AI is becoming a fundamental capability in Track & Trace. The first use cases powered by the KVINTA AI framework are already helping our clients improve data quality, reduce operational effort and cost, and make Track & Trace processes more efficient and scalable.
The Kvinta AI chat is only the beginning – in Kvinta, you can already talk to your Track & Trace data. When Kvinta is your serialization repository, you can ask questions about serial numbers, events, movements, statuses or patterns – and use AI to analyze the data behind them.
AI becomes part of an actual process – take Kvinta EMVO Alert Management, for example. When an EMVS alert comes in, Kvinta can investigate it against the relevant serialization data and pack history, analyze patterns, identify the likely cause, and propose a response.
The user can then review, adjust, and approve the proposed resolution. Or, where predefined business rules allow it, the response process can be automated.
So Kvinta AI follows the path :
❓ “What happened?” ➡️ “Why did it happen?” ➡️ “Here is the solution.”
And again, even further to a self-improving state, when all the detected data anomalies are used to improve the process and the system.