Mixpeek

Prompt. Generate. Lose it. Repeat. Teams generating AI video hit the same wall: hundreds of takes land in one folder with names like untitled(7).mp4, and the best one is unfindable a day later. Mixpeek indexes each clip as it lands (scenes, speech, on-screen text), so "neon alley in the rain" returns the exact file and second, ranked by match. Your generator makes clips. Mixpeek finds them. mxp.co/i/genclips

The index inside nearly every database, the B-tree, was designed in 1970. In 2018, researchers asked a good question: what if the index was a model that learned where your data lives? It came out ~70% faster with an order of magnitude less memory. The more interesting part is why. An index exists so you don't scan everything. A B-tree does that by sorting a column someone chose ahead of time. A schema is really a list of the questions you expected. Video, audio, and documents don't come with columns, so you decompose them: scenes, faces, spoken words, each with an embedding: a coordinate for meaning. Put enough coordinates together and structure emerges. Clusters form, hierarchies nest, discovered from the geometry, not declared by a librarian. A category stops being a folder and becomes a saved search that re-checks reality on every call. And every query teaches the index a little about where it was wrong. An index maintained by use, not by hand. Applied to video and documents: mxp.co/i/learned-index

Chunking is the highest impact, lowest visibility decision in a retrieval pipeline. How you split a document decides what your search can ever find. Take one document, chunk it four different ways, and you get four different search experiences. Fixed-size, say 512 tokens, is simple and predictable. It also slices through the middle of sentences, cuts code blocks in half, and breaks tables apart. Your embedding ends up holding half a thought. Semantic chunking splits on topic shifts, detected by embedding similarity. Every chunk is coherent and captures one complete idea. The sizes swing hard, though. Some come out at 100 tokens, others at 2,000. Hierarchical chunking nests small child chunks inside large parent chunks. You search the children for precision and pull the parent for context, so you get fine-grained matching without losing the surrounding meaning. The price is a more complex index. Document-aware chunking splits on headings and keeps code blocks and tables intact. The chunks follow the document's own structure. A table stays a table, a function stays whole. It just needs format-specific parsing. Rough guide: fixed-size for homogeneous text or a fast prototype, semantic for long-form articles and papers, hierarchical for Q&A and RAG where context carries the answer, document-aware for technical docs full of code and tables. Most RAG failures aren't model failures. They're chunking failures.

A video editor charges ~$300 to find one clip: four hours scrubbing footage everyone already knew was in there. It's slow because raw video has no find command. A person becomes the search engine, and one missed frame means the hour starts over. The editor was never slow. They were searching a medium nothing had ever read. Mixpeek reads the media once (scenes, spoken words, on-screen text, objects) and turns it into something you query in plain language. One search, exact timestamp. The four hours were the tax on skipping the index. mxp.co

Plain keyword search just beat every AI retriever on 13 benchmarks: ~80% vs 69%. grep and BM25 are that hard to beat on text. Now point that same search at your video library and it finds nothing. There's no keyword for "the tense, blue-lit scene right before the logo". Keywords only match what a person typed in. Multimodal retrieval reads the content itself (the frame, the spoken words, the on-screen text) as embeddings, so the meaning of your query is matched against the meaning of the footage. Mixpeek runs that across video, images, audio, and documents. mxp.co

Google Cloud's Video AI bills by the minute to tag your footage — and you still can't ask one plain-English question across the archive. Labels aren't search. You get annotations per clip, then you're back to scrubbing. I typed "find the moment the red car crashes" over 50,000 videos and got the exact timestamp in seconds. The difference is what gets indexed. Mixpeek reads what's inside the video — scenes, objects, spoken words, on-screen text — into one warehouse you search by meaning. Google for the inside of your footage. mxp.co

AWS S3 will store a petabyte of your video for ~$23,000 a month — and your AI can't read one frame. 99% of enterprise data is unstructured and it lives in object storage you pay for every month. S3 keeps the bytes, not an index, so no query reaches the content. A bigger model doesn't help — there's nothing inside for it to index. Mixpeek turns media into searchable signals — scenes, transcripts, OCR, embeddings — and lands them in a warehouse you can query. Storing it was never the same as making it searchable. mxp.co

You pay Pinecone to be your agent's memory — and it still can't tell you what cracked at 4:12 in a video. A vector database stores the embeddings you hand it. Your footage was never turned into vectors, so the agent falls back to the transcript and guesses. Adding more index or a bigger model doesn't help — the agent isn't short on memory, it's blind. Mixpeek is the extraction + retrieval layer that watches the media: scenes, faces, on-screen text, transcript, embeddings, all searchable. The agent asks "where does it crack" and gets the exact frame with a timestamp. mixpeek.com/agentic-rag

Google can reverse-search any photo you own. Hand it a five-second video clip and it has nothing — there is no reverse search for video. So to find where a clip shows up — the same scene re-uploaded, your logo inside someone else's footage, one moment buried in hours of CCTV — you scrub by hand or search filenames nobody wrote. The answer is in the pixels, and every tool makes you describe it in words first. Reverse video search skips the words. Cut each video into short clips, turn every clip into an embedding — motion, scene, audio — and store the vectors. Now the clip itself is the query: nearest-neighbor finds every place it appears, ranked by similarity. A grainy CCTV frame matches the trailer it was ripped from — across billions of segments, in under 100ms. mxp.co/i/reverse-video

MCP is quickly becoming the default bridge between agents and external tools. Most of those tools speak text. Ours speaks video, images, audio, and documents. Mixpeek now exposes its retrieval stack over MCP, so Claude, Cursor, or any agent can search a multimodal warehouse directly, with no custom integration layer built per app. What gets exposed isn't a raw vector-similarity call. It's your retrievers, configurable multi-stage pipelines that filter, join, and rerank in under 100ms, surfaced as tools. The agent plans a query, picks a retriever, and searches across namespaces of unstructured content that's already been turned into features: faces, scenes, OCR, transcripts, and dense / sparse / BM25 vectors on object storage. A few of the tools an agent gets: * agent_search — autonomous multimodal retrieval across namespaces * retriever_execute — run a multi-stage pipeline (filter · join · rerank) * features_get — read faces, scenes, OCR, transcripts, embeddings * agentic_enrich — extract and embed new objects (write) The default posture is an agent that sees everything and changes nothing. These are the details that decide whether you can actually point an agent at real data. MCP removes the glue code; scoped, multimodal-native retrieval is what makes it safe to operate. Docs: https://lnkd.in/eUKGAFVG