Master data matching with AI: No more manual assignment 3 Min. Reading time

Why traditional approaches are no longer sufficient

When it comes to invoice processing and master data management, many companies encounter a familiar problem: identical items are labelled differently by different suppliers. Traditional systems compare this data on a character-by-character basis and fail when it comes to semantic differences.
The result: items are not recognised, bookings have to be corrected manually, and duplicates are created. This leads to error-prone processes, high time expenditure and unnecessary costs.

The solution: semantic mapping with embeddings

Post Business Solutions' AI-supported IDP platform DAiTA rethinks master data reconciliation. Instead of classic logical approaches such as string comparison, lookup tables and rule-based mappings, the platform analyses the meaning of terms using so-called embeddings. These mathematical vectors capture the context and meaning of texts and enable precise mapping, even with differing spellings.

How intelligent matching works

1. Document receipt & extraction
   Documents are sent to the platform either physically or digitally. Content such as article texts, quantities and prices are extracted using OCR, machine learning and LLMs.
2. Semantic processing
   The extracted texts are converted into embeddings, stored in a vector database and compared with the company's master data – not by wording, but by meaning.
3. Automatic assignment
   Assignment takes place automatically once a defined similarity value is reached. Alternatively, employees receive a sorted list of hits for quick selection.

Practical example

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