The same item appears under multiple codes or naming versions.
Abbreviations and typos make records inconsistent.
Systems are disconnected — data doesn’t link across platforms.
Manual lookup is slow and error-prone.
Variations and mismatches disrupt processes and workflows.
Data volume increases — clarity decreases.
The system detects duplicates and variations and groups them into a single, unified record — so everything is consistent and accessible in one place.
Meaning-based search goes beyond keywords — so items are found even if the spelling, format, or naming differs.
– making every record clear, eliminating duplication, and automatically linking related items. Manual searching is replaced by contextual, AI-driven results — enabling transparent, consistent, and reliable data management.
it delivers reliable results you can confidently base decisions on.
The AI Article Search engine doesn’t just find records — it shows the latest version, related data, and recommended usage. Search becomes confident, not trial-and-error.
AI replaces manual searching — so every needed item is found with a single query.
Not keyword-based — it understands content, synonyms, and abbreviations, so results appear even if the phrasing isn’t exact.
The AI detects alternative names and legacy item numbers — displaying them in one organized result block.
A single search reveals all connected data — item numbers, products, documents, and vendor details.
The system interprets data from tables, documents, and other structured fields — without manual preparation.
Relevant results appear in seconds — with no filtering, time wasting or repeated attempts.
One question is enough — results are fast, accurate, and context-aware.
Different naming conventions, formats, and systems stop creating confusion — and finally work together. With every search, the AI becomes more accurate — making results faster and decisions more reliable.
