The multimodal data warehouse

    Turn any data
    into answers.

    Turn any video, image, audio or document into answers your apps and agents can query. Files stay in your bucket. What's inside them becomes rows you can search, analyze and recommend.

    Index once. Query every way. Or try a retriever, no key

    10 videos indexed
    00:12.4
    Try it now · no account, no key

    See it work on data we've already indexed.

    Each demo app runs on a hosted retriever. Open the app, or run the same pipeline on your own files.

    Files tables

    Every file becomes a table you can query.

    Mixpeek reads each file where it lives and writes what's inside it as columns. Every column names the model that produced it, so you can see exactly what you're querying.

    Browse models
    DESCRIBE scenes;-- 9 columns · from s3://media/**/*.mp4
    #column_namedata_typeproduced_by
    1document_idkeySTRINGMixpeek
    2start_timeFLOATscene splitter
    3end_timeFLOATscene splitter
    4descriptionSTRINGGemini 3.5 Flash-Lite
    5embeddingVECTOR(FLOAT, 1408)Vertex multimodalembedding
    6transcriptionSTRINGWhisper large-v3-turbo
    7ocr_textSTRINGGemini 3.5 Flash-Lite
    8transcription_embeddingVECTOR(FLOAT, 1024)Multilingual E5 Large
    9clusterSTRINGclustering, no labels needed
    9 rows in setTry it on a video file
    “Every scene in our ads where a known public figure appears”
    Your files
    Video · ad scenes
    collection ad_scenes
    00:01.2
    00:04.1
    00:07.8
    00:11.5
    Marketplace
    Image · public figures
    collection public_figures
    32,667 reference faces
    ⋈ Joinedpromo_cut_b.mp4 · 00:04.1 · scene + public figure, one row
    then: analyze by campaign
    spring_launch14
    holiday_promo9
    creator_series4
    Illustrative query and frames
    Joins + analytics

    Join your files with ours. Then analyze.

    Line up what's seen, said and shown at the same moment, across your own files and pre-indexed marketplace datasets, then analyze the results like any warehouse table.

    For creative strategists

    Find what makes your best ads work.

    Point Mixpeek at your past ads and their results. See which hooks show up in your winners.

    Hook patternswinnerslibrary
    • Hands-on close-up in first 2s
    • Face to camera + price on screen
    • Reveal, then reaction
    • Product on white, voiceover only
    Example
    Start from a template

    Pick a job. Deploy it in one click.

    A template is models, data and a retrieval strategy put together for one job, in one manifest. Deploy it to Studio, then point it at your own bucket.

    See all templates
    A camera operator silhouetted against a lit setScene · 0.91Camera · 0.89Video · footage.webp

    Find the take in an archive nobody has watched end to end.

    Models
    Vertex AI multimodalembedding@001
    Data
    footage-activeadseditor-assets
    Retrieval
    feature_searchllm_filter
    1.27M
    scenes in the reference corpus
    5 → 3
    sources into clean collections
    How Mixpeek retrieves

    Describe it once. It keeps getting better.

    Describe what you want to find. Mixpeek assembles the models, data and search strategy, then adapts the retriever as queries and feedback change. You set the targets for accuracy, speed and cost.

    01Describe it

    Say what you’re looking for. Mixpeek builds it.

    1Your request

    2Mixpeek builds
    Models
    SAM3 · Gemini embeddings
    Data
    Celebrity faces · marketplace
    Search method
    Multi-vector face join

    Connected into one working search system, automatically.

    3Ready to search
    Search ready
    See how it works
    02It learns

    Your retriever adapts to how people search.

    1It learns from
    Query patterns
    Week 1
    Week 8
    general searchesbrand-specific searches
    Feedbackclickspurchasesratings
    2The retriever adapts
    rerank every queryfilter by brand first
    equal weightsmore weight on brand match
    3Results improve
    Relevance
    ↑ higher
    Latency
    ↓ lower
    Cost
    ↓ lower
    Explore automatic optimization
    For developers

    Every model, open to inspect.

    See what each model does, what it reads and writes, how it runs, and whether we've measured it on real data. Agents can read the whole catalog as JSON.

    Browse models
    ModelTaskIn → outRuns as
    Gemini Embedding 2embeddingtext·image·video → vectorProvider API
    SigLIP Base patch16 224embeddingimage·text → vectorServed on Mixpeek
    Multilingual E5 Large Instructembeddingtext → vectorServed on Mixpeek
    Vertex multimodalembedding@001embeddingimage·video·text → vectorProvider API
    Gemini 2.5 Flashvideo / image Q&Avideo·image → textProvider API
    The full Mixpeek catalog · filter by task, modality, license, hardware
    Ways in

    Choose how much you want Mixpeek to handle.

    $ curl -fsSL https://mixpeek.com/install | sh

    # then, in Claude Code, Codex, Gemini CLI or Cursor: “Deploy the Creative DNA template on s3://ads/ and find our strongest hooks”

    Or add it to Claude Code by hand
    $ claude mcp add --scope user --transport http mixpeek https://mcp.mixpeek.com/mcp --header "Authorization: Bearer YOUR_API_KEY"
    The category

    Why a warehouse, not another pipeline.

    What you bring
    Who does the understanding
    Query across modalities
    Shared datasets
    Built for
    Assembled stack
    Separate tools and the glue between them
    Transcription, frame sampling, embeddings, a vector DB, glue code
    You, per vendor
    Custom code
    None
    One workflow at a time
    Multimodal lakehouse
    Open storage, your own pipelines
    Your own pipelines and models on open storage
    You
    SQL + vector search over what you've built
    Bring your own
    ML teams training models
    Multimodal data warehouse
    Mixpeek
    Files and what you want from them
    Mixpeek, with models you can inspect
    Search, joins and analytics built in
    Marketplace datasets, joinable
    Apps and agents asking questions

    A collection of these tables is a warehouse. What is a multimodal data warehouse?

    Your bucket is the source

    No migration. Point it at where your files already live.

    Reads S3, GCS, R2, Azure or any S3-compatible bucket. Your storage stays the system of record. Run it in our cloud or yours.

    S3GCSR2AzureBYO-Cloud
    Founded by engineers from
    MongoDBBerkeleyNVIDIAEtsyAmazon Web ServicesEquinixIAB Tech Lab
    Enterprise-ready
    SOC 2-ready Encrypted at rest BYO-Cloud Self-hosted option Audit trails SSO / SAML
    Pricing

    Free to try. From $25/mo on your own files.

    Marketplace
    Free

    Run hosted IP, brand and ad checks with no account or key, up to a rate limit.

    Build
    $25/mo plus usage

    Templates and managed indexing on your own files, or bring your own vectors.

    Enterprise
    Custom
    From $10,000/mo

    Dedicated infrastructure, your own models, self-hosting, SSO and an SLA.

    What is a multimodal data warehouse?

    A system that turns files into data you can query. It reads video, images, audio and documents where they live, writes what's inside them as durable fields, and serves search, joins and analytics across every modality to apps and agents.

    How is it different from a lakehouse or a vector database?

    A lakehouse stores multimodal data for teams that build their own pipelines. A vector database stores embeddings you already made. A warehouse does the understanding for you and answers questions across all of it.

    Do I have to move my data?

    No. Mixpeek reads from your existing S3, GCS, R2, Azure or S3-compatible bucket, and your storage stays the system of record.

    Where should I start?

    Try a marketplace retriever with no key, then deploy the template closest to your use case on your own files. Developers can start from the model catalog.

    Start from a template that works.

    Deploy a working use case in one click, see what it finds, then point it at your own bucket.