Product Name: TigerGraph
Company Name: TigerGraph, Inc.
URL: https://www.tigergraph.com
Entry Year: 2012

Market Share (or Graph DB Revenue):
TigerGraph is a rapidly growing player in the graph database space, with strong traction in enterprise markets. While specific revenue figures are not disclosed, it has established a significant presence in industries such as healthcare, finance, and telecommunications.

Number of Employees: Approximately 300-400 employees

Capital: Not publicly disclosed

Funding:
TigerGraph has raised over $170 million in funding, with the most recent Series C round securing $105 million in February 2021. Investors include Tiger Global, Dell Technologies Capital, and SIG.

Major Users:
TigerGraph’s customers include notable organizations such as Jaguar Land Rover, UnitedHealth Group, Intuit, Xandr (a division of AT&T), and GSK. It is also widely used in industries such as finance, healthcare, manufacturing, retail, and telecommunications.

Key Application Areas:
TigerGraph is applied in fraud detection, customer 360 views, supply chain optimization, enterprise knowledge graphs, recommendation engines, cybersecurity, and financial risk management.

Product Overview:
TigerGraph is a distributed, native graph database designed to handle massive volumes of data and complex graph analytics at scale. It focuses on real-time deep link analytics and enables enterprises to model and analyze highly interconnected datasets. TigerGraph supports large-scale applications, including machine learning, by combining graph traversal and analytical queries in a unified platform.

Data Compatibility:
TigerGraph supports various data formats such as CSV, JSON, and integrates with relational databases, data lakes, and ETL pipelines. It offers connectors for Apache Kafka, Spark, and various business intelligence (BI) tools. Additionally, TigerGraph provides APIs for integration with external systems.

Knowledge Graph Implementation:
TigerGraph is used extensively to build enterprise knowledge graphs. It supports flexible modeling and storage of entities and relationships, allowing organizations to unify disparate data into a connected knowledge graph. The database is optimized for large-scale, real-time querying and analytics.

Query Method:
TigerGraph uses GSQL, a highly expressive and parallelizable graph query language. GSQL supports both transactional and analytical queries, making it well-suited for real-time analytics on complex graph datasets.

Natural Language Queries:
TigerGraph does not natively support natural language queries, but integrations with third-party NLP tools can allow for natural language inputs to be processed and translated into GSQL queries. Some applications also implement custom NLP layers for domain-specific querying.

Native Machine Learning:
TigerGraph offers built-in support for graph-based machine learning, enabling deep link prediction, node classification, and embedding generation. Its Graph Data Science (GDS) features allow machine learning models to operate directly on graph structures, improving the ability to discover patterns and relationships in data.

Support for Traditional Machine Learning:
TigerGraph can integrate with traditional machine learning frameworks like TensorFlow and scikit-learn. Its graph analytics engine can extract features from the graph, and those features can be used as inputs for traditional ML models, enriching model accuracy with relationship data.

Support for LLMs:
TigerGraph is exploring integration with large language models (LLMs) to enhance its capabilities in unstructured data processing and semantic search. This integration is particularly useful for improving knowledge graph enrichment and expanding the scope of data analytics.

Support for RAG (Retrieval-Augmented Generation):
TigerGraph’s real-time analytics and query capabilities make it well-suited for use in RAG models. By providing structured, contextually relevant data to LLMs for text generation, TigerGraph can enhance the relevance and accuracy of generated responses in retrieval-augmented generation systems.

Other Notable Features:

• Distributed architecture: TigerGraph’s distributed architecture allows for scaling across billions of nodes and edges, supporting high-performance graph analytics.
• Real-time analytics: TigerGraph is optimized for real-time analytics on massive graph datasets, with capabilities to process complex graph traversals in milliseconds.
• GraphStudio: A visual tool for designing and querying graph models, accessible to both technical and non-technical users.
• TigerGraph Cloud: A fully managed, scalable graph database-as-a-service, available on cloud platforms like AWS, Microsoft Azure, and Google Cloud.
• Security: Role-based access control (RBAC) and integration with enterprise-grade security protocols ensure secure graph database management.