Rasyn

Building the Perception Layer for Enterprise Agentic AI

[Rasyn @rasyn_ai]

6/n We broke down how it works and built a tutorial so you can try it yourself. Covers the architecture, benchmarks, and practical applications. Includes Colab notebook as well to follow along.

Full breakdown: https://t.co/4tjT81Iklp

[Rasyn @rasyn_ai]

1/n How is DeepSeek-OCR different?

Instead of extracting text and sending it as tokens, they keep the document as an image and use vision models for compression.

[Rasyn @rasyn_ai]

5/n The core insight is counterintuitive. We usually think of images as larger than text, but in the token economy of LLMs, it works the other way around.

[Rasyn @rasyn_ai]

6/n
At Rasyn we understand this from day 0 where we build system which deeply understands the workflows with minimal disruption and maximum adaptability.

[Rasyn @rasyn_ai]

1/n
YC gets it. This is the same study we reference in our pitches. Our notes from reading the MIT State of AI report (https://t.co/lhqGLTF2nF): Only 5% of enterprises have adopted embedded/task-specific AI, while 40% use general-purpose AI.

[Y Combinator @ycombinator]

MIT's new State of AI in Business report went viral for claiming that 95% of enterprise AI projects fail. But the real story isn't that AI doesn't work — it's just big companies can't build it.

On the @LightconePod, @garrytan, @harjtaggar, @sdianahu, and @snowmaker break down what the study really says, why in-house enterprise AI efforts keep stalling, and how startups are filling the gap with products that learn, integrate, and actually deliver value.

2:08 - The enterprise AI adoption gap and why the failure rate is high
3:32 - Even Apple can be bad at software
4:30 - Why getting enterprise software to actually work is so hard
11:08 - The Reducto case study
13:39 - The type of enterprise employee you should find as a founder
14:39 - Meet founders who’ve been acquired by enterprises
15:25 - Enterprise/startup tension and symbiosis

[Rasyn @rasyn_ai]

5/n
Startups that successfully cross this GenAI divide have fundamentally low setup burden and fast time-to-value which outperforms heavy enterprise builds.

[Arindam Ghosh @arindam_qed]

Imagine a little kid reading a big science book. They can read all the words, but they don't understand what any of it means. That's AI today. Rasyn is like taking AI through school. We teach it what everything it sees means, so it can finally solve the problems at the end of the textbook. @rasyn_ai

[Inderpreet Singh @the_inderpreet]

@pmitu You know how robots need eyes? Rasyn gives AI smart glasses to read messy documents and tricky forms. PLUS a playground where they turn 'what I read' into 'watch me do!', like reading a recipe and baking cookies, building forts, or whatever fun they want! @rasyn_ai

[Rasyn @rasyn_ai]

At Rasyn AI, we're building the perception layer that turns unstructured documents into actionable knowledge.

Your documents shouldn't be tombs. They should be treasures.

More coming soon. 📄→🧠

[Rasyn @rasyn_ai]

Humanity's legacy rests in documents that shaped empires and ideas.

Scientific breakthroughs, engineering marvels, infrastructure blueprints—all preserved and passed forward across generations.

Yet 95% of enterprises can't unlock this knowledge for their AI systems.

[Rasyn @rasyn_ai]

The challenge? Domain-specific documents.

Handwritten medical notes, complex insurance claims, specialized terminology. These need deep contextual understanding.

Current systems can read, but cannot comprehend.