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.
Bring vectors
MVSAgent-native vector store on object storage. Dense, sparse, and BM25 search. From $25/mo.
Connect files
ManagedManaged indexing extracts scenes, faces, OCR, transcripts, and embeddings from any file type.
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.
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 →Posters that learn your taste
Like or dislike movie posters and watch the grid adapt to your taste in real time. Interaction signals feed learned fusion so recommendations improve from usage.
Try movie personalization →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
- Jul 16APIYour object metadata now reliably flows into documentsCustom metadata on your source objects — brand, job ID, dates, status flags, anything you attach — could be dropped when producing documents, so metadata-driven features (attribute filters, recency ranking, brand/job scoping) silently had nothing to work with unless you'd explicitly configured field passthrough. Object metadata now always carries into the documents it produces by default, across both the extraction path and text extraction, so the fields you attach at ingest are there to filter and rank on.
- Jul 16APIDocuments without edges no longer break listing and retrievalA document written with an explicit empty-edges value could silently fail validation on read: listing quietly dropped those documents, and any retriever whose results touched an edge-less document returned zero results with a 200 status — an error visible only with explain mode on. Empty and missing edge values are now both handled, so reads return every document and retrievers stop mysteriously coming back empty.
- Jul 16EngineResubmitted batches are no longer instantly killed as stalledWhen you resubmitted a batch, its new run inherited the previous run's stall checkpoint — so the fresh job, reporting zero processed so far, was measured against the old run's clock and force-terminated within minutes as 'stalled.' The stall checkpoint is now tracked per run, so a resubmitted batch starts with a clean slate and runs to completion.
- Jul 16StudioNew signups no longer bounce between org creation and the plan pageImmediately after creating a workspace, the new organization membership hadn't yet propagated into the session token — so the app could conclude 'no org' and send a brand-new signup to organization creation while the checkout gate wanted the plan page, a loop that dropped real signups. Studio now recognizes a just-created org from its creation evidence, so fresh signups go straight to plan selection instead of churning.
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.