Self-learning AI matching agent for logistics dispatch
Service
Solution
Sector
IPP is a leading logistics service provider in the circular pooling sector. The company processes thousands of pallet movements daily and is highly dependent on accurate address data to coordinate product flows.
The solution focuses on an AI-driven address matching engine integrated within the Dispatch 2.0 platform. The engine uses smart text analysis, normalization, and external validation sources to automatically match customer input with existing addresses. When a perfect match is not found, the system suggests the most probable options with confidence scores. This minimizes incorrect input and significantly speeds up the validation process.
%20_12.jpg)
Variations in address input
Deviating address input led to incorrect delivery information, delays, and unnecessary manual correction work. Small variations, such as typos or missing elements, prevented addresses from being linked to existing references.
Limitations of existing systems
Because the matching logic was confined within the existing ERP system, changes were slow and costly. The lack of automatic recognition burdened both the back office and the customer, directly impacting lead times and data quality.
.png)
Intelligent matching layer in OutSystems
By deploying AI agent technology, an intelligent matching layer was introduced in OutSystems. This layer combines string matching, geocoding, and AI models to normalize addresses and calculate similarities.
Dynamic suggestions and self-learning capability
Where exact matching is not possible, the agent generates dynamic suggestions based on similarity scores, allowing users to select the correct location with minimal effort. The architecture supports self-learning functionality: once confirmed, matches are stored for reuse.
Thanks to AI-driven matching, thousands of manual correction steps have been eliminated. The system continuously learns and provides us with more control and higher data reliability.
.avif)
.avif)
