Video
The Intelligence Infrastructureacross physical and digital worlds
Graphon finds and connects meaningful moments, people, places and things across videos, images and text, and turns them into context, so AI can actually work.
Book a demoFood, beverage and CPG manufacturing
Quality lead
Why did these go out with no label at all?
Graphon
Searched640,000line-camera clips12plants8.4 Mentities
Cited
Graphon automatically builds and maintains
a large-scale ontology.
(graphons: a mathematical framework for very large graphs, so an answer is accurate on both recall and precision)
Graphon reads video, images, audio and documents, and goes into the content itself rather than the labels wrapped around it. It builds the ontology as it reads, roughly ten times deeper than anyone would model by hand.
So you are not held to broad entities like “a bottling line.” You can ask for amber 750 ml bottles with a tamper band, a lot code printed low on the shoulder and no allergen panel, and see where each result came from.
It rests on graphons, a mathematical framework for very large graphs. The founders developed the underlying work during their PhDs at Penn’s GRASP Lab.
You can drag the slider to see how it compares.
Your graph doesn’t go deep.
It misses all the nuance, and no matter what you do it never quite works. More data only adds surface to guess from, and another ontology to model by hand.
Without Graphon3 sources · 4 entity types · stops at the entity
With Graphon5 sources · 6 entity types · 10 to 100 levels below
Replace your whole stack.
Memory, search, vector store and LLM.
Raw data in, a cited answer out, and nothing in between to buy.
We build the ontology for you. Graphons are what let us do that on very large graphs, and every piece you need comes in the box.
AgentWhich cases shipped with no label?
Nine runs, at two plants123
12Graphon
04
Inference
Answers in plain language, with the frame, page and timestamp it stands on.
03
Retrieval
Pulls the whole relevant subgraph, not three or four chunks.
02
Storage and a self-updating ontology
One dense graph, built with no modelling step and extended as data lands.
01
Extraction
Reads what happens inside every video, image and document. Not the filename.
VideoImagesAudioDocumentsText
The highest precision and recall
on petabyte-scale multi-modal data.
Everything gets harder as the corpus grows and the modalities pile up. That is where the difference shows.
Answer accuracy
precision and recall, per centGraphonOther stacksCost per answer
indexed to the smallest corpusGraphonOther stacksCorpus on a log scale, one decade per step, and the modalities grow with it, one to four.
Curious how it holds on your data?
Book a demo| Corpus | Accuracy, Graphon | Accuracy, other stacks | Cost, Graphon | Cost, other stacks |
|---|---|---|---|---|
| 1 GB | 97% | 72% | 1.0x | 1.0x |
| 10 GB | 97% | 68% | 1.1x | 1.5x |
| 100 GB | 97% | 63% | 1.1x | 2.2x |
| 1 TB | 96% | 57% | 1.1x | 3.2x |
| 10 TB | 96% | 50% | 1.2x | 4.6x |
| 100 TB | 96% | 42% | 1.3x | 6.7x |
| 1 PB | 96% | 34% | 1.4x | 9.7x |
Runs where your data
is allowed to live.
The same stack, moved to the ground you can defend: our cloud, your own VPC, or a box sitting next to the cameras with nothing leaving the building.
01
Cloud
Start indexing in our cloud. Nothing to stand up.
02
On-prem / your VPC
Indexing and querying inside your own account and network.
03
On device
The stack runs where the data is made, for footage that never leaves.
