent — Claude Opus
System — Cron + Slack + Audit
110+APIs
18Routers
194Tests
2Weeks
🔴 PROBLEM
Daily ops scattered across Slack, email, Sheets, and manual checklists
Meeting minutes manually typed — key decisions and action items lost
🟠 STRATEGY
Built unified BizOps platform — AI agent + meeting pipeline + task management in one place
Reactive agent monitors anomalies (chargeback spikes, overdue tasks) and auto-alerts via Slack
🟢 RESULT
110+ API endpoints, 18 routers — full-stack platform built solo in 2 weeks
Meeting → minutes → tasks pipeline: 30min manual work → 2min automated
ai-translate
Air-gapped AI translation system for internal use. No external API calls — runs entirely on local SQLite vocab.db. Full-text search (FTS5) across 25,070 terms in 10 languages, 12 source integrations, 17-tier priority system. Changelog tracking for every edit.
ai-translate/
├── app.py ← FastAPI server (1,477 lines)
├── vocab.db ← 25,070 terms · 13.0MB
├── changelog.db ← full edit history
├── static/
│ ├── css/
│ │ └── style.css ← 473 lines
│ └── js/
│ └── app.js ← 855 lines SPA
└── templates/
└── index.html ← SPA shell
├── app.py ← FastAPI server (1,477 lines)
├── vocab.db ← 25,070 terms · 13.0MB
├── changelog.db ← full edit history
├── static/
│ ├── css/
│ │ └── style.css ← 473 lines
│ └── js/
│ └── app.js ← 855 lines SPA
└── templates/
└── index.html ← SPA shell
Key Features
FTS5 Full-Text Search
SQLite FTS5 index — partial matching, multilingual morpheme search across 10 languages
12 Source Integration
Translation compilations, product glossaries, Confluence, app language packs — 17-tier priority
Batch Translation
1~20 texts per batch, vocab.db pattern matching, auto language detection
Changelog Tracking
Every add/edit/delete recorded in changelog.db — full audit trail
PythonFastAPISQLiteFTS5Vanilla JSSPAREST API
🔴 PROBLEM
10 language translations scattered across 12+ Excel sheets, Confluence pages, and PDFs
No centralized search — operators manually grep through files to find official terms
🟠 STRATEGY
Built air-gapped translation DB — no external API, no data leaves the server
Consolidated 12 sources with priority-based deduplication (37,839 → 25,070 terms)
SQLite FTS5 for instant search — no external search engine needed
🟢 RESULT
25,070 terms across 10 languages, 10 categories — single source of truth
CS Manual docs + FAQ 101 articles searchable in-app — no login required
Live Screenshots
Term Lookup — FTS5 search with autocomplete
Batch Translation — Auto detect → Target language
Docs — CS Manual + FAQ 101 articles, 10 languages
cs-ai-chatbot
ArchivedAI PythonFastAPI
AI-powered CS chatbot prototype — evolved into cs-ai-dashboard (above). Claude API + RAG, 10 markets, fraud detection, smart escalation. Prototyping role completed.
