Persistent Intelligence.

AI that remembers what it has read, heard, seen and understands how it all connects.

Set up Graphon with your agent
Then ask your agent to build
Generational memory

Train bigger models to get to AGI.
We went a different direction.

the model side

Bigger models

More parameters, more compute, more training runs. Where every lab is racing.

the data side

What models draw from

Where Graphon pushes: read, relate, remember and retrieve everything, losslessly.

Finite context windows.

Every fact, every relation,
every source, kept.

Persistent
Intelligence

Graphon beats every existing memory system on accuracy and on cost, while being truly multimodal across video, audio and text. It sets a new cost-accuracy Pareto frontier.

ATM-Bench ↗

The first benchmark for multimodal, multi-source personalized referential memory QA over long time horizons (~4 years), with evidence-grounded retrieval and answering.

Graphon is #1 and #2 on the leaderboard.

ATM-Bench / memory systems better
QA score (%)Recall@10 (%)
Best run per system, from the public ATM-Bench leaderboard. The models behind each run and the full results are on the leaderboard. GraphonOthers
Source: atmbench.github.io/leaderboard ↗

Four powerful APIs in one go

Dev Docs →
A tidy cabinet of small drawers standing in a sunlit meadow, one drawer cyan.

Storage

S3 compatible fast, reliable, and secure file storage.

A lighthouse beam crossing the sea to land on one small cyan boat.

Search

Ultra fast keyword and semantic search over markdown, json, pdf, images, videos. Zero config. Replaces elastic search in your dev stack

An open book in a field, with threads running back to a letter, a film reel, a photograph and a record.

Answer

Natural language question and answer API for humans and agents with source citations. Zero config, SOTA accuracy and speed.

A figure with a telescope above a patchwork valley traced by a cyan river.

Research

Powerful deep research question and answer for complex research and analysis across TB of data.

how it works

From points to a shape.

the context problem

Gigabytes of multimodal data.

Video, audio, PDFs, text, markdown, JSON and spreadsheets. One square is one file: 1,024 here, millions in practice.

the usual way

Vector search sees points.

It cuts your data into chunks, turns each one into a point, and returns the nearest few. Everything between the points (the reasons, the sequence, the relations) is gone.

12 points. the rest? gone.
relate

Graphon reads it all at once.

Graphon builds one large graph of your data, then fits a graphon to it: the limit object of that graph, a single function that maps the topology of the full graph.

global structure
across every file, not chunk by chunk
inferred schema
learned from the data, no ontology to draw
scope
the whole corpus
strong linkweak linkacross topics
there’s always one that’s late →
remember

Graphon learns the shape between them.

A graphon is the continuous limit of a graph: one smooth function over all of your data instead of a pile of points. Nothing between the points is lost.

← this is the good part
retrieve

Ask, and the graphon guides the search.

A question first finds a few matches. From there the graphon steers the search toward what is connected to them, until it has every source the answer needs.

How do we set the charger voltage on the APS-200 auxiliary converter?
manual.pdf · p. 47training.mp4 · 12:40ticket #4471

The manual says 110 V, set in menu 4.2. The training video adds a step: isolate the 750 V supply and wait five minutes first. A ticket notes units past serial 2400 read 2 V high, so set those to 108 V.

it shows its work.
fig. 1 — 32 × 32

Memory that persists.

Book a demo →← we’ll remember you said hi
plate iiisome things are worth remembering.