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: luma.com/xll5ryfk
Tuesday at Dogpatch Cafe: partiful.com/e/YmLLByo9kUXgD…
Thursday at 51 Federal Street - partiful.com/e/EH4dmIScT1v49…
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: luma.com/xll5ryfk
Tuesday at Dogpatch Cafe: lnkd.in/gfkJG4ih
Thursday at Dogpatch Cafe: lnkd.in/g4JxKeKa
Speaking of love, we made a short video! ♥️
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: partiful.com/e/YmLLByo9kUXg…
Thu, 51 Federal Street: tech-week.com/calendar/sf/ev…
Free rides to evening events, Mon-Fri: luma.com/xll5ryfk
Come say hi 👋 #SFTechWeek
@techweek @GraphonAI
Graphon AI retweeted
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?
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 👇
luma.com/k7nnf5g6
#MultimodalAI #EngineeringLeaders #Agents #ContextIntelligence #Memory #Graphon
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: job-boards.greenhouse.io/gra…
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.
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 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: wsj.com/tech/ai/graphon-says…
Learn more: graphon.ai
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.
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.
technologyreview.com/2026/04…
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
wsj.com/articles/selling-ai-…
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.
techradar.com/pro/security/a…
cc: @TechRadar
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
AI is moving fast. The data layer isn’t.
Vector DBs find similarity.
SQL organizes data.
But neither helps AI reason across relationships, context, and modalities.
That missing layer is the unlock for agentic systems.
That’s where Graphon comes in.
dell.com/en-us/blog/ai-at-sc…
AI brittleness shows up in the smallest places.
Ask a simple follow-up question, and the system breaks. @ArbaazK87777726 explains why this isn’t a tuning issue; it’s an architectural one.
#EnterpriseAI #MultimodalAI
Enterprise AI isn’t blocked by ambition.
It’s blocked by a gap between use cases, value, and multimodal data.
@deepakmishraVC on the real-world challenges enterprises are encountering — and why architecture matters.
#EnterpriseAI #MultimodalAI
AI brittleness shows up in the smallest places.
Ask a simple follow-up question, and the system breaks.
@ArbaazK87777726 explains why this isn’t a tuning issue; it’s an architectural one.
#EnterpriseAI #MultimodalAI
Enterprise AI has a missing layer.
Between ingestion and copilots.
Between storage and reasoning.
Petabytes in. Models on top.
No durable long-context infrastructure in between.
That gap is now the strategic layer.
Graphon fills it.
There’s a hidden tax in AI infrastructure:
Teams pay 10–50x more to dodge multimodal context limits.
Stacking bigger models + more RAG ≠ fixing the constraint.
When cost and complexity spike, it’s usually architectural.
Graphon is the ultra-long-context intelligence layer built for multimodal enterprise data.
If you’re seeing this, find out more: graphon.ai