cs-ai-chatbot/
├── app/
│ ├── main.py
│ ├── routers/
│ │ ├── chat.py
│ │ ├── escalation.py
│ │ └── metrics.py
│ ├── services/
│ │ ├── language_detector.py
│ │ ├── intent_classifier.py
│ │ ├── fraud_detector.py
│ │ └── response_generator.py
│ ├── rag/
│ │ ├── knowledge_base.py
│ │ └── template_store.py
│ └── models/
│ └── schemas.py
├── tests/ ← 161 tests
├── Dockerfile
└── .github/workflows/
└── ci-cd.yml
├── app/
│ ├── main.py
│ ├── routers/
│ │ ├── chat.py
│ │ ├── escalation.py
│ │ └── metrics.py
│ ├── services/
│ │ ├── language_detector.py
│ │ ├── intent_classifier.py
│ │ ├── fraud_detector.py
│ │ └── response_generator.py
│ ├── rag/
│ │ ├── knowledge_base.py
│ │ └── template_store.py
│ └── models/
│ └── schemas.py
├── tests/ ← 161 tests
├── Dockerfile
└── .github/workflows/
└── ci-cd.yml
Key Features
Intent Classification
7 categories × 32 sub-intents, 90/10 hybrid
Fraud Detection
9 Red Flag scoring (0~100) + Minna/Ethoca block
3-tier Smart Routing
L1 Escalate / L2 Draft / L3 Self-service (38%)
Multilingual Templates
30 templates × 10 languages, quality scoring
PythonFastAPIClaude APIChromaDBDockerPydantic v2GitHub Actions
🔴 PROBLEM
1,600+ monthly tickets across 10 markets in 10 languages handled manually
Inconsistent response quality — no fraud detection at intake
🟠 STRATEGY
Designed hybrid AI chatbot — 90% template consistency + 10% Claude AI flexibility
3-tier routing with RAG knowledge base and real-time fraud scoring
Full CI/CD: 161 tests → Docker build → staging deploy
🟢 RESULT
38% self-service target (~630 tickets/month automated)
9 Red Flag fraud detection operational with quality scoring (0.0~1.0)
Message Processing Pipeline
bizops-artifacts-portal
Public documentation portal hosting 58 operational artifacts across 9 categories. CS manuals (EN/KO), FAQ (10 languages), ToS (10 languages), chargeback templates, AI dashboard docs, analysis reports, and weekly archives.
bizops-artifacts-portal/
├── index.html
├── cs-operations/ ← 10 docs
│ ├── cs_manual_EN.html
│ ├── cs_manual_KO.html
│ ├── fraud_analysis.html
│ ├── cs_infographic.html
│ ├── cs_flow_diagram.html
│ └── weekly-reports/ ← W1~W4
├── chargeback/ ← 3 docs
│ ├── operation_manual.html
│ ├── template.xlsx
│ └── flow_diagram.html
├── ai-dashboard/ ← 6 docs
│ ├── policy.html
│ ├── quickstart.html
│ └── l3_engine.html
├── ip-protection/ ← 1 doc
│ └── ip_manual.html
└── dashboards/ ← 2 docs
├── ticket_dashboard.html
└── dashboard_manual.html
├── index.html
├── cs-operations/ ← 10 docs
│ ├── cs_manual_EN.html
│ ├── cs_manual_KO.html
│ ├── fraud_analysis.html
│ ├── cs_infographic.html
│ ├── cs_flow_diagram.html
│ └── weekly-reports/ ← W1~W4
├── chargeback/ ← 3 docs
│ ├── operation_manual.html
│ ├── template.xlsx
│ └── flow_diagram.html
├── ai-dashboard/ ← 6 docs
│ ├── policy.html
│ ├── quickstart.html
│ └── l3_engine.html
├── ip-protection/ ← 1 doc
│ └── ip_manual.html
└── dashboards/ ← 2 docs
├── ticket_dashboard.html
└── dashboard_manual.html
Categories
CS Operations (10 docs)
EN/KO manuals, fraud report, flow diagrams, weekly reports
AI Dashboard (6 docs)
Policy, quickstart, L3 engine, roadmap, settings
Chargeback (3 docs)
8 PG dispute manual + evidence form V5 + template v2
Dashboards & IP (3 docs)
Ticket dashboard, manual, IP protection guide
GitHub PagesHTML/CSSGitHub ActionsClaude AI@media printConfluence
🔴 PROBLEM
Operational docs scattered across Confluence, Sheets, and local files
No single source of truth for 10 global markets' operations
🟠 STRATEGY
Built centralized GitHub Pages portal with CI/CD auto-deployment
5 categories, EN/KO bilingual, print-optimized for PDF export
🟢 RESULT
58 documents publicly accessible — all authored solo
Single source of truth for 10 global markets with auto-deploy on push
📁 Documents by Category
Portal Screenshot
BizOPS Artifacts Portal — 58 docs, 9 categories
bizops-automation-toolkit
EXECUTION
MIT
Python
Business process automation tools for global operations teams. Automates Zendesk reporting, chargeback tracking, Slack team digests, and Confluence documentation.
