Plain-language multimodal search
Describe a scene in plain English and retrieve the matching moments across video, images, audio, and documents. The homepage positions this as semantic search over multimodal content.
Mixpeek
RevendiquerMixpeek is a multimodal video search and retrieval platform for finding exact moments, scenes, and content in video, image, audio, and document libraries.
Overview
Mixpeek is a video search and multimodal retrieval platform that lets you find scenes, moments, and other content inside video, image, audio, and document libraries using plain-language queries. It combines a managed extraction path for raw files with a vector store path for teams that already have embeddings.
The homepage and pricing page frame the product around object storage: you can either connect files and let Mixpeek extract scenes, faces, OCR, transcripts, and embeddings, or bring your own vectors and search them with dense, sparse, or BM25 queries. The result is a searchable layer for media libraries, archives, and agent workflows that need content-level retrieval rather than filename lookup.
Core capabilities
Managed indexing from files
Connect object storage and let Mixpeek extract scenes, faces, OCR, transcripts, and embeddings automatically. This is the managed path for teams that want files indexed without building the pipeline themselves.
Bring-your-own-vector search
Use MVS as a vector store on object storage with dense, sparse, and BM25 search. The pricing page also shows you can bring your own embeddings and search them directly.
Multi-stage retrieval
Combine filter, join, and rerank steps in a multi-stage retrieval pipeline. The site says these retrievers can be composed in under 100 ms.
Typed feature extractors
Work with typed extractors for faces, scenes, transcripts, OCR, fingerprints, and embeddings. The extractor pages indicate these pipelines are documented and can be chained.
Integration-friendly stack
Connect Mixpeek to existing storage and tooling such as S3, GCS, R2, Mux, LangChain, and MCP. The integrations page is organized around plugging into an existing stack rather than migrating data.
Practical use cases
Scene search in video archives
Let editors, producers, or internal tools search a video archive by what is happening in the scene instead of by file name or manual tags. The homepage specifically positions the product around finding exact moments in video libraries.
Face and person lookup
Identify clips where a person or face appears across a media library. The site highlights face and person search as a core use case, including retrieval across large video collections.
Document and transcript retrieval
Search PDFs, lecture recordings, or meeting transcripts for specific spoken phrases, chart layouts, or document regions. The source shows examples for transcript search, OCR, and layout extraction.
Continuous media indexing
Connect storage and index new uploads automatically so libraries stay searchable as content changes. This fits teams that want continuous indexing without manual tagging.
Cross-modal retrieval for complex queries
Use cross-modal joins to answer compound questions that require more than one signal, such as matching a speaker, on-screen text, and scene context at the same timestamp.
Pros and Cons
Pros
- Supports both managed extraction and bring-your-own-vector workflows.
- Searches across multiple modalities, including video, images, audio, PDFs, and text.
- Uses object storage as the underlying architecture, which fits existing storage-first pipelines.
- Supports content-based retrieval patterns such as face search, scene search, transcript search, and cross-modal joins.
- Offers a self-serve plan and an enterprise path with dedicated infrastructure and support.
Cons
- The source does not provide a full public walkthrough of implementation steps, so buyers may need to evaluate setup for their specific stack.
- Some details are only described at a high level on the public pages, especially around integrations and operational limits.
FAQ
How does Mixpeek fit into an existing workflow?
Mixpeek offers two main paths: Managed indexing, where you connect a bucket and let Mixpeek extract scenes, faces, OCR, transcripts, and embeddings from files, and MVS, where you bring your own embeddings and search them on object storage.
What kinds of search does it support?
The source shows workflows for video search, face and person search, document search, audio transcript search, and cross-modal retrieval. It is aimed at teams that need to search media libraries by content rather than filenames or manual tags.
What kind of output does Mixpeek produce?
Managed indexing processes raw files such as video, images, audio, PDFs, and text, then returns searchable features and retrievers. MVS supports dense, sparse, and BM25 search over your own vectors.
Is there a self-serve plan or an enterprise option?
The pricing page describes self-serve plans with monthly billing and no long-term commitments, plus an Enterprise option with custom infrastructure, SSO, audit logs, and dedicated support.
How do you get started?
The source does not describe a full implementation workflow beyond connecting storage or installing the SDK, so setup details will depend on whether you use MVS or Managed indexing.
Quick Facts
- Category
- Video search API / multimodal retrieval
- Platform
- Cloud service with object-storage-based workflows
- Primary users
- Teams building media search, retrieval, and content analysis workflows
- Source domain
- mixpeek.com
- Deployment model
- Managed indexing or standalone vector store (MVS)
- Pricing model
- Free tier, monthly self-serve plans, and custom enterprise pricing