AI-powered order-to-dispatch system for a D2C skincare brand
A fast-growing skincare brand selling 200+ orders a day was reconciling Shopify, WhatsApp, and Excel sheets manually — and customers were constantly asking for order updates.
- Reduction in manual order entry Reduction in manual order entry: 85%
- Fewer WISMO support tickets Fewer WISMO support tickets: 60%
- Faster order-to-dispatch time Faster order-to-dispatch time: 2.3×
- Recovered from prevented stockouts/month Recovered from prevented stockouts/month: ₹2.1L+
- Next.js
- Supabase
- Shopify API
- n8n
- Claude
- WhatsApp Cloud API
- Client industry
- D2C consumer skincare
- Timeline
- 10 weeks · Fixed scope · fixed price
- Technology stack
- Next.js, Supabase, Shopify API, n8n, Claude, WhatsApp Cloud API
Orders were arriving from five different channels, but the back-office still ran on spreadsheets. Every morning one team member spent 3+ hours copying orders, checking stock, and updating warehouse status. Inventory was always out of sync, bestsellers went out of stock without warning, and customer support was flooded with 'Where is my order?' messages.
- Consolidate orders from all five channels into one operational dashboard.
- Cut daily manual order-entry time from 3+ hours to under 30 minutes.
- Prevent bestseller stockouts with automated reorder alerts.
- Reduce inbound 'Where is my order?' support tickets by more than half.
- Week 1 · Discovery
Mapped every channel, message template, warehouse handoff, and SKU. Documented the current 3-hour morning workflow end-to-end before touching code.
- Weeks 2–3 · Data model
Built a canonical order + inventory schema in Supabase; migrated legacy Shopify SKUs into a mapping table so the operations team never had to relearn part numbers.
- Weeks 4–6 · AI intake
Trained the WhatsApp and Instagram DM parser against 30 days of real customer messages; every extraction had to pass a human review before writing to Shopify.
- Weeks 7–8 · Inventory sync
Wired real-time stock ledger with reorder-point alerts pushed to the founder's WhatsApp.
- Weeks 9–10 · Parallel run + cutover
Ran the new system alongside spreadsheets for two weeks, compared numbers daily, then cut over.
- Instagram Graph API rate limits forced a batched retry queue for high-DM days around launches.
- Legacy Shopify SKUs lacked unique barcodes, so a manual mapping pass was needed before automation could be trusted.
- Warehouse staff on the field had unreliable connectivity — the fulfilment UI needed offline-tolerant states.
- Unified order management dashboard. WhatsApp and Instagram DMs are captured by AI, validated against live stock, and pushed directly into Shopify and the warehouse sheet — no manual copy-paste.
- Automated inventory and reorders. Raw-material and finished-goods levels are tracked in real time. Alerts fire when bestsellers hit the reorder point, and purchase suggestions are generated automatically.
- WhatsApp AI agent for fulfilment. Customers receive proactive order confirmations, dispatch alerts, delivery tracking, and instant return or exchange initiation via WhatsApp.
Not published. This engagement is under NDA. We can arrange a reference call with the client at proposal stage — we do not publish paraphrased or synthesized quotes.
- Start with the messiest channel — WhatsApp — first; it exposes 80% of the edge cases the tidy channels hide.
- Reorder alerts only work when the operations team already trusts the stock number, so data cleanup pays for itself.
- Hard guardrails on AI actions (never auto-cancel, never auto-refund) matter more than automation speed.
Illustrative workflow example. Named client case studies are shared on request where permitted.