Demo Area

Demonstrates how Knowledge Graphs enable algorithms to better analyze text by not only processing words, but also understanding the underlying concepts and their context.

Demonstrates how unifying company, product, and market knowledge into a single AI-ready graph gives teams one reliable source of truth.

Demonstrates how a connected, semantically powered system helps employees find precise answers in seconds instead of digging through documents - while ensuring teams reuse approved knowledge instead of recreating it.

Demonstrates how natural language access to complex biomedical data lets researchers ask questions directly and surface relevant relationships - cutting down manual data exploration and speeding up hypothesis generation.

Demonstrates how natural language interaction with complex power system data allows engineers to quickly trace grid relationships, identify issues, and access critical information - without needing to navigate multiple specialized tools.

Demonstrates how turning complex ESG disclosures into structured, explainable data allows analysts to quickly compare metrics, trace how scores are derived, and confidently support reporting and investment decisions.

Demonstrates how bringing Graphwise into M365 helps employees instantly find the right document, or answer within their daily tools - reducing time spent searching and cutting down on repeated questions.

Demonstrates how semantic search helps users quickly find the most relevant research across a vast library - reducing time spent sifting through publications and enabling faster access to actionable insights in agriculture and environmental challenges.

Demonstrates how precise, self-service search across 200+ medical sources allows users to get reliable answers in seconds - reducing dependency on support channels and ensuring consistent, trusted health information at scale.

Demonstrates how structured semantic search lets users quickly navigate complex ESG topics - speeding up compliance checks, and improving data discovery and understanding.

Demonstrates how a centralized, auto-tagged knowledge base gives users a single place to find accurate information - reducing duplicate content, shortening search time, and ensuring everyone works from the same up-to-date resources.

Demonstrates how integrating geospatial datasets into a unified view allows agencies to compare regions, identify patterns, and make more informed territorial planning decisions based on consistent data.

Demonstrates how linking news content to knowledge graphs enables users to explore related topics, follow evolving stories, and receive more relevant content - turning passive reading into structured, actionable insight.

Demonstrates how connecting skills and uncovering hidden relationships helps organizations identify skill gaps, match candidates to roles more accurately, and make better training and hiring decisions.

Demonstrates how structuring content with custom taxonomies and ontologies turns scattered information into a searchable, reusable asset - making it easier to find, connect, and apply knowledge across the organization.

Transform business processes such as hiring, talent management and human resources pooling into a dynamic Knowledge Graph.

Demonstrates how applying semantic technologies to buildingSMART Data Dictionary improves data consistency and validation - making it easier for Architecture, Engineering, Construction, and Operations professionals to use shared standards reliably across projects and tools.

Demonstrates how automated product attribute extraction at scale enriches product catalogs - enabling more accurate filters, better search results, and easier product discovery, while human-in-the-loop validation ensures data quality.

Demonstrates how combining NLP and knowledge graphs transforms unstructured text into structured, semantically rich data - enabling advanced analysis and improving the prediction of risks and opportunities.

Demonstrates how linking fragmented biomedical data helps researchers quickly identify potential therapeutic targets - reducing time spent integrating sources and accelerating early-stage discovery.

Demonstrates how integrating and linking electricity data enables users to query, validate, and analyze energy information in one place - making it easier to monitor trends, compare markets, and support data-driven energy decisions.