Graphon Labs

Persistent Intelligence for AI. #1 on virtually all memory benchmarks! Check us out at:

[Graphon Labs @GraphonAI]

@TechWith_Rachel @heyDhavall 100%. Smarter isn't the same as knowing your business.

[Graphon Labs @GraphonAI]

Hello World!! Models got smarter. Memory didn’t.
Today we’re launching Graphon and announcing our $35M Series A, led by @RRE, six months after our seed.

Graphon is the persistent intelligence layer that makes AI accurate on what your company actually knows: documents, images, video, audio. All of it.
Thank you to the customers and investors who bet on us early.

Try it: https://t.co/C8ZlNckdBI

[Arbaaz Khan @graphonfounder]

After years working on robot memory at @NASAJPL, @GoogleDeepMind , @Meta and @nvidia, @clarkzhango and I kept asking: why does AI still forget?

Today, with @deepakmishraVC, we’re announcing @GraphonAI and our $35M Series A just 6 months after our seed.

• #1 on ATM-Bench, ATM-Bench Hard
• 94% on LoCoMo
• 84% on DolphinBench, 10x cheaper
• A new cost-accuracy frontier across 7 benchmarks

https://t.co/8MkVOtVV0Y

[Graphon Labs @GraphonAI]

@graphonfounder @NASAJPL @GoogleDeepMind @Meta @nvidia @clarkzhango @deepakmishraVC Hello world!! We remember everything, so we will remember this day.

[Graphon Labs @GraphonAI]

@kartikeyjandial See you there!!

[Graphon Labs @GraphonAI]

We love SF Tech Week ♥️! Come celebrate with complimentary VIP car rides all week long, plus coffee at Dogpatch Cafe and our office.

VIP car rides: https://t.co/4mmjED9PoG

Tuesday at Dogpatch Cafe: https://t.co/l1atDGxawK

Thursday at 51 Federal Street - https://t.co/2zJyDYuI6x

[Graphon Labs @GraphonAI]

We love SF ♥️! We love Tech ♥️! We love SFTechWeek ♥️! Come celebrate with complimentary VIP car rides all week long and coffee at the Dogpatch Cafe.

VIP car rides: https://t.co/4mmjED9PoG

Tuesday at Dogpatch Cafe: https://t.co/VDuJgT4nB7

Thursday at Dogpatch Cafe: https://t.co/Beeh0ewL30

Speaking of love, we made a short video! ♥️

[Graphon Labs @GraphonAI]

SF Tech Week already has an incredible lineup, so we're not adding another event. We're helping you get to more of them.

Free coffee pop-ups:
Tue, Dogpatch: https://t.co/BoX0v3WnQV…

Thu, 51 Federal Street: https://t.co/2Kx9CP4TJO…

Free rides to evening events, Mon-Fri: https://t.co/rkHIYmqz9F

Come say hi 👋 #SFTechWeek

@techweek @GraphonAI

[Raja Rao DV @rajaraodv]

I mapped an entire movie by mood for about 5 cents (Jev @typesafeai + @GraphonAI).

Moods like: Funny, Scary, Thrilling, Suspenseful etc.

Around 7,000 AI analyses for roughly $0.05 and 7 seconds.

The demo is pretty simple:

First, Graphon maps the movie.

We ask Graphon about 40 questions designed to cover what's happening throughout the video. Graphon returns descriptions of the scenes, along with citations to the exact moments in the movie.

We use those questions to build a picture of what's happening across the entire movie.

Then we send those scene descriptions to Jev from @TypeSafeai

Jev looks at each scene and judges the probability that it is funny, scary, suspenseful, exciting, calm, or whatever other categories we want to analyze.

That's where we do around 7,000 analyses in parallel.

The result is basically a mood map of the entire movie.

Now imagine you're a movie studio, video editor, or creator.

You could say:

“Show me every scary moment.”

“Find all the car chases.”

“Give me the funniest 30 seconds.”

“Create a compilation of the most suspenseful moments.”

And jump directly to those exact scenes.

Today, doing this at scale is surprisingly complicated. Someone has to watch, tag, classify, scrub through footage, and manually find all of these moments.

With Graphon + Jev, you can start doing it automatically.

And the crazy part?

Those ~7,000 mood analyses cost about 5 cents.

This is what I think multimodal search starts to look like when you combine understanding, citations, and reasoning across an entire video.

@CompleteSkeptic what do you think?

[Graphon Labs @GraphonAI]

At Graphon, we believe intelligence lives in the connections, not the nodes.

