Travel technology platform TourMind has introduced MappingMind, an AI-powered room type mapping solution intended for the B2B hotel distribution sector.
MappingMind is claimed to be the first hotel room type mapping solution driven by deep learning and large language models.
The solution delivers a 99.6% accuracy rate in mapping and an automation rate exceeding 90%, according to TourMind. Furthermore, the solution is said to respond in as little as 500 milliseconds.
MappingMind is engineered to fix the issue of inconsistent room type naming conventions and diverse descriptions across various suppliers within the industry.
It operates as an AI-based room terminology matching and mapping application programming interface (API), capable of functioning on a global scale.
The solution utilises advanced algorithms and deep semantic analysis to synchronise room type information between suppliers and distributors.
MappingMind's core functionalities include high-speed base matching for instantaneous processing of extensive datasets and a deep thinking mode for deciphering intricate and ambiguous room descriptions.
The solution can also customise matching to tailor mapping strategies to individual business requirements.
According to TourMind, this solution can integrate business features such as room type and facilities and uses deep neural networks for processing. This produces a match confidence level that surpasses 99.8%, said the company.
MappingMind uses large language models for arbitration and validation purposes, providing transparent and comprehensible results.
The solution is constructed on “multi-scenario models” and trained with trillion-scale data points. It uses deep semantic understanding technology to accurately discern hotel aliases, abbreviations, errors in addresses, and other unclear details, thereby preventing mismatches from occurring.
MappingMind is designed to serve B2B hotel distributors, online travel agencies (OTAs), hotel technology companies, and travel groups.
TourMind CEO Karma Young said: "Room type matching has always been a technical challenge for B2B hotel distribution, with traditional methods often struggling in complex scenarios.
"MappingMind leverages large language model technology to better understand the semantic information in room type descriptions, significantly improving matching accuracy and automation levels. We hope this technology can help distributors reduce operational costs, improve work efficiency, and create practical value for our industry partners."
