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Rajeev Shrivastava

CHIEF EXECUTIVE OFFICER

Rajeev brings extensive leadership experience from top technology companies. Previously, he drove significant growth and innovation at Google and NICE inContact, leading major strategic initiatives and successful mergers. His expertise in scaling businesses and fostering innovation is underpinned by an MBA from the Wharton School and a Bachelor’s degree from Delhi College of Engineering. Prior to joining TigerGraph, Rajeev was at Google, where he served as GM & Product Lead for an AI-first Customer Conversation Platform. In this role, he managed a significant P&L and led teams driving innovation and growth within Google’s expansive business landscape. Previously, Rajeev played a pivotal role in the growth of NICE inContact as their Chief Product & Strategy Officer. Prior to NICE inContact, Rajeev led go-to-market and marketplace initiatives at Rackspace.

A network diagram with icons for people, banks, houses, and locations, illustrating connections. Text reads: How Graph Analysis Finds Repeating Laundering Patterns. TigerGraph logo appears in the top left corner.
How Graph Analysis Finds Repeating Laundering Patterns
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A graphic compares isolated transaction amounts on the left with a connected network of icons (person, devices, location) on the right, illustrating how graph context reveals structuring and evasion patterns. TigerGraph logo is present.
Structuring and Evasion Patterns Become Clearer With Graph Context
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A central orange user icon is connected to smaller nodes, surrounded by six gray icons representing various business concepts. Text reads: High-Impact Graph Database Project Ideas for Modern Data Teams. TigerGraph logo is in the top left.
High-Impact Graph Database Project Ideas for Modern Data Teams
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An infographic from TigerGraph shows a comparison: on the left, nodes linked in a network labeled Before point to a central LLM circle; on the right, nodes with icons branch from LLM. Text reads, Should Graphs Power AI Before or After the LLM?.
Should Graphs Power AI Before or After the LLM?
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A diagram showing three stages: a connected graph labeled Graph, a grid labeled Vectorization, and a head with a gear labeled LLM. Text below reads, Agentic GraphRAG Gives AI a Playbook for Smarter Retrieval.
Agentic GraphRAG Gives AI a Playbook for Smarter Retrieval
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A graphic showing time series signals turning into graph context, with icons for data, sensors, and networks, alongside the text Time Series Database Fundamentals in Modern Analytics. TigerGraph logo is in the top left.
Time Series Database Fundamentals in Modern Analytics
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A blue graphic showing AI in a circle, connected documents and banks, and a human head silhouette with financial icons. Text reads: Static Models Miss the Signal – Jefferies First Brands fraud graph intelligence. TigerGraph logo in the corner.
How Jefferies’ First Brands Scandal Exposed the Limits of Static AI and Why Graph Intelligence Is the Future of Financial Risk Detection
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A person in a hoodie with headphones works on a laptop in a dimly lit room; overlay text reads TigerGraph Cybersecurity Use Cases with a shield icon in an orange circle in the center.
Smarter Threat Detection Starts with Connected Security Analytics
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