E-commerce Order Automation
End-to-end automation: order intake → inventory → fulfillment → customer notifications.
Client
Multi-brand fashion & lifestyle retail platform
Industry
E-commerce
Region
Asia-Pacific (India + Southeast Asia)
Duration
8 weeks initial build + phased rollout
A multi-brand fashion retailer was burning operations hours and accuracy points during festival peaks. We built an end-to-end orchestration layer connecting storefront, ERP, WMS, and notification systems - turning a 4-system manual handoff into one automated flow. Result: 80% of manual processing eliminated, order accuracy at 95% during 5× peak volume, and an 18% lift in repeat purchases driven by faster, cleaner fulfillment.
Headline results
The client runs storefronts for 14 brands across fashion, beauty, and home - processing roughly 18,000 orders a week at baseline and spiking to over 90,000 during major sale events. Pre-automation, every order touched four systems with manual handoffs: storefront → ERP → WMS → customer notifications. Peak processing time was 30-60 minutes per order, error rates crept past 8% during sales, and the operations team relied on temp hires that did not pay back.
What we built and shipped
End-to-end orchestration layer
Custom orchestration connecting storefront (Shopify Plus), ERP, WMS, and notification systems - every order flows automatically from cart to dispatch with full state tracking.
- Event-driven architecture with at-least-once delivery guarantees
- Idempotent operations across all systems to handle replays safely
- Real-time order status visible to ops and customers
Real-time inventory sync & smart fulfillment
Inventory across multiple warehouses synced in real time, with intelligent fulfillment routing that picks the lowest-cost, fastest-delivery warehouse per order based on location and stock health.
- Sub-minute inventory updates across warehouses
- ML-based fulfillment routing considering RTO history per pincode
- Auto-substitution rules for out-of-stock SKUs in approved categories
AI exception handling
An AI layer reads exception emails from carriers, suppliers, and customers, classifies them, and either auto-resolves or routes to a human with full context - turning a 200-email-a-day pain point into a 20-email queue.
- Classification across 14 exception categories with 95%+ accuracy
- Auto-resolution of address corrections, partial shipments, and reschedules
- Human escalation with pre-drafted resolution suggestions
WhatsApp + email customer notifications
Order confirmations, shipping updates, delivery confirmations, and proactive WISMO replies - all triggered by events from the operations layer with no manual sends and personalised per brand voice.
- Multi-language (English, Hindi, Bahasa) templated on WhatsApp Business API
- Per-brand sender identity and voice
- Proactive WISMO outreach for delays before customers ask
How it actually works
Event bus
Every state change in the order lifecycle becomes an event on a durable queue. Downstream services subscribe - no system holds the source of truth alone, and replaying a failed event is one click.
Orchestration layer
Stateful workflow service that knows the canonical order state and triggers downstream steps in the right sequence - handles retries, timeouts, and human escalations cleanly.
Inventory sync service
Sub-minute polling and webhook integration with the WMS across warehouses, with a reconciliation job every 15 minutes that catches drift before it causes a stockout.
Notification engine
Per-brand templates, multi-language support, and channel preference handling on top of WhatsApp Business API and SendGrid.
Phased delivery timeline
Discovery & integration mapping
Mapped every API, webhook, and manual handoff across the 4 systems. Built integration spec, identified breaking edge cases from the last 6 months of order data.
Core orchestration build
Shipped event bus + orchestration layer + inventory sync. Ran in parallel with manual flow for 2 weeks, reconciling daily - caught 12 edge cases the spec missed.
AI exception handling + notifications
Built classifier + auto-resolution for the top 8 exception categories. Shipped WhatsApp + email notification engine, brand-tuned.
Phased rollout & festival hardening
Rolled brand by brand from 1 to 14, hitting first festival peak in week 11 - 5× normal volume handled with zero operations team escalation past tier-1.
Before vs after
Same business, same team - measurably different operating model after the engagement.
- Order processing time30-60 min / order
- Manual ops hours per 1,000 orders85 hours
- Order accuracy during sales92%
- Peak volume capacity (vs base)1.5×
- Inventory drift incidents per month20-30
- Customer WISMO tickets per 1,000 orders180
- Order processing timeUnder 2 minutes
- Manual ops hours per 1,000 orders12 hours
- Order accuracy during sales95%+
- Peak volume capacity (vs base)5× with same team
- Inventory drift incidents per monthUnder 3
- Customer WISMO tickets per 1,000 orders32
What changed and by how much
Operational and revenue metrics tracked from go-live, measured against the pre-engagement baseline.
