Co-Founder & Chief Product Officer
A decade building AI and search products at the world's largest technology platforms. Now building the infrastructure that helps brands survive the shift from search to AI-generated answers.
"Brands spent a decade learning how to rank in search. They now have a three-year window to figure out AI before the citations calcify and the same ten brands own every category answer. We built Jeevan AI so every brand gets a fair shot at that window."
Background
Shiv has spent over a decade inside the organizations that defined how people find information online. At Google, he worked on core search infrastructure, developing a firsthand understanding of how ranking signals are constructed, how entity knowledge graphs are built, and why some sources get cited while others disappear. That understanding of how machines decide what is credible is the foundation everything at Jeevan AI is built on.
At Microsoft, Shiv contributed to AI product development at a time when language models were moving from research curiosity to production systems. He was part of teams building the inference, retrieval, and grounding pipelines that power today's AI answers at scale. He understands not just what AI platforms return, but why: the architecture decisions, the data weighting choices, the trust signals that determine which sources make it into a model's output and which do not.
At Meta, Shiv worked on AI-driven content and recommendation systems at a scale that few organizations in the world operate at. Understanding how billions of pieces of content get ranked, recommended, and surfaced to users trained a discipline around measurability and signal clarity that now shapes how Jeevan AI approaches brand visibility tracking.
Experience
Meta
AI & Recommendations Product
Worked on AI-driven content recommendation and ranking systems operating at billions-of-users scale. Developed deep expertise in how recommendation signals are constructed, measured, and iterated on in production environments where small signal changes have massive downstream impact.
Microsoft
AI Product & Language Model Infrastructure
Contributed to AI product development during the critical transition from traditional search to language-model-powered answers. Worked on the retrieval and grounding pipelines that determine which external sources AI platforms cite, giving Shiv a direct view into the citation architecture most brands have no access to.
Search Infrastructure & Knowledge Systems
Built within Google's core search and knowledge graph infrastructure. Gained foundational expertise in how entity understanding, domain authority, and citation trust signals are built into large-scale information retrieval systems, the same underlying principles now powering Google AI Mode and Gemini.
Domain Expertise
AI Citation Architecture
Firsthand understanding of how retrieval-augmented generation systems decide which sources to cite, built from working inside the infrastructure at two of the world's top AI labs.
Search & Knowledge Graphs
Deep technical knowledge of how entity graphs, domain authority signals, and structured data feed both traditional search ranking and modern AI answer generation.
AI Visibility Measurement
Designed the measurement frameworks that underpin Jeevan AI's tracking system: what signals to watch, how to attribute AI citation sources, and what benchmarks indicate a brand is winning or losing visibility.
Product at Scale
Experience shipping AI products to hundreds of millions of users at Meta, Microsoft, and Google, with the product discipline to build for reliability, measurability, and real-world brand impact rather than research metrics.
Why Jeevan AI
Most of the people building AI visibility tools are SEO practitioners who watched their traffic drop and looked for a new framework. Shiv came at it from the other direction. He had been inside the systems generating AI answers, and he could see exactly what brands were getting wrong because he knew what those systems were actually looking for.
The gap between what AI platforms reward and what most brand marketing teams produce is wide and growing. AI platforms cite sources with specific, verifiable, consistently-stated claims across multiple independent touchpoints. Most brand content is promotional, platform-siloed, and structured for human readers rather than machine extraction. Jeevan AI exists to close that gap: to give brand teams the visibility data they need to understand where they stand, and the guidance to build citation assets that actually work.
The window is real. The brands that build AI visibility now will hold structural citation advantages for years. Shiv built Jeevan AI because he knew from the inside that this window would not stay open, and that most brands would miss it without something telling them exactly what to do.
If you are a brand that wants to understand your current AI visibility and what it will take to improve it, we want to hear from you.