News
Research Report
The AI Transformation 100: Concrete Ideas for Fixing How We Work
Key Takeaways
- Graphon AI raised $8.3 million in seed funding to build a pre-model intelligence layer for enterprise AI.
- The platform maps relationships across video, audio, documents, images and databases before data reaches an AI model.
- Graphon says its technology helps LLMs and AI agents reason over enterprise data without relying only on context windows.
- GS Group is already using Graphon’s technology for retail movement mapping and construction-site safety analytics.
Graphon AI has raised $8.3 million in seed funding to build an infrastructure layer designed to help AI models understand relationships across massive enterprise datasets before that data reaches a foundation model.
The San Francisco-based startup emerged from stealth with backing from Novera Ventures, Perplexity Fund, Samsung Next, GS Futures, Hitachi Ventures, Gaia Ventures, B37 Ventures and Aurum Partners. The round was led by Arvind Gupta, founder and managing director of Novera Ventures.
Table of Contents
- LLMs Need Structure, Not Just More Tokens
- How Graphon's Platform Works
- Why Investors Are Paying Attention
- Graphon Plans to Scale Its Enterprise AI Platform
- GS Group Already Using Graphon’s Technology
- Graphon Says It Amplifies, Not Replaces, LLMs
LLMs Need Structure, Not Just More Tokens
Graphon is developing what it calls a “pre-model intelligence layer” for enterprise AI. The company says its platform uses mathematical graphon functions to identify hidden relationships across video, audio, documents, images and structured databases.
The goal is to give large language models and AI agents a better understanding of connected enterprise data without forcing all of that information into a model’s context window.
That limitation has become a growing concern for companies trying to apply AI to large, messy and multimodal datasets. Even advanced models can only process a limited amount of information at once, while enterprises often need AI systems to reason across years of files, logs, videos, sensor data and business records.
How Graphon's Platform Works
Instead of operating inside a model’s context window, Graphon says its software works before the model sees the data. The system maps relationships across different data types, creating a persistent representation of how information connects.
That structure can then be used by third-party foundation models and autonomous agents.
According to the company, the approach can support:
- Enterprise content management across video, audio, images and documents
- Industrial intelligence for spotting process gaps, safety risks and compliance issues
- Agentic workflows that depend on rich multimodal inputs
- On-device AI using data from phones, cameras, wearables and connected systems
The platform is designed to work with any foundation model or agent framework.
Why Investors Are Paying Attention
Graphon’s pitch lands in the middle of a much-discussed enterprise AI problem: companies want AI systems that can reason over business operations, not just retrieve individual pieces of information.
Retrieval-augmented generation, or RAG, can help models find relevant content. But retrieval alone, according to Graphon, is not enough for tasks that require understanding relationships across datasets.
“Graphon changes where the intelligence happens,” Gupta said. “Most companies are trying to build ever-larger models. Graphon is improving the layer between raw enterprise data and the model itself. That gives today’s foundation models a much better understanding of complex data—and makes them far more capable without needing to be bigger.”
Graphon Plans to Scale Its Enterprise AI Platform
Graphon said it will use the funding to build out its relational representation platform, advance its continuous multimodal graph technology and scale infrastructure for enterprise-wide AI agent frameworks.
The company is led by founder and CEO Arbaaz Khan. Its leadership team and advisory board include former AI, robotics and engineering talent from Amazon, Meta, Google, Apple, NVIDIA, Samsung AI Center, NASA, MIT and Rivian.
Jennifer Chayes, Dean of the College of Computing, Data Science and Society at UC Berkeley and Christian Borgs, the UC Berkeley computer science professor who coined the term "graphon," are also advising the company.
GS Group Already Using Graphon’s Technology
Graphon has already launched production deployments with GS Group, one of South Korea’s largest conglomerates.
The company said GS is using Graphon’s technology to analyze customer movement in retail environments and support real-time safety analytics at industrial construction sites.
“Graphon has been an invaluable partner in GS Group's AI transformation journey, bringing exceptional passion and AI expertise to the table,” said Ally Kim, vice president at GS. “Their multimodal AI solutions have been instrumental in solving real-world challenges, such as analyzing customer movement in convenience stores and enhancing safety through CCTV analysis at construction sites.”
Graphon Says It Amplifies, Not Replaces, LLMs
The goal, according to Khan, is not to replace foundation models. Instead, they improve what those models can do by preserving the structure of enterprise data before a model begins reasoning over it.
Webinar
May
19
From Content Sprawl to Competitive Advantage: A Knowledge Operations Roadmap
How automation, AI and a centralized content platform turn knowledge fragmentation into a scalable competitive advantage.
Register
Webinar
May
20
AI With Intent: How CX Leaders Decide Where Automation Belongs (And Where It Doesn't)
Senior CX leaders from REI & Hyatt reveal their framework for using AI to boost retention.
Register
Webinar
May
21
Content Strategy Leaders Live: Managing Scale, Safety & AI in Manufacturing
How manufacturing leaders update content systems to handle product complexity and scale while integrating AI safely.
Register
Webinar
Jun
2
From AI Investment to CX Results: What Enterprise Leaders Need to Know
Move beyond experiments. See how top enterprises scale AI for CX results.
Register
Webinar
Jun
16
From Legacy to Launch-Ready: How Gainbridge Made Its Website a Marketing-Led Growth Engine
Join in to learn how a D2C annuity brand gave marketing full website ownership — without slowing down or risking compliance.
Register
Webinar
On demand
How Marketing Teams Can Take Control of the Web Experience
See how Veo and Kvalifik put marketing in the driver's seat, shipping faster and growing qualified bookings by 150%.
Watch Now
“AI has spent the last decade learning to mimic language,” Khan said. “But the world isn’t made of tokens, it’s made of relationships. By preserving that structure, we make foundation models more accurate and more useful at enterprise scale. An LLM with Graphon is better than an LLM alone. We’re not replacing models – we’re amplifying them.”