Graphon Labs Raises $35M Series A to Scale Persistent AI Memory

Graphon Labs announced a $35 million Series A on November 10, 2026 to scale persistent-memory infrastructure for AI agents across enterprise, developer and on-device products. RRE Ventures led the round, with HP, Energy Impact Partners, Hitachi Ventures and Gaia VC participating; Graphon’s Series A release put total funding at $43.3 million.

Chief executive Arbaaz Khan said the capital will move the company from early experimentation to scaled enterprise deployments, open its infrastructure to developers, accelerate research and support on-device memory.

RRE general partner Raju Rishi said the firm sees Graphon becoming a source of truth beneath agents, explaining: “Models can reason, and agents can act, but neither gets better if they cannot build off of prior knowledge.”

How Graphon’s Memory Architecture Works

Graphon calls its approach Persistent Intelligence, meaning AI that can continuously access, understand, remember and build on earlier information. The company says retrieval-augmented generation, GraphRAG, context windows and conventional retrieval can compress or fragment relationships, history and temporal understanding as information grows and changes. Its platform instead ingests video, images, audio and documents, automatically building an ontology—a structured representation of the data and its connections—while retaining links to sources.

Khan’s technical explanation divides the architecture into two pieces: a detailed graph representing information and relationships, and a compressed graphon representation that guides retrieval through that larger structure. Graphons are a mathematical framework for very large graphs, and the founders developed the underlying research during their PhDs at Penn’s GRASP Lab. The engineering goals include incorporating new information, tracing evidence back to its origin and operating at practical cost and speed.

Developers use S3-style storage and search concepts such as buckets, objects, files and queries without needing to understand the mathematics. The interface supports keyword, semantic and image searches, plus agent-assisted questions across multiple sources. A simple lookup and a deeper investigation therefore draw on the same platform, reducing the need to assemble a separate retrieval stack for each kind of question.

Graphon reports 81.7% accuracy on ATM-Bench, a University of Cambridge benchmark for question answering across video, audio, images and text. The reported result is 5.0 percentage points ahead of the next system on the public leaderboard.

Products And Early Enterprise Use

The architecture supports three products. Graphon Enterprise provides governed memory across enterprise data and can be hosted by Graphon, deployed in a Graphon-managed or customer-managed virtual private cloud, or installed on-premises. Graphon Developer is intended for builders working across data types without recreating the context stack, while Graphon On-Device Memory targets phones, laptops, cameras, wearables and other devices.

Adoption differs among the customers named in the announcement. Although the release broadly describes deployments with the San Francisco 49ers, Hitachi and other large enterprises, 49ers technology executive Costa Kladianos said the organization is exploring applications such as uncovering sponsorship opportunities and supporting internal teams. Hitachi Rail’s Amaury Jourdan described an evaluation showing promise for contextual-knowledge discovery, automated data-source connections and improved AI inference in its industrial setting.

Graphon’s company profile lists July 2025 as its founding date and Khan, chief technology officer Clark Zhang and chief operating officer Deepak Mishra as its founders. The San Francisco company emerged from stealth on May 14, 2026 with an $8.3 million seed round led by Arvind Gupta of Novera Ventures.

The seed announcement identified South Korean conglomerate GS as an early enterprise customer. Its representative cited applications including analysis of customer movement in convenience stores and CCTV-based construction-safety work.

The Series A therefore funds a commercialization test as well as a research program. Graphon must turn exploratory enterprise work into scaled deployments, persuade outside developers to build on its memory layer and adapt the same architecture for private on-device use.