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KGNN

Equitus KGNN is an automated data unification platform that ingests, structures, and contextualizes large volumes of data, without traditional ETL.

The Fusion interface in action, 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.

Cost / License

  • Pay once
  • Proprietary

Platforms

  • Self-Hosted  On IBM Power10 or X86.
Fusion is not a standalone software but a visualization layer that allows users to explore and interact with the knowledge graph automatically generated by KGNN. It provides transparency into the semantic structure and data relationships created behind the scenes—useful for validation, analysis, or integration, but not required for KGNN to operate.
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Features

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  1.  Data visualization
  2.  Seamless Data Integration
  3.  Knowledge base

 Tags

  • unstructured-data-extraction
  • data-integration
  • Data Analysis
  • knowledge-engine

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KGNN information

  • Developed by

    US flagequitus

  • Licensing

    Proprietary and Commercial product.

  • Pricing

    One time purchase (perpetual license) that costs from $150000.

  • Alternatives

    4 alternatives listed

  • Supported Languages

    • English
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What is KGNN?

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 AI (GenAI) deployment.

Equitus KGNN uses a combination of natural language processing (NLP), machine learning (ML), and semantic technologies to dynamically build a self-constructing RDF knowledge graph. This semantic core enables organizations to extract entities, relationships, and contextual meaning from raw data—including documents, logs, and databases—and transform it into structured, vectorized formats optimized for advanced analytics and AI model consumption.

Equitus KGNN is suited for:

  • Enterprises operating across fragmented data systems.
  • Organizations needing contextualized data for AI, BI, or compliance use cases.
  • Teams looking to unify legacy and modern systems without redesigning infrastructure.

Key Capabilities:

  • Automated Data Ingestion: Handles structured and unstructured sources without manual pipelines.
  • Semantic Auto-Mapping: Dynamically generates a schema-less RDF knowledge graph.
  • Federated Integration: Enables bi-directional data exchange across legacy and modern platforms.
  • Real-Time Vectorization: Prepares data for AI models, RAG/CAG pipelines, and vector search.
  • Governance and Provenance: Maintains full data lineage, security, and compliance controls.

Benefits:

  • Reduce reliance on manual data engineering by 80%.
  • Minimize latency with near real-time data processing.
  • Improve AI accuracy and explainability through contextual enrichment.
  • Ensure compatibility with secure, on-premise, or air-gapped environments.

Minimum System Requirements: IBM Power10/11 40 Cores 512GB RAM 4TB SSD (usable) RedHat OpenShift 4.18

X86/GPU 24 Cores 256GB RAM Nvidia GPU with 24GB+ 4TB SSD (usable) RedHat OpenShift 4.18

Equitus KGNN is built for scalability, edge-readiness, and enterprise-grade deployment, enabling seamless data unification across the full lifecycle of AI and analytics initiatives.

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