AI Platform
The Graphwise AI platform combines knowledge graph technology with generative AI to unify and query structured and unstructured data through a semantic layer. It uses graph modeling, semantic enrichment, and large language models to automate knowledge management, improve AI accuracy, and enable natural language interactions for search, recommendations, and analytics. By integrating Retrieval Augmented Generation (RAG), ontology management, and multi-modal data, Graphwise helps organizations gain insights, enhance decision-making, and scale AI-driven processes across various domains.
Components of the Graphwise AI Platform
Graph AI Services
Graphwise AI Services consist of a set of capabilities that enable the creation of downstream Graph AI applications for natural language querying, Graph RAG, conversational agents, process agents, and Graph AI assisted search and recommendation systems. Upstream Graphwise Ai Services provide support and semi-automation for graph model generation and reasoning.
GraphDB
GraphDB is a semantic graph database for managing large knowledge graphs using RDF and SPARQL. It offers reasoning capabilities and integrates with applications like Elasticsearch for semantic search. Highly scalable, GraphDB supports real-time inferencing and query evaluation under massive data loads. It performs well across on-premise and cloud environments, making it suitable for industries where the stakes are high
Graph and Text Analytics
Search and Recommendation
Graphwise Search and Recommendation are highly configurable, allowing the user to display information according to their specific needs. With Graphwise Search and Recommendation, users can benefit from knowledge retrieval that understands the user’s natural language, where faceted search helps to narrow down results, and recommendations are based on intent and context.
UnifiedViews
PoolParty UnifiedViews is an ETL framework that integrates diverse data into a knowledge graph, supporting RDF and ontologies. It offers a graphical interface for creating data processing pipelines, allowing users to transform data into RDF and perform cleansing and validation. UnifiedViews supports features like SHACL for error detection and SPARQL queries for further data processing.
Graph Modelling
Graphwise award-winning graph modeling tool delivers 1st class taxonomy and ontology management. It offers an intuitive, centralized platform to create, link, and manage knowledge structures, with generative AI recommendations and easy integration. By structuring and connecting enterprise data, PoolParty transforms siloed information into actionable insights, supporting AI-driven analytics and business growth.
Refine
BI semantic layers focus on consistent data views for BI tools, using relational databases and SQL. Knowledge graph semantic layers represent data as interconnected entities, enabling reasoning and discovery via knowledge graph tech like RDF and SPARQL. Combining both leverages their strengths for comprehensive solutions.
Knowledge as a Service
The platform built to serve all your AI use cases
Graph RAG
Position
A Graph RAG design pattern combines symbolic AI and generative AI for better domain fidelity and fewer hallucinations. It uses NLP and semantic search to ensure that documents are found based on the intent of the user's query.
Agentic AI
Position
Agentic AI enables autonomous systems to make decisions and perform tasks across multiple platforms. In AI Solutions Builder, it supports multi-agent LLM searches over GraphDB, Elasticsearch, and third-party agents.
Conversational AI
Position
It provides accurate, context-rich answers traceable to specific sources, enhancing trust. It streamlines content and product management, automates routine tasks, and improves employee satisfaction by enabling focus on meaningful work.
Graph NLP
Position
The most precise method to create knowledge from unstructured data. Extract relevant terms from a text with a tool that is able to disambiguate language discrepancies and the context of document.
Natural Language Quering
Position
Natural Language Querying (NLQ) allows to access complex data using everyday language without technical skills. A Semantic layer unifies metadata and data sources, simplifying data discovery and improving analytics accuracy.
Highlighted features included in the Graphwise AI Platform
Taxonomy Advisor
Speed up the taxonomy creation process and improves its enrichment by using LLMs to generate narrower concepts and alternative labels. The LLM is integrated with PoolParty so the suggestions can be added easily to the taxonomy.
Talk to your Graph
TTYG eliminates the need for technical query skills, streamlines data discovery, and empowers business users to generate insights quickly and confidently, all while ensuring semantic consistency and data governance across the organization.
LLM grounding
Graph grounding of LLM allow for richer contextual
understanding, multi-hop reasoning, and explicit entity recognition, overcoming many of the issues vector-only systems face.
Entity Extraction
Workflow Management
Quality ensurance
- Monitor the progress of a pipeline
- Extensive debugging tools to improve data at any point of the pipeline
Knowledge Retrieval
Lab and Workbench
Services:
Access to the Graphwise Help Desk
Detailed Help documentation
Best Practice Configurations:
Once you have decided to use Graphwise it is time to find the configuration that best suits your needs. A key to this is the so-called multi-tiering, which is the use of Graphwise licenses on a distributed infrastructure.