Automate Warehouse Inventory and Reordering with AI Agents

Most small logistics operations rely on their Warehouse Management System's (WMS) built-in inventory module. Tools like Fishbowl or NetSuite WMS use simple min/max logic. The system triggers a reorder for an item when it hits a static threshold of 10 units, regardless of seasonality, current sales velocity, or supplier delays. This creates costly blind spots.

Consider a 20-person e-commerce business using a standard WMS. A product suddenly goes viral, and daily sales increase fivefold. The system's reorder point is 50 units, a threshold that was previously a two-week supply but is now only a two-day supply. The WMS waits until the stock hits 49 units to send an alert. By the time a manager sees the alert and manually creates a purchase order, the item is already sold out, leading to 10-15 days of lost sales.

Inventory plugins for platforms like Shopify or BigCommerce track stock levels accurately but cannot act on the data. They can send a low-stock alert, but they cannot automatically generate a formatted purchase order and send it to a specific supplier's required email address or portal. This leaves a critical manual gap between knowing there is a problem and solving it.

The structural issue is that these tools are designed for passive inventory tracking, not active procurement automation. Their data models are rigid. They cannot ingest and act on external signals, like a supplier's updated PDF price list or a shipping carrier's API showing port delays, to dynamically adjust reordering logic. Your business logic is forced to fit the tool's limitations.

The engagement would start with a data systems audit. Syntora maps your entire inventory data flow, from the WMS API and historical sales data to supplier communication channels (API, email, PDF attachments). This process establishes a baseline for actual lead times, sales velocity, and data quality for each major SKU category. You would receive a summary report outlining the exact logic for the reordering agent.

The core of the system would be a set of AWS Lambda functions running on a 15-minute schedule. A Python service pulls current stock levels from your WMS and recent sales data from your e-commerce platform. Using this data, a simple forecasting model projects demand for the next cycle. For suppliers without APIs, the Claude API would parse incoming emails and PDF price lists to extract item numbers, costs, and availability, updating a Supabase database that feeds the ordering logic. When a reorder is triggered, a FastAPI service generates the PO and sends it.

The delivered system is a serverless application that runs in your AWS account, which you own and control completely. It requires no daily manual input. You get a simple monitoring dashboard, built on Vercel, showing stock projections, pending reorders, and system logs. The system is designed to augment your existing WMS by adding an intelligent automation layer, not replace your core software.