Composition of impact
Approximate breakdown of how this engagement contributed to the business outcome - the headline metric is a roll-up of these levers.
- Manual ops cost-out34%
- Peak volume handling without hires24%
- WISMO deflection & CX lift18%
- Inventory accuracy & fewer stockouts14%
- Brand-tuned notification revenue10%
What we built it with
Storefront + commerce
- Shopify Plus (14 brands)
- Razorpay + Shopify Payments
- Custom checkout extensions
Orchestration
- Custom Node.js services
- AWS Step Functions
- Redis for queues + caching
AI & data
- OpenAI GPT-4 for exception classification
- Custom rules engine
- PostgreSQL + ClickHouse for analytics
Notifications + monitoring
- WhatsApp Business API (Meta)
- SendGrid for transactional email
- Sentry + custom dashboards
What we de-risked along the way
Catastrophic failure during festival peak
Mitigation: Pre-festival load test at 8× expected peak, with automated rate limiting and a manual fail-safe kill-switch to flip back to legacy flow within 60 seconds.
Inventory double-allocation across warehouses
Mitigation: Sub-minute sync + reconciliation job every 15 minutes + advisory locks on hot SKUs during sale events. Drift caught in seconds, not days.
Notification template rejection by Meta
Mitigation: All templates pre-approved with backups; per-brand sender identities rotated to spread quality scores; weekly compliance audits.
What we'd carry into the next build
Run in parallel before cutting over
Two weeks of running new and old flows side-by-side caught 12 edge cases that would have caused customer-visible outages in production. The cost of slow rollout was zero; the cost of fast cutover would have been brand damage.
Event-driven beats request-response for ops
Replacing API request chains with a durable event bus made every step replayable. A failed shipping update is just a retry, not a manual fix in three systems.
AI classification beats deterministic rules for exceptions
We tried rules first. After 50 patterns, we still had 35% of exceptions falling to manual. Switching to LLM classification got us to 95%+ accuracy with a fraction of the maintenance.
Brand voice in notifications drives repeat purchase
Generic shipping updates do nothing. Brand-tuned WhatsApp updates measurably lifted repeat purchase - customers remember a brand that talks to them like a brand, not a logistics platform.
ROI & payback
Investment
Mid-six-figures one-time build + low-five-figures monthly run cost (infra + messaging + model usage)
Payback period
Inside 5 months - primarily from manual ops hours cut and peak-volume staff savings
Year-1 ROI
Estimated 6-8× ROI on the build cost in year one, with the bigger compounding from repeat-purchase lift
“We were drowning at 10× normal volume during sales. Now we run festival peaks on autopilot with the same team - and our return-customer rate is up because nothing slips. The AI exception handler alone gave us our operations weekends back.”
Questions about this engagement
How did the project handle 5× festival volume without breaking?+
Three things: (1) load testing at 8× expected peak before go-live, (2) automated rate limiting on the orchestration layer with a manual kill-switch, and (3) phased rollout across 14 brands so any issue was contained to one brand at a time. First festival peak ran with zero ops escalation past tier-1.
What was the biggest source of the 80% manual work reduction?+
Order-state transitions that previously required a human to copy data between systems. Once events flowed through the orchestration layer end-to-end, the human role moved to exception handling - which the AI layer then deflected 90% of.
Did automation hurt the customer experience?+
No - it improved it. Order confirmation time dropped from hours to seconds, WhatsApp updates landed automatically, and WISMO tickets dropped 82%. CSAT on notification-driven interactions rose by 14 points.
How is RTO reduction handled?+
The fulfillment routing service considers pincode-level RTO history when choosing a warehouse - and on COD orders flags high-risk pincodes for a WhatsApp address-confirmation step before ship. RTO on flagged orders dropped by 30%.
What was the rollout sequence?+
Phased by brand from week 9 to week 15. Smaller volume brands went first to surface edge cases, then the two largest fashion brands transitioned over a 2-week window with daily monitoring. Full 14-brand coverage was hit before the next festival peak.
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