AI-Powered Commerce
Operating System
for Southeast Asia
An AI-native platform unifying commerce, marketing, loyalty, and operations — built to scale across Southeast Asia.
11
Microservices
540+
API Endpoints
24
AI Agent Tools
~50K
Daily API Calls
Live
In Production
March 2026 · Bangkok, Thailand
The Problem
E-Commerce Brands in SEA Are
Drowning in Operational Complexity
Selling on multiple platforms means managing multiple dashboards, manual workflows, and zero automation. There's no integrated solution built for this region.
Fragmented Operations
Brands sell on 5+ platforms — Lazada, Shopee, TikTok Shop, Facebook, own website. Each has its own dashboard, data format, and API. No unified view.
A single brand manages 4-6 separate dashboards daily
Manual Everything
Content creation, ad management across Meta/Google/TikTok, loyalty, KOL management — all done manually via spreadsheets, LINE chats, and Google Sheets.
60% of team time wasted on repetitive tasks
No AI, No Automation
SEA e-commerce tools are 5+ years behind US/EU. No affordable AI-integrated commerce platform exists for the region. Brands can't compete at scale.
Zero affordable AI solutions for SEA e-commerce
$230B+
SEA E-Commerce Market (2025)
$35B+
Thailand E-Commerce
500K+
Active Online Sellers in Thailand
15% YoY
Market Growth Rate
Our Solution
An AI-Native Operating System
for E-Commerce Brands
One platform that connects everything — with an autonomous AI agent at the center.
The Platform
AI
SuperchargeAgent — AI Brain
Autonomous AI with 24 tools, LLM inference, RAG, vector search. Runs 24/7.
1
Create — Creative Hub
AI-generated images, videos, ad copy. 8 AI models. Auto-schedule 12+ platforms.
2
Sell — Salepage + Marketplace Sync
Landing pages, Lazada/Shopee/TikTok sync, COD, Payment Gateway, POS.
3
Engage — KOL + CRM
Loyalty, missions, influencer management, affiliate tracking. 260+ endpoints.
4
Advertise — Ads Platform
Automated Meta + Google + TikTok campaigns. ROAS auto-optimization.
5
Analyze — CDP
Unified customer profiles, cross-platform identity resolution.
6
Operate — WMS + HR + PM
Warehouse, team ops, project management with MCP integration.
How We're Different
- AI-first, not AI-bolted-on — SuperchargeAgent orchestrates across ALL modules autonomously.
- Built for Thailand & SEA — COD, LINE, Thai NLP, local marketplace APIs.
- 11 systems, one platform — Competitors offer 1-2 of these.
- SaaS-ready architecture — Multi-tenant, API-first, event-driven.
- Already in production — Real revenue, real customers, 50K API calls/day.
Currently Live
50K
API calls/day
15K
Events/day
100K+
Profiles
2K
AI calls/day
AI-First Company
AI Is Our Core Technology —
Not a Feature
Every system in our platform is designed around AI as the central decision-making layer. SuperchargeAgent isn't an add-on — it's the brain that runs the entire business.
Traditional E-Commerce Platforms
AI = chatbot widget bolted onto existing system
Humans make every decision, AI assists occasionally
Data silos — AI can't access cross-system data
No memory — AI forgets context every session
No autonomous action — AI suggests, humans execute
Moonshot — AI-First Architecture
AI = the operating layer that orchestrates all 11 systems
AI makes decisions autonomously — humans set strategy
Unified data access — AI calls any API across 9 systems
Long-term memory — pgvector stores decisions & patterns
Full autonomous execution — AI decides AND acts 24/7
AI Capabilities Built Into the Platform
LLM
Language Model Inference
Thai NLP, ad copy generation, report summarization, customer service, multi-step reasoning
RAG
Retrieval-Augmented Generation
pgvector embeddings, semantic search, decision history retrieval, pattern matching
GEN
Generative AI Pipeline
Image generation (8 models), video generation, product photography, ad creative automation
AGT
Autonomous Agent System
24 tools, 9 system APIs, multi-step planning, tool orchestration, cronjob execution 24/7
~2K
AI Inference Calls/Day
24
Agent Tools
8
Generative AI Models
9
Systems AI Controls
24/7
Autonomous Operation
Cloud infrastructure is the bottleneck: To scale from 2K to 25K+ AI inference calls/day, train models on proprietary Thai e-commerce data, and serve multiple brands — we need managed ML infrastructure, GPU compute, and vector database at scale.
Architecture
Platform Architecture
11 microservices connected through centralized gateway, async event bus, and autonomous AI — designed for cloud-native deployment.
AI Orchestration
SuperchargeAgent · LLM Inference · RAG · Embeddings · 24 Tools
↓
Infrastructure
API Gateway · Go + Chi
Event Bus · Redis Streams
↓
Data & Compute
Managed PostgreSQL
Redis Cache
pgvector
Object Storage
ML Inference
Job Queue
↓
Application Layer — 11 Containerized Microservices
Creative Hub
CDP
Salepage
KOL + CRM
Ads Platform
HR
WMS
Task Manager
↓
External Integrations
LazadaShopeeTikTok ShopLINEMeta AdsGoogle AdsTikTok AdsPayment GatewayPOS SystemAI Engine
5
Managed Databases
11
Containerized Services
8
AI/ML Models
18
Event Streams
SuperchargeAgent
AI & ML Architecture
How our autonomous AI processes, reasons, and learns — from raw data to autonomous action across 9 systems.
