Use AI to Achieve 99% Inventory Accuracy in Your Warehouse

Many small to medium-sized warehouses run on Fishbowl Inventory or the inventory module in NetSuite. These systems are effective for basic transaction tracking, but their cycle counting logic is simplistic. They typically generate counts based on fixed rules, such as counting all A-items every 30 days or counting locations that have not been audited in 90 days.

Consider a 25-person warehouse managing aftermarket auto parts. A popular air filter (SKU #AF451) has a high sales velocity and looks identical to another filter (SKU #AF452). A picker accidentally grabs and scans the wrong part. The WMS registers the pick, but the physical inventory is now off for two SKUs. Because SKU #AF451 was cycle counted last week, the system will not flag it for another month, allowing dozens more incorrect orders to ship.

The structural problem is that these WMS platforms treat inventory as a transactional ledger, not as a system with predictable patterns of failure. They lack the ability to run predictive models. They cannot correlate receiving errors, picking mistakes, and return data to calculate a real-time risk score for each SKU. The WMS architecture is designed for recording transactions, not for statistical analysis of those transactions over time.

The result is constant firefighting. Managers spend hours manually reconciling discrepancies discovered during packing and shipping. This leads to stockouts on popular items, inflated safety stock for others, and eroded profit margins from expedited shipping and canceled orders.

The engagement starts with a data audit. Syntora would connect to your WMS database or API to extract 12-24 months of inventory movements, cycle count results, sales orders, and purchase orders. This data is analyzed in a Python environment using pandas to identify the strongest predictors of inventory errors. You receive a report on data quality and the most promising signals for a predictive model.

The system would be a set of Python services running on AWS Lambda. One service pulls data from your WMS nightly. Another trains a predictive model to score every SKU based on factors like velocity, recent handling, and historical error rates. The core logic is wrapped in a FastAPI application, which exposes an API endpoint that your handheld scanners or manager dashboard can call to get the top 15 riskiest SKUs for counting each day. Data is stored in a Supabase Postgres database.

The delivered system integrates directly with your team's existing workflow. Instead of a generic list, your dashboard would show a prioritized audit list with reasons for each flag, such as 'High return rate this week'. The API has a response time under 200ms and the system costs less than $50 per month to host on AWS. You receive the full source code and a runbook for maintenance.