Find any scene in your video library.
Mixpeek understands your video, image, audio, and documents, and returns the most accurate, timestamped results from the storage you already have. Every search teaches it: relevance keeps improving the more you and your agents use it.
Explore the live demos above with zero setup: then start here when you want Mixpeek running on your own data.
Bring your own vectors
You already have embeddingsMixpeek Vector Store (MVS): an agent-native vector store that runs on your object storage. Dense, sparse, and BM25 search. From $25/mo.
Connect your files
You have raw filesManaged indexing: point us at video, audio, or documents and we extract scenes, faces, OCR, transcripts, and embeddings. No pipeline to build. From $25/mo.
What the agent sees.
Every object you index becomes structured, searchable features: faces and objects in a frame, layout regions in a document, speakers in audio. Those are the same features an agent queries, and joins across modalities.
Hover or tap a card to preview the search it powers.
Person · 0.97Face · 0.95Handbag · 0.92Video · 00:04:12“A woman carrying a tan tote bag walks past a red storefront.”
transcript · “…meet me at the corner in five.”
HeaderChartBodySignaturePDF · resume.pdfHeader, body, charts, and signature detected as typed regions.
OCR · 1 header · 3 sections · 1 signature
Who spoke when, aligned to the transcript and the timeline.
audio · 2 speakers · matched at 00:01:30
One query across every modality.
Real questions rarely fit one feature. “Find the moment our CEO said guidance while the slide read Q4 outlook” needs a face, a spoken phrase, and on-screen text to line up at the same instant.
Mixpeek ties those features to the same object and timestamp, so an agent gets back the exact clip instead of three unrelated matches.
The CEO says “guidance” as the slide behind her reads “Q4 outlook.”
"What concepts exist in my data that nobody has labeled yet?"
No keyword, example, or prompt can answer that: they all assume you already know what you're looking for. Mixpeek clusters what belongs together on its own, so a natural hierarchy surfaces instead of a flat pile of tags: a taxonomy built from your data, organized around what your business actually cares about.
That taxonomy is your ground truth, and it feeds back. Every search, every correction sharpens the features, the clusters, and the relationships. Your competitors' metadata decays. Yours compounds.
- Creative moments644
- └Unboxing214
- └Hands-on close-up121
- └Reveal + reaction93
- └Night driving88
- └Product on white342
every search + correction feeds back → sharper features, tighter clusters, truer taxonomy
In production right now.
Search video by what happens on screen
Type what you want to see, not what someone said. Every query fans out across three vector spaces (one for the picture, one for what a vision model says is happening, one for the dialogue) and reciprocal-rank fusion merges them server-side. Results are scenes with timecodes, not whole files.
Try video search →Visual search across 45k artworks
Upload any image and find visually similar paintings across 45,000+ artworks, or just describe what you're looking for. Hybrid image and text retrieval, ranked with RRF.
Try gallery search →Face search across video
Drop in a headshot and find every clip a person appears in across 63 video ads and 2,600+ faces. Full trace for takedown evidence.
Try face search →One install. Two paths.
Most retrieval stacks mean gluing together a vector DB, a file pipeline, and an agent layer. Mixpeek is one install with two ways in.
Bring embeddings
Plugs into your existing stack.
Connect your storage, point Mixpeek at it, and every file becomes searchable by what's inside it. No migration, no code changes.
Mux
Every Mux upload becomes searchable by face, scene, transcript, and on-screen text, with no manual tagging.
View integration →
Backblaze B2
S3-compatible extraction at 1/5th the cost. Store on B2, extract with Mixpeek, zero egress fees.
View integration →
Iconik
Every asset in your DAM becomes findable by what's inside it: scenes, faces, spoken words, on-screen text.
View integration →Pick your file types. Choose what to search by.
Video, image, audio, documents, text, or web: connect a bucket, pick the features you want to search by, and these pipelines run as they are. Every one is documented and open source in the extractor cookbook.
Video · Image · Audio · Text
Search by: Scenes, speech & visual similarity
Unified embeddings for video, audio, image, and text. Scene and silence chunking, Whisper transcription, thumbnails.
Multimodal (Video/Audio/Image)
Any file
Search by: Everything in one pass, any file
One extractor for image, video, audio, and documents. Auto-detects modality and applies the right pipeline.
Universal All-in-One
Image · PDF
Search by: Visual similarity, described in words
Dense 768-D image embeddings with Google SigLIP for text-to-image search in one contrastive space.
Image Embeddings (SigLIP)
Text
Search by: Meaning, not keywords, in any language
Multilingual dense text embeddings with E5-Large for semantic search and RAG out of the box.
