Build Custom AI Automation for Ecommerce Fulfillment & Inventory

Many stores try to connect Shopify and their 3PL with off-the-shelf connectors. These tools work for simple one-to-one syncs but fail with complex logic. They cannot handle order splitting if an item is backordered, routing orders to different warehouses based on shipping cost, or updating inventory for product bundles.

A Shopify merchant selling gift baskets faces this daily. A single basket contains 5 SKUs. A connector might sync the basket's inventory but fails to decrement the inventory of the 5 component SKUs. When one component SKU sells out, the entire bundle must be manually marked out of stock across all channels, leading to lost sales or overselling.

These connectors are fundamentally state-less. They react to one event (a new order) but cannot hold state to manage a complex process like "check inventory for all 5 components, reserve stock, wait for payment confirmation, then trigger shipment". This requires a stateful system, which is a custom engineering task, not a point-and-click configuration.

Syntora would begin with a discovery phase to understand your existing order fulfillment workflows, sales channels, and specific inventory challenges. This helps us define the most effective system architecture and technology choices.

The first technical step would involve connecting to your e-commerce platform's API (e.g., Shopify or BigCommerce) to ingest historical order and product data. This data would be loaded into a Supabase Postgres database, chosen for its reliability and integrated real-time capabilities. We would work with you to model your product catalog, including variants, bundles, and component SKUs, ensuring the data structure supports your business logic. The resulting data model would be documented and delivered to your repository.

For real-time order processing, Syntora would build a Python service using FastAPI. This service would be designed to listen for new order webhooks from your e-commerce platform. Upon receiving an order, a Pydantic model would validate the incoming payload. The service would then query the Supabase database to check component inventory, atomically reserve stock within a database transaction to prevent overselling, and route the fulfilled order details to your 3PL's API.

To assist with inventory forecasting, a Prophet time-series model would be trained using your historical sales data. This model would run on a schedule, orchestrated by an AWS Lambda function triggered by EventBridge, to generate reorder point recommendations for each SKU. A daily summary could then be configured to send notifications to a designated Slack channel. Syntora has experience building similar data processing pipelines for various applications, including document analysis for financial institutions using technologies like Claude API, and these architectural principles apply directly to e-commerce data.

The FastAPI service would be containerized using Docker for consistent deployment and configured for continuous integration, potentially on platforms like Vercel or AWS. For operational visibility, all application logs would be channeled to a monitoring system like Axiom. Alerting rules would be established – for instance, triggering on a sustained API error rate or if the order processing queue exceeds a defined threshold – to notify your team and Syntora for timely investigation. The projected hosting costs for this stack are typically modest, often under $50 per month. The deliverables for an engagement would include the deployed system, source code, and comprehensive technical documentation.