Input
Customer Events
Sales Data
Ad Metrics
Inventory
Team Messages
↓
Processing
Embedding Generation
Structured Extraction
Classification
↓
Reasoning
LLM Inference
Tool Use Orchestration
Multi-step Planning
↓
Memory
pgvector Long-Term Memory
Decision History
Pattern Store
↓
Action
API Execution
Task Creation
Team Comms
Campaign Mgmt
Pattern Match
24 tools across 9 systems
RAG retrieval-augmented generation
Autonomous cronjob execution 24/7
Data Layer
Data Architecture
How data is stored, processed, and served across the ecosystem.
Transactional
PostgreSQL 15/16
ACID-compliant storage across 5 microservices. 100+ tables for orders, customers, inventory, campaigns, HR.
5 databases · 100+ tables
Real-Time
Redis Streams + Cache
Event-driven messaging with consumer groups, dead letter queues, sub-millisecond caching.
18 event types · Pub/Sub
AI / Analytics
pgvector + Embeddings
Vector embeddings for semantic search, agent memory, decision history retrieval, pattern recognition.
RAG pipeline · Similarity search
Data Pipeline
Ingest
Multi-platform sync
→
Transform
CDP unification
→
Enrich
AI + embeddings
→
Serve
API + events
Automation Impact
Before vs After:
What Our Platform Automates
Every manual process replaced by an automated, cloud-powered workflow — freeing teams to focus on growth instead of operations.
Before — Manual
Order Processing: Copy orders from 5 dashboards into spreadsheet. Check stock manually. Update each platform separately. ~2 hours/day
Ad Management: Log into Meta, Google, TikTok separately. Check ROAS manually. Pause/scale one by one. ~3 hours/day
Content Creation: Brief designer → wait 2-3 days → revisions → upload to each platform. ~4 hours/asset
KOL Tracking: Google Sheets for missions. Manual UTM links. No real attribution. No ROI data
Reporting: Pull data from 6 sources. Build report in Sheets. Takes half a day. ~4 hours/report
→
After — Automated
Order Processing: 1 purchase → 6 automated actions in 10 seconds. CDP unifies, WMS deducts, CRM awards. Zero manual work
Ad Management: SuperchargeAgent monitors ROAS hourly. Auto-pauses <2x, scales >4x. Cross-platform. 24/7 autonomous
Content Creation: AI generates 20 variations + Thai copy → auto-distributes to 12+ platforms. 3 minutes
KOL Tracking: Full attribution via CRM. Every conversion tracked to specific KOL. Auto-commission. Real ROI per KOL
Reporting: SuperchargeAgent auto-generates Thai daily report at 18:00 → Google Chat. Fully automated
Cloud requirement: Event streaming for real-time processing, always-on AI inference, auto-scaling containers, managed databases with read replicas, GPU compute for content generation.
Infrastructure
Cloud Compute Requirements
Per Workflow
Each automated workflow demands specific cloud infrastructure. Here's exactly what runs behind every action.
Order Processing
What happens: Customer orders → Event Bus dispatches to 5 services simultaneously → CDP unifies identity → WMS deducts stock → CRM awards points → Ads syncs audience → Agent reports
Event streaming · 5 concurrent consumers · Managed PostgreSQL (write-heavy) · Redis pub/sub · ~500 events/hour peak
AI Content Generation
What happens: Brand brief → 8 AI models generate images/video → NLP writes Thai copy per platform → CDN delivers assets → auto-schedule to 12+ channels
GPU inference (image gen) · LLM inference (copy) · Object storage 50GB+/month · CDN for global delivery · Async job queue
Ad Optimization
What happens: Hourly ROAS check across Meta/Google/TikTok → Agent analyzes with RAG memory → auto-pauses/scales campaigns → shifts budgets → notifies team
Always-on LLM inference · pgvector for RAG · Scheduled compute (hourly cron) · 3 external API integrations · Redis cache
Customer Intelligence
What happens: CDP syncs 4 marketplaces every 5 min → identity resolution → RFM segmentation → audience sync to 3 ad platforms → predictive scoring
Managed DB with read replicas · Batch ETL pipeline · 100K+ profile processing · Embedding generation · Data warehouse queries
Autonomous Agent
What happens: SuperchargeAgent runs 24/7 → calls 9 systems via API → reasons with RAG → takes autonomous actions → creates tasks → reports to team
24/7 LLM inference endpoint · pgvector (RAG memory) · 24 API tool calls · Structured output · ~2K inference calls/day → 25K+ at scale
Real-Time Processing
Event-Driven Architecture
& Data Flow
Every action in the platform generates events that flow through Redis Streams to trigger downstream automation — requiring cloud-grade streaming infrastructure.
