"Graphon AI emerged from stealth on Wednesday with $8.3 million in seed funding to build a “pre-model intelligence layer” that discovers relationships across multimodal enterprise data before it reaches a foundation model. The round was led by Novera Ventures, with participation from Perplexity Fund, Samsung Next, GS Futures, Hitachi Ventures, and others. The company is named after a mathematical concept co-formalised by its technical advisors, UC Berkeley professors Jennifer Chayes and Christian Borgs."
"A graphon is the limit of a sequence of dense graphs: a continuous function that captures the structure of relationships as networks grow infinitely large. It is the kind of concept that exists at the boundary between pure mathematics and theoretical computer science, and it is now the foundation of a startup that claims to have built the missing layer between enterprise data and the models that are supposed to make sense of it."
"Today's large language models can process roughly one million tokens at a time. Enterprises hold trillions of tokens across documents, video, audio, images, logs, and databases. Retrieval-augmented generation, the current standard approach, can surface relevant content from that mass, but it cannot discover relationships between pieces of data that were never stored together. An LLM using RAG can answer a question about a specific document."
"Early customer GS Group (South Korean conglomerate) has deployed Graphon for convenience-store analytics and construction-site safety. Founded by Arbaaz Khan (CEO), Deepak Mishra (COO), and Clark Zhang (CTO), with team members from Amazon, Meta, Google, Apple, NVIDIA, and NASA."
Graphon AI emerged from stealth with $8.3 million in seed funding to build a pre-model intelligence layer that finds relationships across multimodal enterprise data before it reaches foundation models. The company is named after a graphon, a mathematical object defined as the limit of a sequence of dense graphs, representing continuous structure in growing networks. Large language models can handle about one million tokens, while enterprises contain trillions of tokens across documents, video, audio, images, logs, and databases. Retrieval-augmented generation can retrieve relevant stored content but cannot discover relationships between data that were never stored together. Graphon AI aims to bridge that gap by uncovering cross-data relationships prior to model inference. Early customer GS Group uses it for convenience-store analytics and construction-site safety.
#graphon-ai #pre-model-intelligence #multimodal-enterprise-data #retrieval-augmented-generation #foundation-models
Read at TNW | Artificial-Intelligence
Unable to calculate read time
Collection
[
|
...
]