NEW YORK, June 23, 2026 – To further ensure and accelerate business success, Graphwise, the leading Graph AI provider, today announced a new quarterly enterprise initiative and webinar series designed to show organizations how to build a Semantic Backbone and create a reliable infrastructure for Enterprise AI.
To learn more about the Graphwise Platform Pulse initiative or to sign up for the complimentary July webinar and end to end live demo that demonstrates how the different components play together click here.
The company also announced the integration of the Graphwise Platform, which turns fragmented data into measurable, hallucination-free business outcomes. Rather than stitching together and maintaining different software applications to build a secure AI data pipeline, the Graphwise Platform provides organizations with a unified environment by automating the transition from raw, disconnected enterprise data to highly intelligent, autonomous AI.
Grounding AI with Verified Truth
The Graphwise Platform builds a solid semantic layer that connects siloed data, documents, and unique business logic into one trusted source. By giving GenAI a “business brain” that completely understands the business POV, the Graphwise Platform structures data into a secure web of explicit relationships to create a trusted Semantic Backbone that reduces unpredictable AI hallucinations and replaces uncertainty with reliable facts.
The Platform guarantees 100% deterministic, explainable AI responses with multi-hop reasoning, which is the ability to connect pieces of information across entirely different systems while automatically generating the data pipelines via AI-assisted modeling.
Creating a Trustworthy, Scalable Foundation for Enterprise AI
The Graphwise Platform divides the journey into two distinct, manageable phases:
Phase 1: Establish the Structural Foundation
- Mapping Knowledge Through Taxonomies and Ontologies:
Graphwise helps organizations map the logic behind their data, using taxonomies to standardize unstructured text terminology (PDFs, manuals) and industry ontologies to map structured real-world relationships (SQL, IoT).
- Making Data Operational Through Graph Automation:
Once labeled, the platform acts as an automated pipeline that coordinates ingestion workflows across separate systems. It later enriches the content identifying entities, extracting concepts, applying metadata, and automatically tags information so that previously hidden knowledge becomes visible to the system. Each piece of information thus becomes part of a larger network of meaning and context.
Enriched knowledge is then stored in GraphDB, Graphwise’s enterprise-grade semantic graph database that uses native reasoning to automatically infer new business insights.
Phase 2: Deploying the Semantic Layer and Powering Intelligence:
- Powering AI Applications with GraphRAG:
Once the knowledge graph is built and securely stored, the Platform is ready to power AI applications with GraphRAG. The Graphwise Platform uses the knowledge graph to assemble verified business context before generation happens so AI works from governed enterprise facts. This enables traceable responses and multi-hop reasoning across different data sources.
- Achieving Autonomous AI:
Once the above steps are completed and the foundation exists, data becomes reusable across search and recommendation, analytics, AI assistants, and future agentic workflows. This removes the need to create a new pipeline for every use case, and prepares organizations for the secure deployment of autonomous AI agents.
“GenAI is smart, but it has no idea what the business knows, how data is connected, or which answers it should trust,” said Andreas Blumauer, Senior VP Growth at Graphwise. “The Graphwise Platform, and resulting Graphwise Pulse initiative, is about making enterprise AI simpler to connect, easier to trust, and ready to scale. Instead of managing separate systems for modeling, enrichment, storage, and retrieval, organizations get one complete, governed path from raw enterprise data to production-ready AI with every output traceable back to its source.”