18 Event Types Flowing Through the System
Commerce Events
order.createdorder.paidorder.shippedorder.syncedstock.deductstock.low
CRM & Loyalty Events
member.joinedmember.purchasetier.upgradedpoints.awardedaffiliate.conversionkol.mission.completed
Platform & AI Events
customer.updatedcampaign.performancecampaign.createdcreative.generatedagent.actionagent.report
Current Volume
▸ ~15,000 events/day processed through Redis Streams
▸ ~500 events/hour peak during flash sales
▸ 5 consumer groups processing events in parallel
▸ Dead letter queue for failed event handling
▸ Average latency: <50ms from publish to consumer
▸ ~500 events/hour peak during flash sales
▸ 5 consumer groups processing events in parallel
▸ Dead letter queue for failed event handling
▸ Average latency: <50ms from publish to consumer
Why Cloud Is Required
▸ Current Redis on PaaS has no clustering — single point of failure
▸ Flash sales create 10x traffic spikes that crash current infra
▸ No auto-scaling consumers — backlog during peaks
▸ Need managed Redis with replicas, persistence, monitoring
▸ At SaaS scale: 150K+ events/day requiring cloud streaming
▸ Flash sales create 10x traffic spikes that crash current infra
▸ No auto-scaling consumers — backlog during peaks
▸ Need managed Redis with replicas, persistence, monitoring
▸ At SaaS scale: 150K+ events/day requiring cloud streaming
SuperchargeAgent
The AI Runs Everything
Autonomous AI command center — inference, memory, reasoning, and execution across 9 systems 24/7.
AI
SuperchargeAgent
LLM inference + RAG + embeddings + multi-step planning
LLM Inference · Tool Use
Embeddings · pgvector · RAG
Structured Output · Planning
24 tools · 9 systems · Cronjobs
Embeddings · pgvector · RAG
Structured Output · Planning
24 tools · 9 systems · Cronjobs
↓ calls via API Gateway (JWT auth)
Sales
GET /cdp/reports
Members
GET /crm/stats
Ads
GET /ads/roas
Stock
GET /wms/low
Team
GET /hr/leaves
↓ AI analyzes with RAG memory → takes action
Report
Google Chat · Thai
Reorder
POST /wms/po
Adjust Ads
PUT /ads/budget
Tasks
Task Manager MCP
Patterns
pgvector search
Infrastructure
Cloud Resource Requirements
Per Microservice
Each of our 11 microservices has specific compute, storage, and networking requirements that demand managed cloud infrastructure.
| Service | Stack | Endpoints | Cloud Needs |
|---|---|---|---|
| SuperchargeAgent | LLM + RAG + pgvector | 24 tools | ML inference, GPU, vector DB, always-on |
| API Gateway | Go + Chi + Redis | ~50K/day | Load balancer, auto-scaling, rate limiting |
| Event Bus | Redis Streams | ~15K/day | Managed Redis cluster, persistence, DLQ |
| Creative Hub | Next.js + AI Pipeline | 8 models | GPU inference, object storage, CDN |
| CDP | Go/Gin + PostgreSQL | 50+ | Read replicas, ETL pipeline, batch compute |
| Salepage | Go/Gin + Next.js | 92 | CDN for pages, auto-scale for flash sales |
| KOL + CRM | Go/Gin + PostgreSQL 16 | 260+ | Managed DB (61 tables), event processing |
| Ads Platform | Meta + Google + TikTok | 3 APIs | Scheduled compute, external API calls |
| WMS | Go/Gin + PostgreSQL | 30+ | Real-time stock sync, event-driven alerts |
| HR System | Next.js + Custom ORM | 65 | Managed DB, scheduled jobs |
| Task Manager | Custom API + MCP | 180+ | MCP server hosting, OAuth, webhooks |
540+
Total API Endpoints
100+
Database Tables
5
Separate Databases
11
Containers to Orchestrate
Use Cases — Commerce
Commerce & Order Workflows
How Salepage, CDP, WMS, and CRM work together to unify the commerce experience across every channel.
🔗 Cross-Platform Order Sync & Identity Resolution
Customer buys on Shopee → CDP syncs order within 5 minutes → same customer buys on Lazada next week → CDP resolves identity by phone/email and merges into one profile → CRM shows unified purchase history, lifetime value, and loyalty status across all platforms. No manual data entry.
Shopee API→CDP Sync→Identity Resolution→Lazada API→Unified Profile→CRM History
☁️ Requires: Managed database with read replicas for cross-platform queries, event streaming for real-time sync
📦 COD Order → Delivery → Confirmation Pipeline
Customer picks COD payment → Salepage creates pending order → Event Bus triggers WMS for pick & pack → courier dispatches → delivery confirmed → payment received → CDP records completed transaction → CRM awards loyalty points → SuperchargeAgent includes in daily report.
Salepage→Event Bus→WMS Pick→Courier→Payment→CDP→CRM Points
☁️ Requires: Event streaming (Redis Streams), real-time inventory sync, async job processing
🏪 POS-to-Online Unification
Customer buys at physical store via POS System → Salepage syncs transaction → CDP merges with existing online profile (matched by phone) → CRM awards same loyalty points as online purchase → customer can redeem online. Offline and online become one channel.
POS System→Salepage Sync→CDP Merge→CRM Points→LINE Notification
☁️ Requires: Real-time API integration, identity matching engine, managed database
Use Cases — Marketing
Marketing & Advertising Workflows
How Creative Hub, Ads Platform, CDP, and SuperchargeAgent automate the entire marketing funnel from creation to optimization.
🎨 AI Content Creation → Multi-Platform Distribution
Brand needs product images → Creative Hub's AI Engine generates 20 image variations using our proprietary generation pipeline → NLP module writes Thai captions optimized per platform → Social Scheduler distributes to 12+ platforms (Facebook, Instagram, TikTok, X, LINE) → auto-published on calendar. One input, 12+ outputs.
