A knowledge graph is a network of entities, their semantic relationships, and associated properties organized in a graph structure. It represents information in context, much like human knowledge. For your organization, it can connect siloed data across departments, enable more accurate search and discovery, power AI with real-world context, and facilitate data-driven decisions by providing a comprehensive view of your enterprise knowledge.

Implementation timelines vary based on project scope and data complexity. We typically deliver initial proof-of-concepts in 4-6 weeks, allowing you to see value quickly. Full enterprise implementations generally take 3-6 months, following our phased approach: domain modeling (2-4 weeks), data integration (4-8 weeks), knowledge graph construction (4-6 weeks), and application development (4-8 weeks). Our agile methodology ensures you see incremental value throughout the process.

Knowledge graphs significantly improve LLM performance in several ways: they provide factual grounding to reduce hallucinations by up to 60%, enable reasoning over enterprise-specific knowledge that isn't in LLM training data, support retrieval augmented generation (RAG) with structured context rather than just text, allow for verifiable answers with sources and provenance, and enforce business rules and constraints during generation. Our integrated solutions combine the natural language capabilities of LLMs with the structured knowledge and reasoning of graphs.

Knowledge graphs can integrate virtually any data source, including structured databases (SQL, NoSQL), semi-structured data (JSON, XML), unstructured content (documents, emails, reports), external APIs and web services, real-time streams from IoT devices or transactions, and third-party knowledge sources. Our platform includes over 100 pre-built connectors and a flexible integration framework to create custom connectors. We handle data in any format, at any scale, and establish semantic mappings to create a unified knowledge layer across all your information assets.

While knowledge graphs provide value across all sectors, we've seen particularly transformative results in financial services (for risk assessment, compliance, and investment intelligence), healthcare (for drug discovery, clinical decision support, and patient care), manufacturing (for supply chain optimization and predictive maintenance), retail (for recommendation systems and customer insights), and government (for intelligence analysis and regulatory oversight). Any organization with complex, interconnected data can benefit from a knowledge graph approach.

Our knowledge graph solutions are designed for enterprise-scale performance, handling billions of nodes and relationships. We employ distributed architecture, horizontal scaling, and advanced partitioning strategies to maintain high performance even with massive datasets. Our largest implementation manages over 50 billion triples with sub-second query response times. Additionally, our platform includes built-in performance monitoring, query optimization, and evolutionary design patterns that allow your knowledge graph to grow seamlessly with your organization.

Yes, we provide comprehensive support through our Continuous Knowledge Services. This includes 24/7 monitoring and incident response, regular health checks and performance optimization, ontology evolution and data quality management, knowledge engineering advisory services, and feature updates and enhancements. We offer tiered support plans (Standard, Premium, and Enterprise) with SLAs ranging from same-day to 1-hour response times. Our goal is to ensure your knowledge graph continues to deliver value and adapt to your evolving business needs.

Unlike traditional databases that store data in rigid tables with predefined schemas, knowledge graphs use a flexible graph structure where relationships between entities are first-class citizens. This offers several advantages: dynamic schema evolution without downtime, native support for complex relationship queries, semantic understanding of data meaning (not just values), built-in inference capabilities for discovering implicit knowledge, and the ability to integrate heterogeneous data without extensive transformation. Knowledge graphs complement rather than replace databases, adding a semantic layer that brings context and meaning to your enterprise data.