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Autonomous
Graph Platform

Unify your fragmented enterprise data into clean, contextual, AI-ready intelligence. Cut integration costs and accelerate time to insight.

Enterprise AI is stuck in bottlenecks

  • Manual and slow data preparation eats time and budgets

  • Siloed platforms block scalability and integration

  • Black box AI erodes trust and limits adoption

KGNN automatically turns raw, disconnected data into real-time, actionable intelligence.

Uncover hidden patterns, gain the full picture, and put your data to work for faster, smarter decisions.

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Benefits

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Automated

Eliminate manual ETL, schema design, and mapping. Cut the time to ingest, structure, and build knowledge graphs by up to 80%.

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AI-Ready

Deliver clean, contextualized data that fuels BI, analytics, and AI initiatives. Reduce bias, errors, and hallucinations.

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Scalable

Onboard any dataset without predefined schemas. Scale from on-prem to cloud to edge environments.

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Secure

Protect sensitive information with enterprise-grade provenance, fine-grained access control, and compliance support for regulated industries.

Simplify Integration

Make all your Data AI and RAG Ready

Scale your AI Initiatives Faster, more Easily, and more Efficiently, in the most cost-effective way.

KGNN on IBM Power10 

No GPUs, No Cloud Needed

AI-at-the-Edge Enabler

KGNN runs natively on IBM Power10 servers empowering organizations to create autonomous AI systems that operate at the edge, independently of external cloud services, and overcoming GPU resource limitations.

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The on-premise Equitus KGNN appliance provides a robust solution for unifying and connecting knowledge assets residing within your organization's diverse systems and applications. This integration enables a holistic understanding of complex relationships and facilitates enhanced decision-making.

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Our partnership with IBM amplifies KGNN capability, offering a solution that is both cost-effective and energy-efficient without the need for GPUs.

KGNN, a pretrained Knowledge Graph Neural Network Engine, automatically ingests, structures, and augments raw data. It transforms your data into a semantically rich, machine-readable format optimized for AI processing and Retrieval-Augmented Generation (RAG) pipelines across diverse applications and microservices.

KGNN helps users understand complex situations, make informed decisions, and improve operational effectiveness.

Automated Data Structuring
for Enhanced AI-RAG

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KGNN transforms your data into a semantically rich, machine-readable format optimized for AI processing and Retrieval-Augmented Generation (RAG) pipelines across diverse applications and microservices.

 

KGNN enhances Retrieval-Augmented Generation (RAG) with LLMs by providing a structured, context-rich representation of your data, which can be used to improve the accuracy and relevance of the information retrieved for LLM generation. By leveraging the relationships within the graph, you can provide the LLM with more specific and relevant context, leading to more informed and accurate responses. 

Other AI Uses for KGNN:  

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  • Advanced Pattern Analysis: Identify recurring patterns, trends, and subtle relationships within large datasets to gain deeper insights across various operational scenarios.

  • Temporal Analysis: Analyze the evolution of events over time to understand situational dynamics and identify potential future risks or opportunities.

  • Link Analysis: Reveal critical connections and relationships between entities to provide a comprehensive understanding of underlying networks and interactions.

  • Entity Extraction: Efficiently extract and analyze specific entities (e.g., individuals, organizations, events) from unstructured data to focus on relevant information.

  • Integrated Geospatial and Temporal Analysis: Analyze data incorporating both location and time-based components, such as mapping crime hotspots or tracking the progression of events geographically.

The Equitus Advantage

Efficient Data Processing at the Edge

KGNN can ingest and process unstructured data directly on IBM Power10 servers deployed at the edge, and produce contextualized data AI ready.

Facilitating AI Ready Data

  • Structured Output Produces AI-ready, structured graph data that can be readily used by edge AI applications.
     

  • Graph RAG Ready Enhances retrieval-augmented generation systems by providing high-quality contextual data.

Optimize Performance without GPUs

  • MMA Utilization By leveraging IBM’s MMA, KGNN performs complex computations efficiently without relying on costly GPUs.
     

  • Energy Efficiency Reduced energy consumption makes it ideal for edge environments where resources are limited.

Private and Off-Cloud Operations

  • Data Sovereignty Processing data locally ensures compliance with data privacy regulations and reduces security risks.
     

  • Low Latency Eliminates the need for constant cloud communication, resulting in faster data processing and real-time analytics

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