Product Brief→AI Engine (8 models)→20 Variations→Thai NLP Copy→Social Scheduler→12+ Platforms
☁️ Requires: GPU compute for image generation, LLM inference for copy, object storage + CDN for assets
📊 Cross-Platform Audience Sync & ROAS Optimization
CDP creates "high-value customers" segment (RFM analysis on 100K+ profiles) → syncs to Meta Custom Audience + Google Customer Match + TikTok Audience simultaneously → all 3 platforms target the same people → SuperchargeAgent monitors ROAS hourly → auto-pauses campaigns below 2x ROAS → auto-scales winners above 4x → shifts budget to best performer.
CDP Segments→Meta Audience→Google Match→TikTok Audience→Agent ROAS Check→Auto-Optimize
☁️ Requires: Data pipeline for segmentation, scheduled compute for hourly monitoring, ML inference for decisions
🔄 Creative Fatigue Detection & Auto-Refresh
SuperchargeAgent cronjob monitors CTR declining over 3 consecutive days → detects creative fatigue → calls Creative Hub API to generate fresh assets using AI models → updates Ads campaign creatives across all 3 platforms → notifies marketing team in Google Chat with before/after CTR comparison. Zero human intervention.
CTR Monitoring→Fatigue Detected→Creative Hub AI→New Assets→Ads Updated→Team Notified
☁️ Requires: Always-on inference endpoint, GPU for image gen, scheduled compute for monitoring
Use Cases — KOL & Loyalty
KOL & Loyalty Workflows
How the CRM system drives engagement, retention, and influencer ROI through automated loyalty mechanics and KOL attribution.
🏆 KOL Content → Attribution → Paid Amplification
KOL accepts mission via LINE LIFF mini-app → creates viral TikTok content → CRM logs the submission with affiliate tracking link → Creative Hub stores the asset → SuperchargeAgent identifies it as high-performer (engagement rate) → Ads Platform boosts as paid Spark Ad on TikTok → every conversion tracked back to the specific KOL via ?ref= parameter → commission auto-calculated and paid.
LINE LIFF→KOL Content→CRM Track→Creative Hub→Spark Ad→Attribution→Commission
☁️ Requires: Real-time event processing, managed DB for attribution, API integrations with TikTok Ads
⭐ Loyalty Tier Engine & Cross-Platform Rewards
Customer accumulates 500 points from purchases across Shopee, Lazada, and direct website (all unified via CDP) → CRM auto-evaluates tier rules → upgrades to Gold tier → Event Bus triggers LINE notification with congratulations + exclusive 15% discount code → code works on Salepage only (drives direct purchase) → next purchase earns 1.5x points (Gold multiplier). Self-reinforcing retention loop.
Multi-Platform Purchases→CDP Unify→CRM Points→Tier Upgrade→LINE Notification→Direct Purchase
☁️ Requires: Event-driven architecture, managed DB for tier calculations, real-time notifications
🎯 Segment-Based Exclusive Drops
CRM identifies VIP tier members (top 100 spenders) → CDP enriches with cross-platform purchase data and browsing behavior → Salepage generates exclusive landing page with unique access code → CRM sends personalized LINE message with early access link 24 hours before public launch → tracks conversion rate per segment → feeds results to SuperchargeAgent for future campaign optimization.
CRM VIP List→CDP Enrich→Exclusive Page→LINE Early Access→Track & Optimize
☁️ Requires: Data pipeline for segmentation, dynamic page generation, personalized messaging at scale
Use Cases — AI Operations
SuperchargeAgent Autonomous Workflows
How SuperchargeAgent manages daily operations without human intervention — cronjobs, cross-system API calls, reasoning, and action.
📊 Daily Business Intelligence Report (Autonomous Cronjob)
Every day at 18:00, SuperchargeAgent automatically: calls CDP API for daily sales + revenue → calls CRM for new members + tier changes → calls Ads Platform for ROAS across 3 channels → calls WMS for low-stock alerts → calls HR for team leaves → synthesizes everything into a Thai-language summary with action items → sends to Google Chat → creates tasks in Task Manager for anything requiring follow-up. No human triggers this.
Cronjob 18:00→CDP API→CRM API→Ads API→WMS API→HR API→Thai Report→Chat + Tasks
☁️ Requires: Always-on compute for cronjobs, LLM inference for summarization, scheduled job orchestration
📦 Autonomous Stock Replenishment Pipeline
WMS detects stock below 50 units → Event Bus emits stock.low event → SuperchargeAgent cronjob picks it up → queries CDP API for 30-day sales velocity of that SKU → calculates optimal reorder quantity → auto-creates Purchase Order in WMS → assigns review task to procurement in Task Manager → sends alert to team via Google Chat with full analysis (current stock, burn rate, recommended order, estimated delivery).
WMS Low Stock→Event Bus→Agent Picks Up→CDP Velocity→Calculate Qty→Create PO→Assign Task→Chat Alert
☁️ Requires: Event-driven processing, ML inference for demand forecasting, managed DB for inventory data
🚀 New Product Launch — One Command, Full Orchestration
Tell SuperchargeAgent "launch product X" → it calls Creative Hub to generate product images + ad copy via 8 AI models → creates Ads campaigns on Meta, Google, TikTok simultaneously → builds Salepage landing page with optimized layout → creates KOL missions in CRM with affiliate links → sets up CDP tracking for attribution → schedules daily performance monitoring → reports results. One instruction activates the entire ecosystem.
