Removing Manual Inventory Reconciliation and Order-Status Support Volume
A DTC apparel brand selling on Shopify plus two marketplace channels
Representative engagement based on ScaleHardened's standard implementation pattern for DTC brands with this profile. Client identity, exact figures, and quotes are not disclosed or fabricated.
Problem
Inventory was tracked separately across the Shopify storefront and two marketplace channels, requiring a staff member to manually reconcile stock levels daily to avoid overselling. Meanwhile, order-status questions made up the majority of inbound support tickets, most answerable purely from order data already in the system.
Solution
We built an automated inventory sync across all three channels using each platform's API, with webhook-driven updates instead of scheduled polling to keep stock levels current in near real time. In parallel, we deployed an AI support layer inside the brand's existing help desk that resolves order-status and policy questions directly against live order data, escalating anything outside that scope.
Architecture
A central inventory-of-record service reconciles stock across Shopify and the two marketplaces via webhook events and API calls, with conflict-resolution rules for simultaneous sales. The support layer is grounded in the brand's actual order data (via API) and policy documentation, with explicit escalation triggers for refund disputes, damaged-item claims, and anything emotionally charged.
Workflow
A sale on any channel triggers a webhook that updates the central inventory-of-record and pushes corrected stock levels to the other channels.
Customer support inquiries route through the AI layer first; order-status and standard policy questions resolve automatically against live data.
Anything involving a dispute, damage claim, or clear frustration escalates immediately to a human agent with the full order and conversation context attached.
A weekly reconciliation report flags any sync discrepancies for manual review, rather than assuming the automation is infallible.
Implementation notes
The team was initially cautious about automation anywhere near checkout — we scoped the work explicitly to stay outside checkout/payment logic and built the inventory sync and support layer as operational-layer automation only, which matched their risk tolerance.
Results
- — Engagements of this shape typically see overselling incidents tied to cross-channel stock drift drop 60-80% after a webhook-driven sync replaces scheduled polling, based on the mechanism, not audited client figures
- — Order-status tickets that resolve without human involvement typically land in the 40-60% range once they're grounded directly in live order data, based on the mechanism, not audited client figures — freeing the support team for disputes and higher-touch conversations
- — These are engineering estimates of the mechanism's typical effect based on the workflow structure, not audited client-reported figures