bizops-automation-toolkit/
├── zendesk-reporting/
│ ├── weekly_report_generator.py
│ ├── cs_kpi_analyzer.py
│ ├── template_manager.py
│ └── sample_data/
├── chargeback-tracker/
│ ├── sheets_sync.py
│ ├── alert_generator.py
│ └── sample_data/
├── slack-team-digest/
│ ├── collect_updates.py
│ ├── digest_generator.py
│ └── slack_webhook_sender.py
└── confluence-docs-sync/
├── manual_updater.py
└── template_generator.py
├── zendesk-reporting/
│ ├── weekly_report_generator.py
│ ├── cs_kpi_analyzer.py
│ ├── template_manager.py
│ └── sample_data/
├── chargeback-tracker/
│ ├── sheets_sync.py
│ ├── alert_generator.py
│ └── sample_data/
├── slack-team-digest/
│ ├── collect_updates.py
│ ├── digest_generator.py
│ └── slack_webhook_sender.py
└── confluence-docs-sync/
├── manual_updater.py
└── template_generator.py
Key Features
Weekly HTML Report Auto-gen
Zendesk API → Chart.js dashboard + KPI cards
Chargeback Real-time Sync
Google Sheets API → 30-column CSV/JSON
Slack Team Digest
Weekly updates collection + webhook delivery
Confluence Auto-updater
CS manual + category template generator
PythonZendesk APIGoogle Sheets APISlack WebhookConfluence APIpandas
🔴 PROBLEM
Manual reporting consumed 2 days/week — repetitive data collection across 4 platforms
Chargeback data siloed in Google Sheets with no automated tracking
🟠 STRATEGY
Built modular Python toolkit connecting 4 platform APIs independently
Each module runs standalone with auto-commit and sample data for portability
🟢 RESULT
Report generation 2 days → 1 hour (95% reduction)
Chargeback data real-time synced, team digest & docs fully automated
Automation Pipeline
global-cs-analytics
ANALYTICSMIT Jupyter
CS data analytics & interactive dashboards for multi-market operations. Ticket volume trends, SLA benchmarking, category analysis, and revenue save tracking.
global-cs-analytics/
├── notebooks/
│ ├── 01_ticket_volume_analysis.ipynb
│ ├── 02_response_time_benchmark.ipynb
│ ├── 03_category_deep_dive.ipynb
│ └── 04_revenue_save_tracking.ipynb
├── dashboards/
│ └── cs_dashboard.html
├── data/
│ └── synthetic_tickets_10k.csv
└── scripts/
├── data_generator.py
└── kpi_calculator.py
├── notebooks/
│ ├── 01_ticket_volume_analysis.ipynb
│ ├── 02_response_time_benchmark.ipynb
│ ├── 03_category_deep_dive.ipynb
│ └── 04_revenue_save_tracking.ipynb
├── dashboards/
│ └── cs_dashboard.html
├── data/
│ └── synthetic_tickets_10k.csv
└── scripts/
├── data_generator.py
└── kpi_calculator.py
Key Features
Multi-market Volume Trends
10 markets, 11 languages, weekly WoW comparison
SLA Benchmarking
FRT, resolution time, agent performance
9-stage Noise Filtering
82% noise removal → clean data pipeline
Interactive Dashboard
Chart.js + Plotly KPI visualization
PythonJupyterpandasPlotlyChart.jsZendesk API
🔴 PROBLEM
Raw Zendesk data was 82% noise — no structured analysis possible
13K+ tickets across 10 markets with no cross-market pattern visibility
🟠 STRATEGY
Built 9-stage filtering pipeline with 4-tier category classification engine
Created interactive Plotly/Chart.js dashboards for KPI visualization
🟢 RESULT
82% noise eliminated — clean data pipeline established
13,400+ tickets across 7 months analyzed with proactive insights
📊 Data Pipeline
payment-ops-playbook
EXPERTISECC BY 4.0Docs
Open knowledge base for payment gateway operations. Covers PG fundamentals, cross-border settlements, fraud & chargeback management, and market localization from hands-on experience.