"Launch X"→Creative Hub→Ads (3 platforms)→Salepage→CRM Missions→CDP Track→Daily Monitor
☁️ Requires: GPU compute, LLM inference, multi-API orchestration, CDN for landing pages, always-on agent
AI Agent Scenarios
SuperchargeAgent:
Autonomous Operations
Real scenarios where the AI orchestrates multiple systems — no human trigger, no manual work. Each requires cloud-grade compute.
🧠 Competitor Price War — Auto-Response in 3 Minutes
SuperchargeAgent detects competitor price drop via scheduled monitoring → queries CDP for affected product sales data → calculates margin threshold → auto-updates Salepage pricing → adjusts Ads budget to push the product harder → creates flash-sale KOL mission in CRM → notifies marketing team with full analysis. All within 3 minutes.
☁️ LLM inference + RAG retrieval + 6 concurrent API calls + scheduled compute
A human team takes 2-3 days. The agent does it in 3 minutes.
📊 End-of-Month Revenue Gap Closer
On the 25th, agent calculates revenue vs monthly target from CDP → identifies gap → finds top-performing products → creates urgency campaigns on 3 ad platforms → activates double-points in CRM → generates AI creatives → sends KOL missions → reports projected close rate to executives.
☁️ Data pipeline analytics + GPU for creative gen + multi-system orchestration
AI becomes your revenue strategist — sees the gap, plans, and executes automatically.
🎯 Customer Acquisition Autopilot — 24/7
Agent analyzes CDP: top 20% highest-LTV customers → extracts traits (age, location, patterns) → creates lookalike audiences on Meta/Google/TikTok → generates tailored creatives per platform → launches campaigns → monitors ROAS hourly → kills underperformers, scales winners → converts buyers into CRM loyalty → feeds results back. Continuously, 24/7.
☁️ Always-on inference + embedding similarity search + batch analytics + GPU creative gen
Reverse-engineers your best customers and finds more of them — non-stop.
AI Agent Scenarios
SuperchargeAgent:
More Autonomous Workflows
Every scenario requires always-on cloud infrastructure — LLM inference, vector database, scheduled compute, and multi-API orchestration.
⚡ Crisis Management — Viral Complaint in 60 Seconds
Event Bus detects spike in negative mentions → Agent immediately pauses all active Ads → pulls customer's full history from CDP → checks order status from Salepage → checks WMS batch → drafts personalized Thai apology → creates urgent task for customer service → sends executive briefing with full timeline. All before the team sees the complaint.
☁️ Event-driven triggers + real-time LLM inference + 7 concurrent system calls + NLP generation
Full crisis response in under 60 seconds. Brand protected before damage spreads.
🔄 Dead Stock Liquidation Pipeline
Weekly cronjob analyzes WMS inventory aging → identifies 60+ day items → cross-references CDP for matching customer segments → creates clearance landing page on Salepage → generates discount creatives via AI → launches targeted ads → creates KOL flash-sale missions → tracks results daily → adjusts strategy → stops campaign when stock moves.
☁️ Batch analytics + GPU creative gen + dynamic page gen + scheduled monitoring
Agent finds dead inventory and runs full liquidation strategy end-to-end.
🌙 After-Hours Autonomous Operations
After 18:00 when team goes home → Agent monitors Ads ROAS every hour → pauses overspending campaigns → detects low-stock items from WMS → creates morning PO drafts → processes incoming marketplace orders via CDP → awards CRM points → logs everything in Task Manager → prepares morning briefing for 08:00. The business runs 24/7.
☁️ 24/7 always-on inference endpoint + scheduled cron + pgvector RAG + multi-API orchestration
Business never sleeps. Agent runs operations while the team rests.
Growth
Scale & Growth Metrics
Current operational metrics and 12-month targets driving cloud infrastructure requirements.
Current
~50K
API calls / day
~15K
Events processed / day
100K+
Customer profiles unified
~2K
AI inference calls / day
6
Concurrent platform syncs
12-Month Target
500K+
API calls / day
150K+
Events processed / day
1M+
Customer profiles unified
25K+
AI inference calls / day (multi-agent)
12+
Concurrent platform syncs
Architecture designed for 10x growth without re-platforming. Cloud infrastructure is required to hit these targets.
Why Cloud?
We've Outgrown PaaS.
Cloud Is Critical for Our Next Phase.
Currently on Railway + Vercel — great for prototyping, but hitting hard limits that block AI ambitions and SaaS scaling.
Current Limitations
- ✗ No auto-scaling — single instance per service
- ✗ No ML training infra — dependent on third-party APIs
- ✗ No staging / CI/CD pipeline
- ✗ Database without replicas or automated backups
- ✗ No CDN for sale pages and media
What Cloud Unlocks
- ✓ Auto-scaling for flash sale traffic (10x spikes)
- ✓ ML training & fine-tuning on own business data
- ✓ Proper CI/CD with staging environments
- ✓ Managed DB with replicas, backups, monitoring
- ✓ Global CDN for SEA-wide SaaS deployment
🧠 AI/ML Training & Inference
Fine-tune LLMs on Thai e-commerce data. Train recommendation models. Run embeddings at scale for RAG pipeline.
Currently paying $400+/month for third-party AI APIs with limited capabilities
📊 Data Processing & Analytics
Real-time event processing for 15K+ events/day. Customer segmentation. Cross-platform analytics.
