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Turn Any Video into a
Searchable Knowledge Base

Paste a YouTube link or upload a file. AI transcribes, extracts visual text, indexes, and lets you semantically search and ask questions about any video content.

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Languages Supported
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Processing Time (10min video)
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Avg Cost per Video
Features
Everything You Need
From raw video to instant answers in minutes.
Core Feature

Semantic Search

Find any moment in a video by describing what you're looking for. No need to remember exact keywords.

  • Search by meaning, not just keywords
  • Ranked results with similarity scores
  • Timestamps for instant video navigation
  • Works across any language
OpenAI text-embedding-3-small — 1536 dimensions
04:32 94.7%
The attention mechanism allows the model to focus on different parts of the input sequence when producing each element of the output...
12:18 89.2%
Self-attention computes three vectors for each token: query, key, and value. The dot product of query and key determines...
AI-Powered

Intelligent Q&A

Ask any question about the video and get accurate, context-aware answers with source citations.

  • Answers grounded in actual video content
  • Cites relevant transcript sections
  • Conversational follow-up questions
  • Powered by GPT-4o-mini for fast responses
RAG pipeline — top-3 context retrieval
What are the key differences between RNN and Transformer?
Based on the video, there are 3 key differences: 1) Transformers process all tokens in parallel while RNNs are sequential, 2) Transformers use self-attention instead of recurrence, 3) Transformers scale better to long sequences due to O(1) path length...
Why does parallel processing matter?
The video explains that parallel processing enables significantly faster training on GPUs. While RNNs must process tokens one-by-one, Transformers compute all positions simultaneously...
Automatic

Speech + Visual Text Extraction

High-quality transcription with Whisper plus OCR extraction of on-screen text from slides, code, and diagrams.

  • 50+ languages auto-detected
  • Precise word-level timestamps
  • OCR for slides, code, diagrams on screen
  • Upload files or paste YouTube URLs
Whisper + Tesseract OCR — free & offline
00:00:12 Welcome to today's lecture on the Transformer architecture and its impact on modern AI.
00:00:28 The paper "Attention Is All You Need" was published in 2017 by Vaswani et al.
00:00:45 It proposed replacing recurrence entirely with a mechanism called self-attention.
00:01:03 This was revolutionary because it allowed for much greater parallelization during training.
How It Works
Three Simple Steps
From any video to knowledge base in minutes.
1 Step One

Add a Video

Paste a YouTube URL or drag & drop a video/audio file. FrameX handles downloading, audio extraction, and everything else automatically.

FrameX
Process
Valid YouTube URL detected
2 Step Two

AI Does the Heavy Lifting

FrameX downloads the video, transcribes audio with Whisper, extracts on-screen text via OCR, chunks the content, and generates semantic embeddings. Fully automatic.

Processing...
Downloading video
Extracting audio
Transcribing with Whisper
Extracting visual text (OCR)
Chunking content
Generating embeddings
3 Step Three

Search & Ask Anything

Semantically search any moment in the video or ask questions and get AI-powered answers with citations from the transcript.

FrameX — Results
Search
Q&A
04:32 94.7%
The attention mechanism allows the model to focus on different parts of the input sequence when producing each output element...
12:18 89.2%
Self-attention computes query, key, and value vectors for each token. The dot product determines how much attention each token pays...
18:45 85.6%
Multi-head attention runs several attention functions in parallel, allowing the model to jointly attend to information from different subspaces...

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