Avis KI

What is Avis AI?

Avis AI refers to the use of AI-powered methods for the automated processing of incoming delivery notifications – known as advance shipping notices (ASNs). Suppliers transmit such pre-notifications through various channels: as structured EDI documents, as PDF attachments in emails, as free-text messages, or via a web portal. Traditionally, transferring this information into a warehouse management or ERP system required manual data entry steps that are error-prone and time-consuming. AI methods – including natural language processing, optical character recognition, and rule-based classification – make it possible to automatically detect these documents, extract relevant fields such as delivery date, quantity, item number, or supplier, and forward them in a structured format.

How does Avis AI work in practice?

The typical process begins with the receipt of an ASN document in a defined mailbox or integration interface. The AI system first classifies the document type and selects the appropriate extraction model. The fields are then read out and matched against existing master data – such as item numbers or supplier master records. If the data matches, the ASN is automatically created in the target system; in the event of discrepancies or low recognition confidence, a defined exception process is triggered, initiating a manual review.

Recognition quality depends heavily on the variety of formats in incoming documents and the quality of the training data. Systems trained on a large number of supplier formats generally achieve higher automatic processing rates. It is important that the system makes transparent which fields were recognized with what level of confidence – only then can error handling be managed cleanly.

What matters when selecting a solution?

When selecting an Avis AI solution, you should evaluate the following criteria:

• Format support: Which incoming channels and document formats are covered – EDI, PDF, email free text, CSV?
• Recognition rate and confidence control: Above what threshold are documents processed automatically, and how are exceptions escalated?
• Master data matching: How tightly is the solution integrated with existing ERP or WMS systems, and how are discrepancies in item numbers or quantities handled?
• Auditability: Are all processing steps logged to ensure traceability?
• Learning capability: Can the model be further trained on the basis of manual corrections to continuously improve the recognition rate?

Depending on the vendor, solutions differ considerably in whether they are provided as standalone middleware, as a module within a WMS, or as a cloud service. Close coordination between logistics planning and IT is therefore important as early as the requirements phase.