Need to process 100K+ profiles for intelligent segmentation
⚡ Backend & API Scaling
11 microservices need container orchestration and auto-scaling. 50K API calls/day growing 30% MoM.
Current PaaS has no auto-scaling — single instance bottleneck
🗄 Storage & CDN
Product images, AI-generated content, video assets, DB backups. Growing 50GB+/month.
Need CDN for SEA-wide low-latency delivery
Use of Credits
How We'll Use the Credits
12-month allocation plan. Every dollar mapped to specific cloud workloads with measurable outcomes.
AI/ML ~32%
Compute ~28%
Database ~18%
Storage ~10%
DevOps & Analytics ~12%
~32%
AI/ML Training & Inference
The core of the platform — SuperchargeAgent evolves from API-wrapper to full AI system.
▸ Fine-tune LLMs on Thai e-commerce data
▸ LLM inference endpoints (24/7 SuperchargeAgent)
▸ Embedding generation & vector DB compute
▸ Image/video generation GPU inference
▸ LLM inference endpoints (24/7 SuperchargeAgent)
▸ Embedding generation & vector DB compute
▸ Image/video generation GPU inference
~28%
Compute & Containers
11 microservices migrated to auto-scaling containers. From single-instance to production-grade.
▸ Container orchestration for all services · API Gateway + load balancer · Background workers & cron jobs
~18%
Database & Cache
Managed PostgreSQL with replicas + Redis cache layer.
▸ 3 managed DB instances · Redis cache · Backups, snapshots, staging DBs
~10%
Storage & CDN
▸ Object storage for media · CDN for SEA-wide delivery · AI-generated content (50GB+/month)
~12%
DevOps, Monitoring & Analytics
▸ CI/CD pipeline · Container registry · Monitoring & logging · Event processing · Analytics compute
We will be a paying cloud customer beyond the credit period. SaaS revenue from onboarded brands will cover cloud costs.
Market Position
Competitive Landscape
No existing solution in SEA combines commerce, marketing, loyalty, and AI operations in one platform. We fill the gap.
| Capability | Moonshot | Shopify | Storehub | HubSpot | Manual |
|---|---|---|---|---|---|
| Unified CDP (4 marketplaces) | ✓ Built-in | ✗ | ✗ | Partial | ✗ |
| AI Content Generation | ✓ 8 models | Basic | ✗ | Basic | ✗ |
| Autonomous AI Agent | ✓ 24 tools | ✗ | ✗ | ✗ | ✗ |
| Multi-Platform Ad Automation | ✓ 3 platforms | ✗ | ✗ | Partial | ✗ |
| KOL Management + Attribution | ✓ Full stack | ✗ | ✗ | ✗ | ✗ |
| Loyalty + Points + Tiers | ✓ Built-in | Plugin | Basic | ✗ | ✗ |
| COD + Thai Marketplace | ✓ Native | Partial | ✓ | ✗ | ✓ |
| WMS + Auto-Reorder | ✓ AI-powered | Basic | Basic | ✗ | ✗ |
Key insight: Competitors solve 1-2 problems each. Brands need 5+ tools stitched together with manual integrations. We built the full stack as one platform — with AI at the center, not as an add-on. No competitor in SEA offers this.
The Team
Small Team, Big Output
Built the entire 11-service platform in-house. Deep domain expertise in Thai e-commerce, AI systems, and scalable infrastructure.
Founder & CEO
Business & Product Vision
Running profitable e-commerce brands in Thailand. Deep understanding of COD logistics, marketplace dynamics, and local consumer behavior.
CTO & Lead Engineer
Full-Stack Architecture
Designed the 11-microservice ecosystem. Go, TypeScript, PostgreSQL, Redis, AI/ML integration. Built SuperchargeAgent.
AI & ML
SuperchargeAgent Development
LLM fine-tuning, RAG pipeline, embedding systems, tool orchestration. Built the autonomous agent with 24 tools.
Operations
E-Commerce & Growth
Marketplace management, KOL relationships, COD logistics, customer service. Bridge between tech and commerce.
Why We'll Succeed
Already profitable — Real revenue from e-commerce, not pre-revenue
Built it ourselves — 11 systems proves execution ability
Deep domain expertise — Thai e-commerce requires local knowledge
Security & Compliance
Data Protection &
Infrastructure Security
Built with enterprise-grade security from day one — ready for multi-tenant SaaS and PDPA compliance.
🛡
PDPA Compliance
Thailand Personal Data Protection Act (พ.ร.บ.คุ้มครองข้อมูลส่วนบุคคล)
Consent Management
Explicit consent collection at every data touchpoint. Customers control their data preferences via LINE LIFF and Salepage. Opt-out mechanisms across all channels.
Data Subject Rights
Right to access, correct, delete, and port personal data. CDP provides unified data export per customer. CRM handles deletion requests across all linked systems.
Data Retention Policy
Defined retention periods per data category. Automated purge for expired records. Audit logs for all data access and modifications tracked in PostgreSQL.
🔐
Data Isolation & Encryption
Database-level isolation — Each microservice has its own PostgreSQL database. No cross-service direct DB access. All communication via API Gateway.
Encryption at rest — All PostgreSQL databases use AES-256 encryption. Redis cache encrypted. Object storage encrypted.
Encryption in transit — TLS 1.3 on all endpoints. Internal service-to-service communication via encrypted channels through API Gateway.
