Building modular intelligence...
Swetha Prabhakar

Swetha
Prabhakar

Building the reasoning layer between language, data, and decisions. So people don’t just see information, they understand how it fits together.

I’m working toward a future where intelligence is modular — where analytical reasoning, narrative interpretation, and strategic judgment can be separated into distinct, composable systems. Much of my current work centers on a “thinking layer” for data: a system that blends semantic structure, statistical reasoning, and interpretive scaffolding so that analysis becomes both explainable and operational. Instead of dashboards, it produces traceable narratives — structured explanations that mirror how an experienced analyst reasons.

Kuromi
Current focus
Grok, Inc · Grok Personas

A modular intelligence framework where persona-driven agents express distinct modes of reasoning — built on top of My AI Analyst, a structured analytical layer that maps data into concepts, detects signal, and generates traceable, defensible insight.

It’s the reasoning layer that makes each persona’s intelligence feel real and grounded in their social intelligence.

Education
B.S., Mathematical & Computational Science
Stanford University · 2012
M.S., Financial Engineering
Stanford University · 2012
Ph.D. Candidate, Mathematics
Princeton University · Expected 2026

Structured intelligence, narrative systems, and the space between language, data, and decisions

Grok Personas extend the philosophy of My AI Analyst. They are specialized agents built on top of the thinking layer, each expressing a different configuration of these modes of cognition. Like people, each persona (e.g., Kuromi) has its own strengths and blind spots — a deliberate design choice that makes their reasoning style interpretable rather than opaque. Over time, this combination will enable a new class of tools: systems that don’t just compute results, but explain why those results matter, shaping how people think, decide, and reason across domains.

Before this, I spent more than a decade building consumer products at scale. At Snap, Inc., I led work across identity and creator monetization — including Public Stories and Profiles, Gifting/Tipping, Story Replies, and real-time payouts via Crystals. These were deeply cross-functional efforts developed with Engineering, Product Design, Content & Partnerships, Trust & Safety, Support, and more.

Earlier in my career, I founded and ran Fanvana (acquired) and worked as a data scientist at Facebook on Pages monetization — focusing on creator incentives, content performance, and ecosystem health.

I am also a PhD candidate in Mathematics at Princeton University, where my research centers on network structures, cluster dynamics, and the geometry of influence. My dissertation — informally known as Adler’s Algorithm — studies how tightly connected systems behave under stress, drawing on game-theoretic insights from esteemed mathematician John Nash. While distinct from my work at Grok, this research shapes how I think about modular intelligence, system design, and how information flows across connected agents.