Build AI Automation for Multi-Channel Ecommerce

Many SMBs start with Shopify Flow or third-party apps like Sellbrite. These tools handle basic inventory syncing but fail with complex logic. For example, Shopify Flow can trigger when a new order arrives, but it cannot easily query an external warehouse API, check stock levels for a bundled product, and then update inventory on both Amazon and Walmart with different business rules for each. Sellbrite syncs quantities but has a 15-minute lag, which is too slow during a flash sale.

Consider a 10-person ecommerce business selling custom apparel on Shopify and Etsy. They offer product bundles, like a t-shirt and hat combo. An order for the bundle on Shopify needs to decrement inventory for the individual t-shirt and hat SKUs. Shopify Flow cannot handle this "kitting" logic natively across external channels. This means an employee spends 2-3 hours daily manually adjusting inventory levels in Etsy after cross-referencing Shopify sales reports, risking overselling the individual hat if a bundle sells out.

The structural problem is that platform-native tools and generic sync apps are built for the 80% case. Their data models assume a one-to-one relationship between a sale and a SKU. They are not designed for businesses with custom kitting, multi-location fulfillment logic, or the need to parse supplier-specific data formats. Their architecture cannot support running custom Python code to handle your unique business rules, forcing you into manual workarounds.

The engagement would start with a discovery audit of your existing channels (Shopify, Amazon, etc.) and fulfillment systems. Syntora would map out every data flow, from order creation to inventory update, identifying the specific business logic that current tools miss. This audit produces a clear architectural diagram and a fixed-scope proposal, detailing exactly how the new system will connect to your existing stack.

A central FastAPI service, deployed on AWS Lambda for cost-effective, event-driven scaling, would act as the hub. When a new order arrives via a Shopify webhook, the service parses the payload. For bundled products, a call to a Supabase database resolves the bundle into its component SKUs. The system then makes parallel API calls using httpx to update inventory levels on Amazon and Walmart, handling the unique authentication and rate limits for each. A typical inventory update across 3 channels would complete in under 500ms.

The delivered system is a set of serverless functions that you own, running in your own AWS account. The system would include structured logging with structlog to a central dashboard for monitoring every transaction. You would receive the complete Python source code in your GitHub repository, a runbook for maintenance, and a 4-week post-launch support period. The integration is invisible to your team; they just see inventory levels that are always correct.