SaaS multi-tenant isolation — Row-level security + tenant ID on every table. No data leakage between brands.
🔑
Access Control & Auth
JWT authentication — Stateless token-based auth across all services. Short-lived access tokens + refresh tokens. Gateway validates every request.
Role-based access — Admin, Manager, Staff, KOL, Customer roles with granular permissions. Different API scopes per role.
API rate limiting — Per-user and per-service rate limits enforced at Gateway level. Circuit breaker prevents cascade failures.
Secrets management — API keys, DB credentials, and third-party tokens stored in managed secrets service. Never in code or environment variables.
🏗
Infrastructure Security
Network Isolation
VPC with private subnets. Services not exposed to public internet. Only Gateway is public-facing.
Automated Backups
Daily automated DB snapshots. Point-in-time recovery. Cross-region backup replication.
Audit Logging
All API calls logged with user, timestamp, action, and IP. Immutable audit trail for compliance.
DDoS Protection
Cloud-native DDoS mitigation. WAF rules on Gateway. Rate limiting per IP and per user.
SaaS Vision
From Internal Platform
to SEA Commerce SaaS
We built this platform for our own brands first. Now we're productizing it for every e-commerce brand in Southeast Asia.
Today — Internal Platform
- ✦ Running our own e-commerce brands (La-ong Herbal, etc.)
- ✦ 11 microservices, single-tenant architecture
- ✦ Proven with real revenue, real orders, real customers
- ✦ All workflows battle-tested on live traffic
- ✦ Hosted on PaaS (Railway + Vercel) — works but can't scale
Future — Multi-Tenant SaaS
- ✦ Any SEA brand can onboard in minutes
- ✦ Multi-tenant: isolated data, shared infrastructure
- ✦ Usage-based pricing (API calls, AI inference, storage)
- ✦ Self-serve dashboard for each brand
- ✦ Cloud-native: auto-scaling, managed DB, CDN, ML infra
SaaS Modules — What Brands Get
🎨
AI Creative Studio
Image, video, copy generation
📊
Unified Analytics
CDP + cross-platform data
📢
Ad Automation
Meta + Google + TikTok
⭐
CRM & Loyalty
Points, tiers, KOL missions
🛒
Commerce Engine
Salepage, COD, marketplace
📦
Warehouse
Inventory, PO, auto-reorder
🤖
AI Agent
SuperchargeAgent per brand
🔗
Integrations
Lazada, Shopee, TikTok, LINE
Target: 500K+ online sellers in Thailand alone. Starting with mid-tier brands doing 100-10,000 orders/month. Cloud infrastructure is the key blocker — we need auto-scaling, multi-tenancy, and managed ML to go from 1 brand to 100.
SaaS Economics
Path to Self-Sustaining
Cloud Revenue
How the SaaS model generates revenue that covers and exceeds cloud infrastructure costs.
Revenue Model — Usage-Based Pricing
Starter
▸ Up to 1,000 orders/month
▸ 3 marketplace integrations
▸ Basic AI creative (50 images)
▸ CRM + loyalty
▸ Email support
▸ 3 marketplace integrations
▸ Basic AI creative (50 images)
▸ CRM + loyalty
▸ Email support
POPULAR
Growth
▸ Up to 10,000 orders/month
▸ All marketplace + POS sync
▸ Full AI creative suite
▸ Ads automation (3 platforms)
▸ SuperchargeAgent (read-only)
▸ KOL management
▸ All marketplace + POS sync
▸ Full AI creative suite
▸ Ads automation (3 platforms)
▸ SuperchargeAgent (read-only)
▸ KOL management
Enterprise
▸ Unlimited orders
▸ Full autonomous AI agent
▸ Custom ML model training
▸ White-label option
▸ Dedicated infrastructure
▸ Priority support + SLA
▸ Full autonomous AI agent
▸ Custom ML model training
▸ White-label option
▸ Dedicated infrastructure
▸ Priority support + SLA
Growth Projection
Month 1-3: Internal use + beta1 brand
Month 4-6: Closed beta launch3-5 brands
Month 7-9: Public launch Thailand10-20 brands
Month 10-12: Scale + SEA expansion50+ brands
Cloud Cost Coverage
SaaS revenue covers infra byMonth 8-10
Each brand adds incremental revenue with~70% margin
Multi-tenant = shared infra costEconomies of scale
Long-term cloud customer statusPaying customer
Why cloud credits accelerate this: Without cloud infrastructure, we can't build multi-tenancy, can't auto-scale for multiple brands, and can't offer managed AI services. Credits let us go from 1 brand to 50+ in 12 months instead of 24+.
Growth Strategy
AI Studio — Professional
Creative Agency Powered by AI
We built Creative Hub for our own brands. Now we're evolving it into a full AI-powered agency service — producing commercial-grade ads for external brands at scale.
Evolution Path
Creative Hub
Internal tool for our brands
→
AI Studio
Professional agency for clients
→
Revenue + Cloud Usage
New income stream + maximize credits
What We Offer Clients
🎬 Commercial Ad Production
Full ad campaigns — from concept to finished assets. Product photography, lifestyle scenes, video ads (15s/30s/60s), Thai & English ad copy. Delivered same-day, not weeks.
