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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.

AI Slop Happens When AI Loses Realit
AI Slop Happens When AI Loses Reality
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Digital network diagram with nodes on the left connecting to a central point, next to text: If You Can’t Trace It, It’s Not a Decision—highlighting the importance of traceable AI decisions. TigerGraph logo in the top left. Source: McKinsey & Company, Rewired (2026).
McKinsey Highlighted the Risk. Most AI Decisions Still Can’t Be Proven
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A network of blue nodes connects to a central hub, contrasted with the text: Most Systems are Still Just Sequences. TigerGraph logo at the top left; source noted as McKinsey & Company, Rewired (2026) at the bottom right.
McKinsey Described the Agent Factory. Most Systems Are Still Just Sequences.
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Dark background with TigerGraph logo, scattered blue nodes converging into an orange circle, and text Context Defines the Decision. Source: McKinsey & Company, Rewired (2026).
McKinsey Is Right: AI Needs Context. Almost No One Has It.
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A digital graphic for TigerGraph features the text “Data isn’t the problem. Context is.” over a network graph with orange nodes forming an upward trend. Smaller text reads “Less noise. More signal.” TigerGraph’s logo appears in orange.
Data Isn’t the Problem. Context Is.
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A graphic shows an AI stack with five layers: Applications, LLMs, Vector Databases, Relationship Runtime for AI (highlighted), and Data. Text reads, Your AI stack is missing a layer. Add the relationship layer. TigerGraph logo included.
The Missing Layer in Your AI Stack
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An infographic compares Tokenmaxxing and Inference Yield leaderboards, showing top performers change as metrics shift. Tokenmaxxing lists Alex Kim, Taylor Morgan, Jamie Patel; Inference Yield lists Maya Johnson, Jordan Lee, Priya Shah.
Tokenmaxxing is a Phase. Inference Yield is the Strategy.
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Infographic compares Vector RAG and GraphRAG token usage: scattered blue dots (high compute/power) vs. connected orange nodes (lower compute/power). Learn how to reduce AI inference cost and tokens—stop wasting resources today!.
AI Is Facing a 19-Gigawatt Power Gap. Here’s the Fix.
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