So we are bringing the graph to the table.

We are hosting our first Engineering Leaders Dinner sponsored by J.P. Morgan with confirmed attendees from OpenAI, Anthropic, Harvey, Carta, Google, Hippocratic, Lucid Motors, Prime Intellect, Mercor and more.

We have a few seats left and if you are passionate about topics like context intelligence, memory, knowledge graphs, multimodal data and LLMs would love to have you join 👇
https://t.co/VkCgzWGSVU

#MultimodalAI #EngineeringLeaders #Agents #ContextIntelligence #Memory #Graphon

[Graphon Labs @GraphonAI]

Graphon is hiring in San Francisco.

We recently emerged from stealth with $8.3M in seed funding and we're growing fast. We’re looking for builders, researchers and storytellers excited to shape what comes next for enterprise AI.

Open roles: Full Stack Engineer, ML Infrastructure Engineer, Research Engineer, Founding Marketer and Interns.

Apply + share: https://t.co/feZ44MbJdB

[Graphon Labs @GraphonAI]

No matter the enterprise AI use case, the same two flaws exist underneath every one of them: logical blindness and memory decay.

@deepakmishraVC explains why bigger models won't fix it.

[Graphon Labs @GraphonAI]

AI isn’t blind. It just can't hold enough of your data at once to reason over it. And this is a foundational limitation.

@ArbaazK87777726 on why enterprises need a pre-model intelligence layer to help their models go further.

[Graphon Labs @GraphonAI]

Graphon is out of stealth today. $8.3M in seed funding.

We’re building the pre-model intelligence layer — the layer that sits before your AI model and maps how data connects.

Every model has a ceiling on what it can process. Your data shouldn’t.

More in @WSJ: https://t.co/q92bgwrgww

Learn more: https://t.co/VAflaectYm

[Graphon Labs @GraphonAI]

This is a really clear way to think about context windows. The “lost in the middle” problem, how RAG tries to compensate, and why people end up resetting instead of building on prior context all connect here.

At some point, it’s not just about how much information you include, but whether that information holds together in a way the model can actually use.

[Sarah Drasner @sarah_edo]

📍 I made a new drawing about Context Windows. About “lost in the middle”, how RAG affects it, tokenizers and more.

Understanding context windows help you debug and leverage LLMs most effectively. You see why people like Boris from Claude refresh the entire window at times.

[Graphon Labs @GraphonAI]

This article from @techreview gets at something we’ve been thinking about while building Graphon.

Companies have Step 1 (build the tech) and talk about Step 3 (transform the business). But Step 2 — making it work in real workflows with real data — is where things slow down.

In one study, AI agents were tested on 480 everyday tasks and failed most of them.

That’s exactly the problem Graphon is working to solve.
https://t.co/3cCI494Mwz

[Graphon Labs @GraphonAI]

@Stanford's 2026 AI Index is out ➡️ Multimodal publications are up 2.7x in two years, and models are clearing benchmarks almost as fast as they're written.

But it also reveals that these same models can't read an analog clock. This is an architecture problem.

Multimodal is moving fast, but the infrastructure behind it is still catching up. That’s exactly the gap we’re closing with Graphon. More to share soon!
https://t.co/T4SNMMlnbp

[Graphon Labs @GraphonAI]

Enterprise AI buying has gotten harder, and the data shows it. Only 11% say deployments are meeting core goals.

Models work. Systems don't. Retrieval finds data but can't explain relationships, so reasoning stays shallow and ROI stays out of reach.

The bottleneck isn't capability. It's what happens before the model runs.

cc: @WSJ
https://t.co/5pNblrNtgn

[Graphon Labs @GraphonAI]

AI spend is concentrating. A few vendors win, and budgets shift fast.

But the foundation hasn’t changed. More model spend ≠ better outcomes.

The gap isn’t capability. Its structure. That’s what the next layer solves.

https://t.co/P06038CcRg
cc: @TechRadar

[Graphon Labs @GraphonAI]

This is exactly the product we have been building at @graphonai. Instead of compiling raw data into .md and re-reading it into a context window, you build a permanent relational map the AI reasons over directly. Scales far beyond the ~400K words, and across videos, audio, images too. We call it a persistent relational memory. Happy to give you API access to try it on your wiki.

[Graphon Labs @GraphonAI]

Documents were just the beginning.
Most enterprise data lives in video, audio, and mixed formats…and current AI can’t reason across it.

@ArbaazK87777726 on why multimodality is the next real unlock.
#EnterpriseAI #MultimodalAI