Avoid AI Concept Creep with a Knowledge Graph Created in Hours

Modern data lakes and streaming architectures are optimized for data Volume, Variety and Velocity, not for preserving the Meaning of the data.

The Problem: Schemas only describe the structure of data (e.g., "Field A is an integer"), not the semantics (e.g., "Field A is the temperature in Celsius").

The Shift: Over time, data producers may change the intent of a field without a formal "contract" on its meaning. For example, a "Price" field might silently switch from USD to EUR, or a "Customer" tag might expand to include "Leads."

The Result: Silent changes lead to fragile downstream workflows and loss of accuracy as AI systems are guessing the meaning of data elements and concepts instead of having firm definitions.

Graphwise offers a knowledge graph management platform using a hybrid AI to:
- turn expertise and domain knowledge into a shared, unambiguous, evolving, knowledge asset, easy to use for people, AI agents and other IT systems;
- accurately retrieve data for analytics (BI), search (CMS) and AI (RAG).  

There is an abundance of shallow knowledge graph implementations and proprietary ontology formats that cannot deliver on the above promises, because of the lack of formal semantics (for reasoning and data validation), standards (to prevent vendor lock-in) and industry ontologies (for interoperability across the value chain). There are also numerous GraphRAG offerings which fail to bring awareness and precision because of the lack of ontologies and domain knowledge. Most important of all, such implementations cannot meet the governance requirements for production use.

Graphwise platform offers an all-in-one knowledge graph management platform, which is designed and matured for over 20 years to fulfill all of the above roles, based on open standards, namely the RDF-graph technology stack. Historically, the so-called semantic knowledge graphs were complex to build and hard to maintain and use. We offer AI aided taxonomy and ontology management tools that remedy this and make it possible to start quickly, with a single use case, but still have a proven upgrade path to an enterprise-wide semantic backbone.

Throughout the presentation we will make a hands on demonstration of:
- Ontology bootstrapping in the IT infrastructure 
- Automated data ingestion and document validation
- Designing an RAG workflow using the Agentic UI framework
- Monitoring the execution, including AI-related costs