Intragroup, Datavault, and Equitus.us KGNN—appear to relate to different aspects of data management, AI enablement, and IT infrastructure, and all have recent or established connections with IBM.
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1. Equitus.us KGNN (Knowledge Graph Neural Network)
Technology & Focus: A platform that delivers a Knowledge Graph Neural Network (KGNN®).
2 Its primary function is to automatically ingest, unify, and contextualize disparate data (structured and unstructured) into a single, scalable knowledge graph data store.3 Key Value: It provides a semantic layer for data, offering context, explainability, traceability, and on-premise security for AI agents and models. It is described as a sophisticated Retrieval-Augmented Generation (RAG) Engine.
IBM Connection: Equitus is an IBM Technology Partner. The KGNN platform is specifically optimized for and runs natively on IBM Power10 servers, leveraging their Matrix Math Accelerator (MMA) technology for high-performance deep learning at the edge, often without requiring GPUs. This partnership focuses on deploying advanced, secure, and private AI solutions on-premises.
2. Datavault AI (DVLT)
Technology & Focus: A company and platform specializing in AI data experience, valuation, and monetization. It focuses on comprehensive solutions in its Data Science Division, often involving cloud-based platforms.
Key Value: The platform aims to provide a scalable path to data monetization for enterprises and facilitate the use of data within the Web 3.0 environment.
IBM Connection: IBM has an expanded collaboration with and has committed a multi-million dollar investment of resources (engineering, technical sales, and quantum computing expertise) to support the growth and product roadmap of the Datavault AI platform. This collaboration aims to leverage IBM's platforms, like watsonx.ai, with Datavault's innovations to drive data monetization.
3. Project infragraph (from IBM/HashiCorp)
Technology & Focus: An IBM project (debuted at HashiConf) that aims to create an AI-ready control plane to unify infrastructure, security, and applications with a real-time model. The "graph" element implies a structure that maps the relationships between all IT assets.
Key Value: Its goal is to provide a single, unified system for infrastructure management by building a real-time relational model connecting all infrastructure, applications, services, and ownership.
14 This unified foundation is essential for future agentic (AI-powered autonomous) workflows and better visibility/context for IT operations.IBM Connection: It is an IBM project developed with HashiCorp, to be built on the HashiConf Cloud Platform and paired with other platforms through a Model Context Protocol (MCP)-server-based natural language interface.
Summary of Comparison
| Feature | Equitus.us KGNN | Datavault AI | Project infragraph |
| Primary Goal | Unify disparate data into a Knowledge Graph for secure, explainable, and private on-premise AI/RAG. | Facilitate AI data monetization and provide an AI data experience platform. | Create a real-time relational model (graph) of all IT infrastructure for unified management and autonomous AI workflows. |
| Core Technology | Knowledge Graph Neural Network (KGNN®). | AI-driven data monetization platform. | AI-ready control plane/real-time graph model of infrastructure assets. |
| IBM Relationship | Strategic Partner; Platform (KGNN) is optimized for and runs natively on IBM Power10 for edge AI. | Strategic Partner; IBM is investing resources to accelerate its platform and integrate with tools like watsonx.ai. | An IBM/HashiCorp project for IT infrastructure management. |
In short:
Equitus KGNN is about data unification and making the data AI-ready, secure, and traceable with IBM Power Systems.
16 Datavault AI is focused on monetizing and managing data with support from IBM's AI ecosystem.
17 Infragraph is about creating a unified, real-time model of IT infrastructure itself for better operations management.
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