Mixpeek indexes the contents of media and documents for retrieval APIs

Mixpeek turns the contents of video, images, audio and documents into data that an application can search, classify, filter or rank. It offers a managed path for raw files and a separate MVS path for teams that already have embeddings.

Choose files or existing vectors

The managed workflow connects to files and extracts features such as scenes, speech, faces, on-screen text and document layout. The bring-your-own-vector service supports dense, sparse and BM25 search over existing embeddings. The publisher presents storage connectors so customers can work from their current object-storage setup.

The distinction affects both integration and cost. A team with raw footage needs to evaluate extraction as well as retrieval. A team with a prepared vector collection needs to evaluate the search and indexing path rather than assume it must regenerate every feature.

Search a moment rather than a whole file

Mixpeek shows cross-modal joins that align what is seen, said and displayed at the same moment. Its examples include finding a scene where a person says a term while a slide shows related text, and searching across media using extracted features and metadata.

The homepage labels demonstration scores and several examples as illustrative. Those numbers are not independent evidence of accuracy on a customer's archive. A useful trial should include difficult near matches and check whether the returned segment actually supports the requested combination of evidence.

Check usage charges and deployment claims

At capture time, both MVS and Managed started with a $25 monthly plan fee plus usage. MVS bills vector-related operations; Managed bills processing by units of content and includes a processing allowance on the advertised Build plan.

The publisher offers self-hosted and bring-your-own-cloud options, but explicitly says it holds no SOC 2 or HIPAA certification today. Its separate SOC 2-ready label should not be mistaken for certification. Review the intended deployment, data flow and processing charges, then measure retrieval quality on representative files before adopting the API.