List of TigerGraph DB Customers
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United States
Since 2010, our global team of researchers has been studying TigerGraph DB customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased TigerGraph DB for Database Management from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using TigerGraph DB for Database Management include: JPMorgan Chase, a United States based Banking and Financial Services organisation with 317233 employees and revenues of $180.60 billion, Jaguar Land Rover UK, a United Kingdom based Automotive organisation with 38379 employees and revenues of $35.61 billion, Intuit, a United States based Professional Services organisation with 18200 employees and revenues of $18.83 billion and many others.
Contact us if you need a completed and verified list of companies using TigerGraph DB, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The TigerGraph DB customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Intuit | Professional Services | 18200 | $18.8B | United States | TigerGraph | TigerGraph DB | Database Management | 2020 | n/a | In 2020 Intuit implemented TigerGraph DB as a core Database Management platform to serve as the foundation for enterprise knowledge graphs that drive Customer 360, fraud and risk modeling, and in database machine learning feature generation. TigerGraph DB was positioned to centralize connected customer and transaction signals to support detection and personalization workflows across analytics and risk teams. Implementation work centered on building graph schemas and in database feature pipelines that enable traversal, feature aggregation, and real time scoring without exporting large datasets. Functional capabilities implemented include knowledge graph modeling, in database ML feature generation, graph analytics and query based feature serving to downstream risk and personalization models. Configuration emphasized high performance graph traversals and in database feature engineering to support iterative model development. Operational scope included fraud, risk, and customer analytics teams within Intuit, where TigerGraph DB supplies features and graph derived signals into detection pipelines and Customer 360 services. The deployment supported both offline feature generation for model training and low latency in database scoring for production detection and personalization use cases. Intuit reports that after switching to TigerGraph DB they detected approximately 50 percent more risk events while improving precision by approximately 50 percent, and achieved a 77 percent reduction in infrastructure operating cost. Governance and rollout centered on standardizing graph schema and feature definitions across analytics teams to ensure consistent signals for fraud and personalization models. | |
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Jaguar Land Rover UK | Automotive | 38379 | $35.6B | United Kingdom | TigerGraph | TigerGraph DB | Database Management | 2020 | n/a | In 2020, Jaguar Land Rover UK implemented TigerGraph DB to accelerate supply chain planning and connected analytics. The deployment addressed parts and supplier relationship modeling to enable faster decisioning in the UK supply chain, and is classified under Database Management. TigerGraph DB was provisioned via Google Cloud Marketplace and configured as a graph database for connected analytics, supporting schema modeling, data ingestion pipelines, and accelerated query execution for multi-hop relationship queries and supplier risk profiling. The implementation centered on functional capabilities common to graph-based Database Management such as relationship traversal, pattern matching, and analytical query orchestration to support scenario analysis across parts and assemblies. Outputs from TigerGraph DB were integrated into the enterprise BigQuery and Tableau reporting stack to feed dashboards and reporting pipelines used by procurement and supply chain planning teams. Integration work focused on exporting analytical results into BigQuery tables and surfacing insights in Tableau for operational consumption, enabling planners to incorporate graph-derived signals into existing reporting and decision workflows. Provisioning through Google Cloud Marketplace streamlined installation while governance concentrated on data pipeline controls and role based access for supply chain users. Rollout targeted UK supply chain planning groups and supplier analytics use cases, and operational workflows were adjusted so planners consume graph analytics outputs as part of routine risk assessment and planning cycles. The implementation shortened analysis cycles from three weeks to approximately 45 minutes and lowered supplier risk by enabling faster, connected analytics, improving the speed of decisioning in Jaguar Land Rover UK supply chain operations. | |
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JPMorgan Chase | Banking and Financial Services | 317233 | $180.6B | United States | TigerGraph | TigerGraph DB | Database Management | 2019 | n/a | In 2019, JPMorgan Chase implemented TigerGraph DB in the Database Management category to power real-time fraud detection and streaming machine learning inference across transaction and account graphs. The project targeted low-latency online decisioning for fraud prevention and account protection, positioning graph-native queries at the center of transaction screening workflows. TigerGraph DB was configured to support sub-80ms response times for online decisions, enabling streaming ML inference across transaction and account graph topologies. Functional capabilities emphasized real-time graph queries, low-latency pattern detection, and inference serving for streaming models, aligned with Database Management operational requirements for high-throughput, real-time analytics. The deployment used multi-region replicas in the bank's private AWS environment, processing more than 30TB of graph data capacity while providing distributed read performance for online checks. Architecture details reported include streaming ingestion pipelines into transaction and account graphs and geographically distributed replicas to sustain low-latency inference and decisioning at scale. Reported outcomes included approximately $50M in operational savings and protection for about 60M households, aligned with sub-80ms online decisioning SLA targets. The TigerGraph DB implementation therefore combined graph database capabilities, streaming ML inference, and a multi-region private cloud deployment to operationalize real-time fraud and account protection workflows. |
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