Use AI to Achieve Real-Time Inventory Accuracy and Eliminate Waste

Most SMBs run their warehouse on the inventory module of an ERP like NetSuite or a dedicated WMS like Fishbowl. These systems are databases, not monitoring tools. They rely entirely on manual barcode scans for updates, making them a record of where inventory *should* be, not where it actually is. The data is only as accurate as the last scan.

A common scenario involves a warehouse worker moving a pallet to make space but forgetting the location scan. Consider a 15-person e-commerce company using Fishbowl. A worker moves a pallet of fast-selling SKUs from bin C-04 to G-12. The WMS still shows the stock in C-04. A picker receives an order, goes to the empty C-04 bin, and wastes 15 minutes searching for the item, delaying the shipment. This small error, repeated 5-10 times a day, kills operational efficiency.

To combat this, teams perform manual cycle counts, a disruptive process that shuts down sections of the warehouse for hours. Cycle counting finds errors days after they happen, leading to stockouts on items you thought you had and excess inventory of items you thought were gone. The core problem is architectural: traditional WMS platforms are designed for transactional updates, not for continuous, passive data ingestion from sources like cameras.

The engagement would start with a warehouse audit. Syntora would map your existing camera placements, assess lighting conditions, and identify blind spots. We would also audit your WMS API to confirm how inventory data can be updated programmatically. This audit produces a clear plan for integrating a vision system with your current software stack without replacing it.

The technical approach would use Python and the OpenCV library to process video feeds from cameras. An AWS Lambda function would trigger on detected motion in specified zones, like a pallet rack face. This function sends the image to the Claude API, which identifies products by their packaging and counts them. This is more resilient than barcode-only systems; Claude can identify a product with 60% of its label obscured and return a count in under 800ms. A FastAPI service would then reconcile this count with your WMS data and flag discrepancies.

The delivered system would automatically update inventory levels in your WMS every 15 minutes. You would receive a simple dashboard that highlights discrepancies between the AI count and the WMS record, with alerts for variances over a set threshold (e.g., 2%). You get the full source code, a runbook for the AWS services (which typically cost under $50/month to run), and full control over the system.