payment-ops-playbook/
├── 01-pg-fundamentals/
│ ├── payment-flow-overview.md
│ ├── 3-party-vs-4-party-system.md
│ └── card-brand-acquiring.md
├── 02-cross-border-payments/
│ ├── cross-border-settlement.md
│ ├── fx-risk-management.md
│ └── aml-kyc-compliance.md
├── 03-fraud-and-chargeback/
│ ├── fds-rule-design.md
│ ├── chargeback-dispute-process.md
│ └── fraud-pattern-analysis.md
├── 04-localization/
│ ├── thailand-pg-setup.md
│ └── multi-market-operations.md
└── diagrams/
├── payment-flow.mermaid
└── settlement-cycle.mermaid
├── 01-pg-fundamentals/
│ ├── payment-flow-overview.md
│ ├── 3-party-vs-4-party-system.md
│ └── card-brand-acquiring.md
├── 02-cross-border-payments/
│ ├── cross-border-settlement.md
│ ├── fx-risk-management.md
│ └── aml-kyc-compliance.md
├── 03-fraud-and-chargeback/
│ ├── fds-rule-design.md
│ ├── chargeback-dispute-process.md
│ └── fraud-pattern-analysis.md
├── 04-localization/
│ ├── thailand-pg-setup.md
│ └── multi-market-operations.md
└── diagrams/
├── payment-flow.mermaid
└── settlement-cycle.mermaid
Chapters
PG Fundamentals
Payment flow, 3-party vs 4-party, card brand acquiring
Cross-border Payments
Settlement, FX risk, AML/KYC compliance
Fraud & Chargeback
FDS rule design, dispute process, pattern analysis
Market Localization
Thailand PG setup, multi-market playbook
Visa / MastercardNHN KCPCross-borderFDSAML/KYCMermaid
🔴 PROBLEM
PG operations knowledge siloed in individuals — no structured handoff
No onboarding guide for cross-border settlement or fraud operations
🟠 STRATEGY
Documented 10+ years of PG experience into 4 structured chapters
Added Mermaid diagrams for visual payment & settlement flows
🟢 RESULT
Comprehensive playbook covering 4 core domains with visual flows
Reusable reference for PG partnerships, FDS design, multi-market expansion
avatara-nft-platform
BLOCKCHAINNX3 Games Web3
Blockchain-based digital avatar NFT platform designed at NX3 Games (NEXON). Led end-to-end service design — NFT minting pipeline, marketplace trading, tokenomics, and GameFi integration on Polygon network.
avatara-nft-platform/
├── contracts/
│ ├── AvataraNFT.sol
│ ├── Marketplace.sol
│ └── TokenRewards.sol
├── avatar-builder/
│ ├── trait_generator.py
│ ├── metadata_engine.py
│ └── rarity_calculator.py
├── marketplace/
│ ├── listing_service.ts
│ ├── auction_engine.ts
│ └── royalty_splitter.ts
├── gamefi-integration/
│ ├── staking_module.sol
│ └── play_to_earn.sol
└── docs/
├── tokenomics.md
└── whitepaper.md
├── contracts/
│ ├── AvataraNFT.sol
│ ├── Marketplace.sol
│ └── TokenRewards.sol
├── avatar-builder/
│ ├── trait_generator.py
│ ├── metadata_engine.py
│ └── rarity_calculator.py
├── marketplace/
│ ├── listing_service.ts
│ ├── auction_engine.ts
│ └── royalty_splitter.ts
├── gamefi-integration/
│ ├── staking_module.sol
│ └── play_to_earn.sol
└── docs/
├── tokenomics.md
└── whitepaper.md
Key Features
Generative Avatar Minting
Trait-based generative system with rarity tiers
NFT Marketplace
Listing, auction, royalty split on Polygon
GameFi Integration
Staking rewards + Play-to-Earn mechanics
Tokenomics Design
Supply control, burn mechanics, reward distribution
SolidityPolygonNFTWeb3.jsIPFSReactTypeScriptHardhat
🔴 PROBLEM
Game assets locked in centralized servers — no true player ownership or cross-game portability
No secondary market for in-game items — zero residual value for players
🟠 STRATEGY
Designed NFT-based avatar system on Polygon — low gas, high throughput for game assets
Built marketplace with royalty splits (creator + platform) and P2E staking mechanics
Managed tokenomics — supply, burn, reward cycles aligned with game economy
🟢 RESULT
End-to-end NFT platform designed — minting → marketplace → GameFi full lifecycle
Web3 partnership ecosystem established with multiple blockchain partners
NFT Lifecycle Architecture
global-mass-remittance
NEW BIZNHN KCP Fintech
Designed and launched a cross-border multi-currency mass remittance platform for global partners. Developed KRW-KRW and USD-USD localized bulk transfer services for Payoneer, Uber, and Airbnb — enabling mass bank transfers to sub-merchants without a Korean PG license. Generated ₩200M+ monthly fixed revenue.
