Part numbers and table structure
Column headers accompany indexed rows, preserving the meaning of codes, names, specifications, prices and other fields
Table headers and rows remain present in retrieval context, so the assistant can suggest candidates and show the file, sheet and row range behind the result
Try AdaptixAIColumn headers accompany indexed rows, preserving the meaning of codes, names, specifications, prices and other fields
Customers do not need the exact item name: semantic retrieval finds related candidates and keyword search strengthens precise matches
A catalogue can be compared with a proposal or requirements list. The assistant matches items, calculates available quantities and shortages from table data, and explains the results using project policies
The owner adds CSV, TSV or XLSX to the selected project knowledge base
The customer describes the need in text or sends a supported file containing items and conditions
AI compares the retrieved passages and identifies the file, sheet and rows that should be verified
Yes. Encoding is detected safely and header-based CSV and TSV are supported; invalid or unsupported files return a clear error
No. Quality depends on the table and the request. AI proposes relevant candidates and sources, while a person should confirm material commercial decisions
No. A catalogue and proposal are one example. The same retrieval works for assortments, reference tables, price lists, requirements and other structured data
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