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Harness semantic knowledge graph technology to transform your organization’s GenAI into a subject matter expert, capturing deep domain knowledge and complex process flows. Delivering increased informational accuracy, completeness, and reliability.
Food Service Industry
International Telecoms Provider
Professional Services Marketplace
Up to
90%
response accuracy
92%
shorter mean-time to initial resolution
Up to
86.7%
faster customer query responses
"RAG alone does not ensure the completeness and accuracy of information for mission-critical enterprise applications. Squirro quickly identified that this gap can be bridged by combining generative AI with enterprise knowledge graphs and semantic technology."
Knowledge graphs augment generative AI systems by delivering deep contextual understanding that enables them to infer meaning from information and master complex workflows. The result? Smarter, streamlined operations, improved service quality, happier customers, and a stronger bottom line.
Knowledge graphs ground your enterprise GenAI applications in rich, interconnected contextual data, providing deeper semantic understanding, enhanced search capabilities, and more accurate outputs with reduced overlooked information and blindspots.
By rendering complex workflows understandable to AI systems, knowledge graphs enable truly time-saving process automation, enabling everything from improved customer service management, accelerated incident resolution, and streamlined production line and supply chain monitoring.
Knowledge graphs enhance GenAI applications by ensuring predictable outcomes and clear understanding of conclusions. By directly linking insights to data sources, they provide clarity, accountability, and control over data retrieval, boosting accuracy and trust in decision-making.
Semantically represent enterprise knowledge to create machine-readable, inference-bearing knowledge graphs that generate new insights. These graphs enhance decision-making by organizing and connecting data in ways that allow for the discovery of new knowledge.
Discover how integrating knowledge graphs with our advanced Retrieval Augmented Generation (RAG) technology can revolutionize your enterprise.
Use a no-code enterprise taxonomy and ontology management system to model any business domain as an inference-bearing knowledge graph. Standardize enterprise terminologies in taxonomies and capture how entities are described and related in machine-intelligible knowledge models, or ontologies.
Use auto-classification for known entity extraction (inline tagging) and document level classification, automating the capture of concept-to-content annotations and generating a continuously evolving, content-aware knowledge graph.
Adhere to industry standards including ISO 25964 and W3C RDF, SKOS, and OWL to semantically represent enterprise information, enabling knowledge graphs that are machine-intelligible, inference-bearing, and capable of knowledge generation.
Guide the data retrieval process in RAG-enabled generative AI to enhance accuracy, ensure that all relevant information is identified, processed, and validated, and improve the overall quality of generated insights and automated workflows.
"Our customers span various industries, but they all share a commitment to high-quality information retrieval. They use semantic technology to build knowledge organization systems, standardize and enrich metadata, and optimize precision and recall."
Synaptica's Graphite Taxonomy and Ontology Management Tool offers a powerful no-code environment for designing, building, and managing Knowledge Organization Systems. Based on Semantic Web standards and RDF concept modeling, Graphite lets anyone build taxonomies, ontologies, and knowledge graphs that enable richer search experiences, more precisely automated process, and deeper generated insights.
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Book a free, personalized consultation with our team and see how knowledge graphs can drive the accuracy you need to transform your business and deliver game-changing results.