Give your agents eyes and ears.
Mixpeek breaks every video, image, and audio file into structured features your agents can search, reason over, and trust.
Connect any object store. Every file becomes a hierarchy of typed, versioned features.
Multi-stage pipelines: search, filter, join, rerank. Deterministic, auditable traces.
Hot → warm → cold → archive. Same feature URI across all tiers.
Discover structure, label with LLMs, promote to taxonomy nodes.
MCP, LangChain, OpenAI functions, REST. Agents reason over features.
Every execution replayable. Which models scored, which docs dropped.
Not a vector database. A warehouse.
A vector database stores embeddings. Mixpeek is a complete system: ingestion, extraction, tiered storage, multi-stage retrieval, and audit trails, behind one set of primitives.
Feature Extractors
Turn every file into a hierarchy of searchable features: faces, scenes, transcripts, logos, fingerprints. Versioned pipelines, composable extractors, feature URIs that pin every embedding to a model contract.
Multi-stage Retrievers
Answer any query through multi-stage pipelines: search, filter, join, rerank, and agentic navigation. Deterministic execution with full audit traces. One call, <100ms.
Taxonomies & Ontologies
Flat or hierarchical reference collections with similarity-based joins. Encode your domain once. Retrievers enforce it at query time, ingest time, or retroactively when taxonomies improve.
Tiered Feature Store
Hot, warm, cold, archive. Features tier automatically by access pattern. Same feature URI resolves across all levels. 60-80% lower cost.
Clusters
Discover structure with 8 algorithms, auto-label with LLMs, then promote stable clusters to taxonomy nodes. The bridge from unsupervised discovery to structured reference data.
Agent-ready
MCP server, LangChain retriever, OpenAI function calling, REST. Agents reason over structured features with lineage, not hallucinated summaries.
Decompose. Reassemble. Enrich. Audit.
Gets smarter the more
you use it.
Reference collections, annotations, and interaction signals compound into better retrieval automatically.
Try it today →Bring your ground truth.
Connect your storage. Mixpeek decomposes every file into searchable features. Import your reference data. Agents can query in an hour.
The system learns structure.
Clusters discover patterns. LLM labeling names them. Promote to taxonomy nodes. Every annotation becomes ground truth for the next iteration.
The flywheel compounds.
Taxonomies retroactively reclassify old data. Interaction signals train the index. The more you use it, the sharper every result gets.
In production right now.
Talent search across ads
Upload a face, find every ad that talent appeared in across campaigns. Reference collection join checks exclusivity. Full trace for takedown evidence.
Try face search →Copyright & logo matching
Pre-publication clearance: scan creative assets against protected face, logo, and audio databases. Auditable traces for every violation. One API replaces three vendors.
Try copyright detection →Scene similarity recs
Rate a few films. Get recs based on how scenes look, not how someone tagged them. Interaction signals feed learned fusion so the retriever improves from usage.
Try the taste engine →Simple, transparent pricing.
Pay for ingestion and extraction. Searches and retrievals are always free so agents can query without metering anxiety.
Hi, I'm Ethan.
I built Mixpeek because 80% of enterprise data is unstructured and effectively unqueryable. Teams duct-tape five vendors together just to search their own media. The answer is a warehouse: one system that decomposes, stores, and reassembles understanding from any file.