Client pays per campaign · We produce using AI infrastructure
📢 Multi-Platform Ad Management
We don't just create ads — we run them. Manage Meta, Google, TikTok campaigns. AI monitors ROAS hourly, auto-optimizes budgets, refreshes creatives when performance drops.
Monthly retainer · AI does 90% of the work
📊 Performance Creative Strategy
Data-driven creative decisions. AI analyzes what works per audience, platform, and season — then produces winning variations. Not guessing, but iterating based on real ROAS data.
Strategy fee · Backed by CDP + SuperchargeAgent analytics
3-5
Client Brands (Year 1)
20-30
Campaigns / Month
80%+
Margin (AI vs Human Team)
2-3x
Cloud Credit Utilization
Why this works: We already built the AI infrastructure for ourselves. Serving external clients adds revenue with near-zero additional dev cost — while driving 2-3x more cloud compute consumption through GPU inference, LLM calls, and storage.
AI Studio — Cloud Impact
AI Studio Drives Maximum
Cloud Credit Utilization
Every client campaign we produce burns through real cloud compute — GPU for images/video, LLM for copy, storage for assets. This is how credits get used at full capacity.
Ad Image Production
Per campaign: Client sends product → AI generates studio-quality photos, lifestyle scenes, seasonal themes, platform-specific sizes → 20-50 variations per product per campaign
GPU inference per image · Diffusion models · 200+ images/day across clients · Object storage for high-res assets
Video Ad Rendering
Per campaign: Product stills → AI generates motion, transitions, text overlays → renders 15s/30s/60s variants for TikTok, Reels, YouTube → Thai/English subtitles → exports per platform spec
Heavy GPU for video gen · ~2-5 min per video · Async job queue · CDN delivery to clients
Copy & Strategy
Per campaign: LLM analyzes client's brand, audience, competitors → generates Thai/English ad copy per platform → A/B variants → hooks, CTAs, hashtags → performance-optimized based on historical data
LLM inference (Thai NLP) · RAG for brand context · ~100 copy variants per campaign
Campaign Optimization
Ongoing: SuperchargeAgent monitors client campaigns 24/7 → auto-pauses low ROAS → scales winners → detects creative fatigue → triggers new asset generation → reports to client
Always-on inference · Scheduled cron · Multi-API orchestration (Meta/Google/TikTok)
Cloud Credit Impact — AI Studio vs Platform Only
Platform Only (Current)
~2K AI calls/day · moderate GPU usage
Platform + AI Studio (With Clients)
~8K+ AI calls/day · heavy GPU for image/video gen · 2-3x credit usage
AI Studio = Revenue + Credit Utilization: Client campaigns generate agency income while consuming 2-3x more cloud compute. The same infrastructure we built for our brands now serves paying clients — zero additional engineering, maximum cloud credit ROI.
Roadmap
6-Month Cloud Migration Plan
How cloud credits accelerate each phase — from migration to AI infrastructure to SaaS launch.
M1-2
01
Foundation — Cloud Migration
Migrate all 11 services to cloud containers. Managed PostgreSQL with backups/replicas. CI/CD pipeline with staging. Monitoring and logging.
Container orchestration · Managed DB · CI/CD · Monitoring
✓ All services on cloud with zero-downtime migration
M3-4
02
AI Infrastructure — SuperchargeAgent Evolution
Deploy on cloud-native ML infra. Fine-tune LLMs on Thai data. Vector DB for RAG at scale. Data pipeline for real-time analytics. Recommendation model on 100K+ profiles.
ML Training · Inference Endpoints · Vector DB · Data Pipeline
✓ AI costs drop 60% while capability increases 10x
M5-6
03
Scale & SaaS Launch
Multi-tenant architecture. Auto-scaling for flash sale traffic. Global CDN. Open beta for first 5 external brands. Usage-based billing.
Auto-scaling · CDN · Multi-tenant · Billing infra
✓ Platform serving 3+ brands with SaaS pricing validated
Beyond 6 Months
M7+: Paying cloud customer
M12: 20+ brands on SaaS
Break-even: Cloud costs covered by SaaS revenue
Ecosystem Summary
11
Microservices
540+
API Endpoints
24
AI Agent Tools
~50K
API Calls/Day
100K+
Profiles Unified
18
Event Streams
8
AI/ML Models
Technology Stack
Backend: Go/Gin, Chi Router
Frontend: Next.js 14, React
Database: PostgreSQL 15/16
Cache: Redis Streams + Cache
Vector DB: pgvector
ORM: Custom built
Auth: JWT + OAuth 2.0
Protocol: REST + MCP
AI Capabilities
LLM: Custom Tool Use Agent
RAG: pgvector retrieval
Image: Multi-model pipeline
Video: AI video generation
NLP: Thai language
Embeddings: Vector search
Automation: 24/7 cronjobs
Output: Structured JSON
Platform Systems & Integrations
SuperchargeAgent
API Gateway
Event Bus
Creative Hub
CDP
Salepage
KOL + CRM
Ads Platform
HR
WMS
Task Manager
Lazada
Shopee
TikTok Shop
LINE LIFF
Meta Ads API
Google Ads API
TikTok Ads API
Payment Gateway
POS System
Facebook CAPI
GA4
Social Distribution
AI Generation
Google Chat
~32%
AI/ML Inference
~28%
Compute
~18%
Database
~10%
Storage & CDN
~12%
DevOps
Moonshot Labs · Bangkok, Thailandwww.moonshotstudio.ai · March 2026