Text Embeddings (E5-Large)
Any file
Search by: Whole objects: all their files as one
Embed ALL files of an object (images, PDFs, video, audio, text) into one 3072-D Gemini vector.
Multi-File Object Embeddings (Gemini)
Image · Video · PDF
Search by: The same face, across your whole library
Production face recognition that detects, aligns, and embeds faces to 512-D ArcFace vectors.
Face Identity (SCRFD + ArcFace)
What we shipped lately
- Aug 19DocsThe webhook event names in our docs are the ones the API will actually acceptNine of the twenty event names written into documented examples were not in the schema, and three more sat in the event reference table on the webhooks page itself, which is the page everything else points at. batch.completed and batch.failed showed up repeatedly and no batch event has ever existed. cluster.completed is really cluster.execution.completed. taxonomy.materialized has no equivalent at all, so anyone subscribing to it got a webhook that could never fire and no error saying why. One example had webhooks doing an alert's job and is now written as a system alert. Every event name in the docs is checked against the schema on each build, in code samples and reference tables alike, and a page that quotes an invalid name deliberately has to say so and gets named in the output so the exemption cannot go quiet.
- Aug 19DocsA retriever's input_schema can be changed in place, which the reference had been denyingThe PATCH route reference, the request model and the service all described input_schema as immutable, while the field's own description and the running behavior have long allowed non-breaking evolution: adding an optional field, or adding a default, an example or a description to a field you already have. Breaking changes are still refused, with a 422 that names each one rather than a blanket rejection. A reader who checked the route reference before making the call concluded they had to clone the retriever and repoint everything at the copy, and that was never necessary.
- Aug 19APIIndex recommendations say which kind of index to build, not just which fieldRecommendations knew which fields you filter on and had no idea how you filter them, so a field you always compare as a range and a field you match on exact equality produced the same advice. Every recorded query now stores the operator next to the field it applies to, kept inside one entry so an operator can never drift onto the wrong field and hand you a confidently wrong suggestion. Recommendations read the dominant operator class per field and name a keyword, range, text or geo index. Fields whose recorded history predates this return nothing rather than a guess, so an empty answer means we have not seen enough traffic on that field yet.
- Aug 19APIQuery analytics report the filters you actually used instead of returning nothingFive endpoints under /v1/analytics/namespaces came back empty on every namespace: slow queries, most-queried fields, field performance, compound index patterns and index recommendations, along with the summary that composes four of them. They looked for field references inside the raw query text, which stores the search input a caller sent and structurally never contains a field name, so the pattern matched 0 of 295,687 recorded rows. The information was already there in a dedicated column that 53,564 of those rows populate, holding the real filtered fields down to metadata.brand and archive_status. Those queries now read the column that has the data. One namespace was reporting zero slow queries while 10,068 were on record.
From $25/mo. Usage-based everything.
Two products, one model: a monthly minimum that acts as a floor, with usage above it billed at the same transparent rates. MVS is priced by the vector, Managed by the object.
Bring your own embeddings and pay by the vector. Dense, sparse, and BM25 search on your own object storage. Build starts at $25/mo with up to 1M vectors; Scale ($250/mo) covers 25M.
Start with MVSBring raw objects and pay by the object: credits at $0.001 cover extraction, embedding, indexing, enrichment, and retrieval. Build covers 100K objects/mo; Scale ($250/mo) covers 1M.
Start with ManagedDedicated infrastructure, self-hosted options, SSO, SLA, security reviews, and hands-on architecture support.
Talk to usCommon questions.
Do I have to move my data?
No. Mixpeek reads from your existing S3, GCS, R2, Azure, or S3-compatible bucket. Your storage stays the system of record, and nothing leaves your cloud.
How fast is retrieval?
Hybrid queries (dense, sparse, and BM25) return in well under 100ms p95, even with vectors persisted on object storage rather than held in RAM.
Do I need embeddings to start?
No. Bring your own vectors with MVS, or point Managed at raw files and it generates embeddings and features for you.
What can Managed extract?
Faces, scenes, transcripts, OCR, labels, and embeddings from video, images, audio, PDFs, and documents, all indexed at the object level.
Can I self-host?
Yes. Deploy in your own cloud (BYO-Cloud) with SOC 2-ready and HIPAA-ready controls, SSO, audit trails, and namespaces.
How does pricing work?
Both MVS and Managed start at $25/mo minimum. Usage counts toward the minimum: pay the greater of metered usage or the floor. MVS bills storage + queries; Managed bills in credits covering extraction, embedding, indexing, and retriever execution.