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    An embedding is meaning turned into coordinates

    An embedding turns meaning into coordinates — similar things land near each other, across every modality.

    An embedding turns meaning into coordinates: a model reads 'dog', 'puppy', and 'invoice' and outputs vectors, so 'dog' and 'puppy' land near each other while 'invoice' lands far away. Text, images, audio clips, video frames, and faces all map into one shared coordinate space where distance measures difference in meaning.
    An embedding turns meaning into coordinates — similar things land near each other, across every modality.

    An embedding is the simplest idea in AI that nobody explains plainly: turn a thing into a point, and put similar things near each other.

    A model reads 'dog' and outputs a list of numbers. Read 'puppy', the numbers land nearby. Read 'invoice', they land far away. The same trick works on images, audio clips, video frames, faces. Everything becomes coordinates in one shared space.

    Why this matters: once meaning has a location, search becomes geometry. You stop matching strings and start measuring distance. A query about 'refund policy' finds the paragraph about 'returning purchases' even though they share zero words.

    At Mixpeek we compute embeddings for every frame, transcript segment, and page we ingest. Not because embeddings are the product. Because distance is the only operation that works the same on a sentence and a screenshot.

    Similar things, near each other. That's the whole idea.

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    The unstructured-data problem: the large majority of enterprise data is unstructured — video, images, audio, and documents — that keyword systems and relational databases cannot search by meaning until it is turned into embeddings and indexed. The raw files are stored but not answerable.

    99% of your data has no rows

    Semantic search as nearest-neighbor lookup: embed the query into the same vector space as your content, measure distance to every indexed point, and return the closest top-K results with no keyword matching. Exact brute-force compares against everything and is accurate but slow, while approximate nearest-neighbor (ANN) indexes like HNSW and IVF check a smart fraction of the space and return 95–99% of true answers in milliseconds.

    Search by meaning = nearest neighbors

    Object decomposition: you search inside the file, not for it. A one-hour recording.mp4 is split into scenes (visual embeddings), speech (transcript embeddings on silence and speaker turns), faces (who appeared and when), and on-screen text (OCR of slides and captions). A whole file being findable is not the same as usable — the thing you need is a moment inside it, like 23:41 where the renewal number is spoken while the pricing slide is on screen. The same move works on video, PDFs, and audio: one file becomes dozens of small, independently searchable documents.

    You search inside the file, not for it