Mixpeek provides a unified retrieval platform that indexes any file—video, audio, image, or text—and extracts structured, searchable features such as faces, objects, and transcripts. By connecting to existing storage, it automatically generates multimodal embeddings and enables cross‑modal queries without code changes, supporting production workloads with SOC 2 and HIPAA compliance.
Funding
$650K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
1OFounders
Product
Problem
Enterprises generate massive amounts of unstructured media—videos, audio, images, and documents—but lack a scalable way to index, search, and retrieve specific content within these files without moving data or building custom pipelines.
Solution
Mixpeek provides a unified retrieval platform that connects directly to existing object storage (S3, GCS, Azure, etc.) and automatically extracts multimodal features such as faces, objects, OCR text, transcripts, and embeddings using pre‑built pipelines (Google SigLIP, E5‑Large, Gemini, Whisper, ArcFace). Extracted features are stored as searchable documents, enabling cross‑modal queries (e.g., “find video frames with person 42 and the word ‘earnings’”). The service offers both a managed indexing mode and a standalone vector store (MVS) for bring‑your‑own embeddings, with support for dense, sparse, and BM25 hybrid search. Enterprise‑grade security (SOC 2, HIPAA) and flexible deployment options—including self‑hosted and BYO‑cloud—allow organizations to adopt the platform without data migration or code changes.
Target Audience
Primary customers are enterprises and developers that need to search and retrieve specific content from large collections of video, audio, image, and document assets—such as media companies, advertising agencies, e‑commerce platforms, and AI‑driven knowledge bases.
Features
- Automatic multimodal extraction pipelines for video, audio, image, and document files, including face embeddings (ArcFace), scene descriptions (Gemini), visual embeddings (SigLIP), and Whisper transcripts
- Unified embeddings across modalities (e.g., 3072‑D Gemini vectors for whole objects) enabling cross‑modal joins in a single query
- Managed indexing mode that processes raw files on‑the‑fly; standalone MVS mode for direct vector upserts and hybrid dense/sparse/BM25 search on object storage
- Multi‑stage retrievers configurable in JSON to filter, join, and re‑rank results in under 100 ms
- Integration with major cloud storage providers (S3, GCS, Azure, R2) and ecosystem tools (LangChain, Mux, MCP) via API and SDKs
- Enterprise security features: SOC 2 Type II, HIPAA compliance, SSO/SAML, audit logs, role‑based access control
- Flexible deployment: SaaS, self‑hosted, or BYO‑cloud with dedicated single‑tenant infrastructure for large customers