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KGNN

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
KGNN
Score 0.0 out of 10
Enterprise companies (1,001+ employees)
Equitus KGNN is an automated data unification platform in the knowledge graph and AI data infrastructure category. It is designed for enterprise organizations seeking to ingest, structure, and contextualize large volumes of structured and unstructured data without relying on traditional ETL processes. KGNN automates the transformation of disparate enterprise data into semantically enriched, AI-ready knowledge to support use cases such as analytics, business intelligence (BI), and generative…N/A
Pricing
KGNN
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Pricing Offerings
KGNN
Free Trial
No
Free/Freemium Version
No
Premium Consulting/Integration Services
Yes
Entry-level Setup FeeNo setup fee
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KGNN
ScreenShots

KGNN Screenshots

Screenshot of KGNN displaying a richly connected semantic network generated by Equitus KGNN. At the center is a core entity node, automatically linked to multiple other entity types such as people, organizations, locations, and categories. These nodes are color-coded and icon-tagged to represent different entity classes.Screenshot of an image where the user is selecting from a variety of layout modes, Concentric, Lens, Sequential, Organic, and Structural, each offering different visual perspectives to better understand the graph’s structure. The central workspace displays a dynamically generated graph consisting of nodes (entities) and links (relationships), color-coded by type or category. Users can manipulate the graph view, search items, group nodes, add documents, run queries, or export the graph data for further analysis.Screenshot of Equitus's integrated UI, which is designed to visualize and interact with the knowledge graph automatically generated by KGNN. The interfae is not required for KGNN to function but offers a powerful, intuitive environment for users who wish to explore and validate semantic relationships within the graph.Screenshot of the interface that enhances transparency and usability of the knowledge graph, especially in environments where data traceability, context validation, or manual exploration is necessary.