Our mission is to build the foundational representation of the real world to enable generalizable intelligence.
What Graphon is
Graphon is a multimodal memory platform. It ingests videos, images, audio, and documents using patent-pending technology, and goes deep into the data to automatically build an ontology.
The result is roughly 10x deeper than what a person could reasonably build by hand. That depth means you are not limited to searching for broad entities like “blue shirts.” You could ask for “blue shirts with five buttons, two pockets, and short sleeves,” and see exactly where each result came from.
The technology uses graphons, a mathematical framework for understanding very large graphs. The founders developed the underlying work during their PhDs at Penn's GRASP Lab.
The problem we're solving
Modern AI models are powerful, but still constrained by limited context.
Enterprises store trillions of tokens across documents, videos, logs, databases, meetings, and internal systems. Traditional retrieval systems like RAG can surface relevant snippets, but they cannot understand how information connects across datasets, modalities, and time.
They can retrieve. They cannot relate.
As a result, AI agents and agentic systems can answer questions about isolated pieces of data, but struggle to reason across the full organizational context needed for accuracy and reliability.
How we're different
from RAG, GraphRAG and hand-built ontologies.
Existing systems retrieve isolated context. Graphon models how that context connects, as a persistent relational memory that lets AI reason across multimodal data at scale. We call it relational context intelligence.
Accuracy
Where RAG, GraphRAG and hand-built ontologies land.
Our patent-pending technology, on the same data.
Time to build
Modelled by hand, and open to human error.
Built automatically, with no modelling step.
Maintenance
Someone has to keep the model current as the data moves.
The ontology extends itself as new data lands.
The work rests on graphons, a mathematical framework for representing very large graphs. Our founders developed the underlying research during their PhDs at Penn's GRASP Lab, and Christian Borgs, a co-creator of graphon theory, advises the company.
Team and vision
Leadership
Graphon AI was founded by researchers and engineers with deep expertise in AI, machine learning systems, and large-scale enterprise infrastructure. The leadership team includes former researchers and engineers from Amazon, Meta, Google, Apple, PayPal, NVIDIA, Rivian, Samsung AI Center, NASA, and leading research institutions.
Founders
Arbaaz Khan · Founder & CEO
5+ years as Senior Applied Scientist, serving 10M+ customers. PhD and Masters in ML and Robotics.
Clark Zhang · Co-Founder & CTO
5+ years as Senior Machine Learning Engineer. PhD and Masters in ML and Robotics.
Deepak Mishra · Co-Founder & COO
5+ years as AI VC. 5+ years as Data Scientist and Consultant.
Technical advisors
Jennifer Chayes
Dean, College of Computing, Data Science, and Society, UC Berkeley
Christian Borgs
Professor of Computer Science, UC Berkeley, Co-creator of graphon theory
Alejandro Ribeiro
Professor, University of Pennsylvania ESE Department
Business advisors
Bask Iyer
Ex-CIO of VMWare, Honeywell, and Juniper Networks
Alan Fletcher
Ex-COO of Regrello (acquired by Salesforce)
Simon Mulcahy
Ex-Chief Innovation Officer of Salesforce
Akash Bhatia
Senior Partner and Global Tech Sector Lead at BCG
Key facts
- Company
- Graphon AI
- Founded
- 2025
- Founders
- Arbaaz Khan (CEO), Clark Zhang (CTO), Deepak Mishra (COO)
- Location
- San Francisco Bay Area
- What it is
- A multimodal memory platform
- Reads
- Video, images, audio and documents
- What it builds
- An ontology of the data, automatically, roughly 10x deeper than one built by hand
- Technology
- Patent-pending, built on graphons, a framework for very large graphs
- Research origin
- PhD research at Penn's GRASP Lab
- Funding
- $8.3M raised
- Deployment
- Graphon cloud, your own VPC, or on device
- Security
- SOC 2 Type II
- Getting started
- Free to start, no card needed
- Website
- graphon.ai
- Contact
- hello@graphon.ai
Frequently asked questions
Is Graphon a RAG system?
No. RAG retrieves snippets that look relevant to a question. Graphon builds a persistent, relational memory of how your content connects, across modalities and over time, so AI can reason across all of it instead of over isolated pieces.
What kinds of data does Graphon read?
Video, images, audio and documents. It reads the content itself, the frames, the speech and the pages, rather than the file names and labels around it, and every answer shows where it came from.
How is it different from building an ontology by hand?
Graphon builds the ontology automatically as it reads, roughly 10x deeper than a team could reasonably model by hand. So you can ask for "blue shirts with five buttons, two pockets and short sleeves," not just "blue shirts."
Where does my data live?
Wherever it is allowed to. Graphon runs in our cloud, inside your own VPC, or on a device next to where the data is made, with nothing leaving the building.
Is Graphon secure?
Graphon is SOC 2 Type II certified. The live control status, and the reports on request, are on the Graphon Trust Center.
How do I try it?
You can start free, with no card needed, or book a demo and the team will walk you through it.
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