Rattanun Rodchawang

Siam Orchard Group Co., Ltd.

• Led feature development end-to-end: gathered requirements from business stakeholders and external partners, wrote technical specifications, and deployed to production
• Built the backend for a car-rental booking and billing platform (NestJS, TypeScript, MongoDB) with 100+ REST endpoints, cutting manual document work by 50%
• Integrated the INET payment gateway (QR and card), using an IP whitelist and idempotent callback handling, plus a daily reconciliation job that checks every payment against the gateway and fixes stuck transactions automatically
• Automated the full accounting document flow (quotation, deposit, invoice, receipt, debit/credit notes, vouchers): PDF generation, email delivery, running numbers, and status cascading from bookings to related documents
• Built a customer data platform that merges records across bookings and documents (normalized phone/email/tax ID/name matching), with confidence-ranked duplicate detection and an idempotent daily sync
• Integrated LINE Messaging, Google Sheets and Mailchimp, and added Sentry monitoring and API audit logging that masks sensitive data
• Built a reusable Atomic Design component library, improving reusability across 3+ internal projects

Diversition Co., Ltd.

• Built the full stack of a real-time collaborative whiteboard: React frontend, Node.js/Express + MySQL backend, with Socket.io-based synchronization for simultaneous multi-user drawing
• Designed responsive, mobile-first interfaces with a focus on accessibility and cross-device compatibility

AI assistant + CRM on LINE for vet clinics, grooming and boarding shops, running as a public demo. Customers ask, book, pay a deposit and cancel in the chat; the bot answers from the shop's own data and hands over to staff when a decision should not be left to AI.

petcare.rattanun.dev ↗

• Designed and built a multi-tenant SaaS that gives vet clinics, grooming and boarding shops an AI assistant on LINE plus the CRM behind it (Next.js, NestJS, PostgreSQL + pgvector, Drizzle, Redis/BullMQ, OpenRouter, LINE Messaging API + LIFF)
• Enforced tenant isolation with PostgreSQL row-level security set per transaction and an RLS test for every tenant table; every service validates input, checks permissions and writes an audit record in one transaction
• Built a guarded LLM pipeline: tool calling for prices and bookings, prices computed in code (never by the model), RAG over each shop's knowledge base, and seven output guards; emergencies hand the chat to staff and alert the team
• Measured AI quality with an eval suite and an untouched hold-out set: 94% across 225 scored runs, hold-out raised from 71% to 84%; compared models on quality, latency and safety, running at about $1.2 per 1,000 bot turns
• Prevented double bookings with a PostgreSQL exclusion constraint, tested by racing real transactions; a BullMQ worker runs debounced bot turns, background memory extraction, reminders, follow-ups and per-contact AI spend caps
• Load-tested the webhook at 100 msg/s (p95 10.7 ms, 0 errors) and cut a hot query from 81 ms to 0.17 ms after finding that row-level security kept PostgreSQL from using the index
• Shipped with CI/CD: ~2,200 tests on real PostgreSQL, Docker images to GHCR, a 32-second SSH deploy behind Caddy that backs up before migrating, and nightly backups to Cloudflare R2 under a 30-day object lock