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    99% of your data has no rows

    Most enterprise data is unstructured — unsearchable by meaning until it is extracted, embedded, and indexed.

    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.
    Most enterprise data is unstructured — unsearchable by meaning until it is extracted, embedded, and indexed.

    Start with the number that explains the whole space: most of what your company knows is not in a table.

    Contracts, call recordings, screen shares, product photos, support tickets, hours of video. The usual estimate is 90 to 99% of enterprise data is unstructured, and honestly the exact number doesn't matter. What matters is that your warehouse can only query the sliver that fits in rows and columns.

    SQL can tell you revenue by region in milliseconds. It cannot tell you which sales call mentioned the competitor, or which screenshot contains the error message. Not because the data isn't there. Because nothing ever turned it into something queryable.

    That gap is where I've spent the last few years building Mixpeek. Everything else in this series (embeddings, chunking, retrieval) exists to close it.

    The data was never missing. The structure was.

    Where this diagram appears

    Run this on your own data

    Mixpeek turns video, images, audio, and documents in your object storage into searchable, timestamped results through one API.

    Search your own data, free

    Related diagrams

    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 is meaning turned into coordinates

    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