
Accurity uses a trained AI model that understands the meaning of words, not just exact matches. It compares glossary terms with catalog objects and proposes the most likely links in both directions.
From glossary to catalog
Glossary term: Employee Headcount by Department
→ Suggested catalog objects: HR_Staffing_Table, Dept_Headcount_Field
Glossary term: Net Revenue Recognition
→ Suggested catalog objects: Revenue_Journal_Table, Net_Rev_Calc_Field
From catalog to glossary
Catalog object: Customer_Order_Date
→ Suggested glossary terms: Order Lifecycle, Sales Transaction
Catalog object: Product_Return_Code
→ Suggested glossary terms: Returns Process, Quality Issue Classification
Each suggestion includes a confidence score so users can decide quickly. Existing links are skipped automatically, and suggestions are always ordered with the most relevant match first.
The model understands multilingual content. If your business glossary is in German and French while your data catalog is in Dutch, it still works. The AI compares meaning, not only surface words, which makes cross-language discovery possible.
Accelerate business lineage by reducing manual search and mapping
Work smarter with AI that understands context and meaning
Support multilingual teams without extra configuration
Improve governance quality by ensuring glossary terms are consistently linked to data assets
Keep control with human review for every suggestion
Competitors like Collibra and Atlan promote AI discovery, but their focus is mostly on metadata enrichment. Alation uses AI for curation, but it does not natively support multilingual linkage. Accurity Business Lineage AI Suggestions goes further. It directly connects the business glossary with the data catalog across languages with confidence-scored proposals that users review and approve.
When glossary and catalog are connected, business lineage builds itself. Compliance checks are faster. Data requests resolve in minutes. And governance becomes something teams want to use, not avoid.
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