Squash irregularities before they become a real problem and effectively shield your order shipments and SLAs, all in real-time
Safeguard your Ecommerce Ecosystem From
Set up rules to automatically accept, hold or reject orders across online sales channels, based on various parameters.
Intelligently route orders from multiple webstores & marketplaces to the most appropriate nodes across various locations, automatically.
Pick, pack, label, and ship your orders across channels from a single screen. Control order allocation and handle exceptions with ease.
Manage returns and cancellations by planning reverse logistics, handling QC checks, and updating stock adjustments automatically.
Stop order anomalies dead in their tracks with early detection
Identify, isolate & manage orders with irregularities in real-time for better inventory utilization
Instantly monitor anomalies across a large number of order attributes – channel, product, quantity, discount, payment & delivery mode
Assess your e-commerce risk levels in real time with our AI Score
AI Driven e-commerce Order Management Platform
AI based Real Time Order Anomaly detection to detect any fraud or an unusual order. It will help sellers to isolate the anomalous orders so that same can be reviewed before releasing for execution. This will support sellers to avoid any fraud orders and for better utilisation of inventory for normal orders.
The AI model in Ordazzle is based on a fast & robust ML algorithm which explicitly isolates anomalies rather than profiles normal instances. Most anomaly detection methods involve building out a 'distribution' of normal based behaviours and then training a 'model' or threshold, to identify periods that do not conform to this 'normality' definition. While the AI model we have used take a fundamentally different approach; optimised specifically to isolate and identify anomalies. Anomalous data points are further away from the regular observations in the feature space.
The highlight of this AI mode is that may flag multiple false alarms but will not have any false negative i.e. will not miss a single anomaly. We intent to use same ML algorithm for detecting any unusual pattern in order processing to flag an alarm for possible SLA breach of order ship.