The missing layer between your data and AI
Joined October 2025
- Tweets 29
- Following 11
- Followers 35
- Likes 6
7 Photos and videos
Jun 4
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…
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May 28
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.
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May 20
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.
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May 14
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
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May 7
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.
May 5
📍 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.
ALT Drawing of a context window with lost in the middle, RAG, NIAH and LooGLE explained
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May 1
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…
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Apr 17
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-…
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Apr 9
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
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Apr 3
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
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Mar 26
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…
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Mar 19
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
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Mar 12
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
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Mar 6
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
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Feb 26
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.
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Feb 20
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
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Feb 12
Enterprise AI isn’t failing — it’s hitting an architectural wall.
Not a talent problem. Not a data problem. Legacy AI stacks were built for text, not multimodal enterprise reality. Most AI stacks were built for text. Now they’re being pushed to reason over massive volumes of video, sensors, mixed media, documents, and edge cases.
🧵And then we’re surprised when:
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Feb 12
This isn’t a scaling issue. It’s a mismatch between current architectures and enterprise reality. Enterprise knowledge doesn’t live in prompts — it lives in dense, multimodal systems.
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