Augmend provides AI technology that records and documents processes from videos. The platform creates documentation and an AI assistant from any video. Users can record with Augmend's app, upload their own video, or integrate with Augmend's API.

Augmend

Record your screen. Capture knowledge.
Your team's shared brain.

Scale your expertise and yourself. Record once, share forever. More than just screen capture. An instant team AI assistant.

Our new video understanding API is live! With a few lines of python code, you can send a video to our service and generate documents, tags, title, and more. Great for technical videos, it uses what people say *and* what's on screen together! Or use Zapier with no code needed!

Check out more info on our blog: https://lnkd.in/gtDVrkcB

In honor of our upcoming API beta at Augmend that lets anyone add automated video to document generation to their apps and agents we've revamped the homepage, check out augmend.com.

There was an intervention to get rid of my "excessive" neon glow drop shadows (can you really have too much cyberpunk neon?), leave a comment if you of course want it back 😂

At Augmend we believe it's important to share what we learn, and it's always nice to sit down and writeup something novel others might find useful. Preprint of a new meeting/video segmentation technique we worked on a few months ago that we're calling TreeSeg: https://lnkd.in/gJ68fvKv.

Dimitrios and team will be sharing a blog post and repo in the next week for those interested. 

The chapters created from this improved segmentation will also be available as part of our upcoming API for any video you submit.

- Abstract -
From organizing recorded videos and meetings into chapters, to breaking down large inputs in order to fit them into the context window of commoditized Large Language Models (LLMs), topic segmentation of large transcripts emerges as a task of increasing significance. Still, accurate segmentation presents many challenges, including (a) the noisy nature of the Automatic Speech Recognition (ASR) software typically used to obtain the transcripts, (b) the lack of diverse labeled data and (c) the difficulty in pin-pointing the ground-truth number of segments. In this work we present TreeSeg, an approach that combines off-the-shelf embedding models with divisive clustering, to generate hierarchical, structured segmentations of transcripts in the form of binary trees. Our approach is robust to noise and can handle large transcripts efficiently. We evaluate TreeSeg on the ICSI and AMI corpora, demonstrating that it outperforms all baselines. Finally, we introduce TinyRec, a small-scale corpus of manually annotated transcripts, obtained from self-recorded video sessions.

Big update! Turn any video into structured, queryable, docs in 1 click, with the Augmend AI assistant. 

Comment with a youtube or loom or just a gdocs link to a video and I'll turn it into a doc + intelligent Q&A link right now. Drop one below and let's try it out together.

Have an existing loom from a brownbag that needs to be written out for your team's wiki or technical docs?

Want step-by-step instructions from a youtube tutorial?

All done for you with no extra work, automatically handled by Augmend.

An AI assistant that takes video and turns it into multi-modal documents is a game changer for learning (and not having to write docs 😅).

Everyone is building summaries and "chat with" experiences off of the voice transcript only, our new system has eyes making it way smarter!

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