Monthly Fixed Revenue
₩200M+
Minimum guaranteed / month
Revenue Model
Fixed + %
Fixed KRW per transfer + commission rate
Transfer Frequency
Daily
Daily & weekly cross-border USD settlement
global-mass-remittance/
├── 01-cross-border-usd/
│ ├── usd-virtual-account-design.md
│ ├── bulk-transfer-api-spec.md
│ ├── settlement-cycle-daily.md
│ └── revenue-model-analysis.md
├── 02-localized-remittance/
│ ├── krw-krw-domestic-transfer.md
│ ├── usd-usd-fx-settlement.md
│ └── sub-merchant-onboarding.md
├── 03-partner-integration/
│ ├── payoneer-integration.md
│ ├── uber-settlement-flow.md
│ ├── airbnb-host-payout.md
│ └── partner-compliance-kyc.md
├── 04-van-banking/
│ ├── van-partner-contract.md
│ └── banking-api-integration.md
└── diagrams/
├── remittance-flow.mermaid
└── settlement-cycle.mermaid
├── 01-cross-border-usd/
│ ├── usd-virtual-account-design.md
│ ├── bulk-transfer-api-spec.md
│ ├── settlement-cycle-daily.md
│ └── revenue-model-analysis.md
├── 02-localized-remittance/
│ ├── krw-krw-domestic-transfer.md
│ ├── usd-usd-fx-settlement.md
│ └── sub-merchant-onboarding.md
├── 03-partner-integration/
│ ├── payoneer-integration.md
│ ├── uber-settlement-flow.md
│ ├── airbnb-host-payout.md
│ └── partner-compliance-kyc.md
├── 04-van-banking/
│ ├── van-partner-contract.md
│ └── banking-api-integration.md
└── diagrams/
├── remittance-flow.mermaid
└── settlement-cycle.mermaid
Key Deliverables
Cross-border USD Mass Remittance
USD virtual account-based bulk transfer with daily/weekly settlement
KRW-KRW / USD-USD Services
Bulk bank transfers for partners without Korean PG license
Global Partner Acquisition
Payoneer · Uber · Airbnb onboarded for Korean market settlement
VAN Banking Infrastructure
Mass bank transfer execution and reconciliation
Partner Lock-in & Revenue Defense
Remittance as value-add → partner retention + new acquisition
Cross-borderUSD Virtual AccountMass RemittancePayoneerUberAirbnbVANKRW-KRWUSD-USD
🔴 PROBLEM
Global e-wallet providers (Payoneer, Uber, Airbnb) needed Korean sub-merchant settlement but couldn't obtain PG licenses
NHN KCP revenue relied solely on PG processing fees — no defense against partner churn
No infrastructure for cross-border multi-currency bulk transfers to Korean recipients
🟠 STRATEGY
Designed USD virtual account-based cross-border bulk remittance platform with daily/weekly settlement
Developed KRW-KRW & USD-USD localized mass transfer services for partners without Korean PG licenses
Partnered with domestic VAN providers for banking API integration and automated reconciliation
Positioned remittance as value-add for existing PG partners → lock-in + new acquisition channel
🟢 RESULT
New platform launched — ₩200M+ monthly fixed revenue with per-transaction commission
Onboarded Payoneer, Uber, Airbnb as remittance clients for Korean market settlement
Dual revenue model: fixed KRW fee + % commission on each cross-border remittance
Existing partners locked in; remittance became a new partner acquisition channel
Mass Remittance Platform Architecture
📊 Service Impact Metrics
