The Query Engine for Multimodal Data
Built by experts
# Process presentation videovideo_data = mixpeek.process(bucket="marketing-assets",key="videos/product-demo.mp4",pipeline="video-insights")# Process product specification PDFpdf_data = mixpeek.process(bucket="marketing-assets",key="documents/specs.pdf",pipeline="pdf-extraction")# Find relationships between video and PDF contentmultimodal_insights = mixpeek.correlate(sources=[video_data.id, pdf_data.id],find_multimodal_matches=True)
Process Any File
Unified API for extracting insights across text, image, and video content
Multimodal Connections
Discover patterns and relationships between different media types
Cross-Format Search
Query across all your media types with a single unified interface
Feature Extractors for Every Data Type
Extract and process features from any type of unstructured data with our specialized extraction models
{
"embedding": [
"[5 items]"
],
"dimensions": 1536,
"model": "text-embedding-..."
}
// ... more fieldsText Embedding
Extract semantic embeddings from documents, transcripts and text content
{
"entities": [
"[3 items]"
],
"model": "en_core_web_lg"
}Named Entity Recognition
Identify and extract named entities like people, organizations, and locations
{
"original_length": 4285,
"summary_length": 420,
"summary": "The report disc..."
}
// ... more fieldsText Summarization
Generate concise summaries of longer text documents
{
"sentiment": "positive",
"score": 0.87,
"confidence": 0.92
}
// ... more fieldsSentiment Analysis
Determine the sentiment and emotional tone of text content
{
"keywords": [
"[3 items]"
],
"language": "en"
}Keyword Extraction
Identify and extract key phrases and important terms from text
{
"topics": [
"[2 items]"
],
"method": "LDA",
"num_topics": 10
}Topic Modeling
Discover abstract topics and themes across document collections
{
"language": "en",
"confidence": 0.98,
"alternatives": [
"[2 items]"
]
}Language Detection
Automatically identify the language of text content
{
"category": "technology",
"confidence": 0.94,
"subcategories": [
"[2 items]"
]
}
// ... more fieldsText Classification
Categorize text into predefined classes or categories
{
"relations": [
"[2 items]"
]
}Relation Extraction
Identify relationships between entities mentioned in text
{
"index_name": "document_search...",
"embedding_model": "text-embedding-...",
"dimensions": 1536
}
// ... more fieldsSemantic Search Index
Create optimized indexes for semantic search capabilities
{
"detected_language": "es",
"translation": "{3 properties}",
"supported_languages": [
"[7 items]"
]
}Multilingual Processing
Process and analyze text in multiple languages
{
"sentence": "The cat sat on ...",
"tokens": [
"[7 items]"
],
"pos_tags": [
"[7 items]"
]
}
// ... more fieldsSyntax Parsing
Extract syntactic structure and dependencies from text
{
"embedding": [
"[5 items]"
],
"dimensions": 1536,
"model": "text-embedding-..."
}
// ... more fieldsText Embedding
Extract semantic embeddings from documents, transcripts and text content
{
"entities": [
"[3 items]"
],
"model": "en_core_web_lg"
}Named Entity Recognition
Identify and extract named entities like people, organizations, and locations
{
"original_length": 4285,
"summary_length": 420,
"summary": "The report disc..."
}
// ... more fieldsText Summarization
Generate concise summaries of longer text documents
{
"sentiment": "positive",
"score": 0.87,
"confidence": 0.92
}
// ... more fieldsSentiment Analysis
Determine the sentiment and emotional tone of text content
{
"keywords": [
"[3 items]"
],
"language": "en"
}Keyword Extraction
Identify and extract key phrases and important terms from text
{
"topics": [
"[2 items]"
],
"method": "LDA",
"num_topics": 10
}Topic Modeling
Discover abstract topics and themes across document collections
{
"language": "en",
"confidence": 0.98,
"alternatives": [
"[2 items]"
]
}Language Detection
Automatically identify the language of text content
{
"category": "technology",
"confidence": 0.94,
"subcategories": [
"[2 items]"
]
}
// ... more fieldsText Classification
Categorize text into predefined classes or categories
{
"relations": [
"[2 items]"
]
}Relation Extraction
Identify relationships between entities mentioned in text
{
"index_name": "document_search...",
"embedding_model": "text-embedding-...",
"dimensions": 1536
}
// ... more fieldsSemantic Search Index
Create optimized indexes for semantic search capabilities
{
"detected_language": "es",
"translation": "{3 properties}",
"supported_languages": [
"[7 items]"
]
}Multilingual Processing
Process and analyze text in multiple languages
{
"sentence": "The cat sat on ...",
"tokens": [
"[7 items]"
],
"pos_tags": [
"[7 items]"
]
}
// ... more fieldsSyntax Parsing
Extract syntactic structure and dependencies from text
No more model chaos
New retrieval techniques require new models, which means maintaining backwards compatibility, handling re-embeddings, and coordinating A/B tests.
Seamless Model Upgrades
Automatically upgrade to newer, better embedding models and retrieval techniques without breaking existing queries.
Cross-Model Compatibility
Query across multiple embedding spaces, removing the need for costly mass re-embeddings.
A/B Testing Infrastructure
Compare embedding model performance with built-in testing tools and automatically roll out the winner to production.
The embedding lifecycle, simplified
Without Mixpeek: Manual re-embedding of collections when models update, version conflicts, complex migration paths, and expensive compute costs.
With Mixpeek: Incremental updates, version management, backward compatibility, and intelligent embedding translation — all managed for you.
How it works
You can get started with just one line of code. But as you do more complex things, Mixpeek provides flexible tools for every step of the pipeline.
Upload Objects
Ingest your unstructured data from any source to Mixpeek
S3 Direct Integration
Connect directly to your AWS S3 buckets for seamless data ingestion
Multi-format Support
Upload files, blobs, and documents of any format (PDF, images, video, audio)
Automatic Content Detection
Let Mixpeek automatically detect content types and prepare them for extraction
# Upload a file to Mixpeekimport mixpeek# Connect to your S3 bucketmixpeek.set_credentials(api_key="YOUR_API_KEY")# Upload objects from your S3 bucketresponse = mixpeek.upload(bucket="my-data-bucket",key="documents/financial-report.pdf",metadata={"source": "quarterly-reports","department": "finance"})print(f"Object uploaded with ID: {response.object_id}")
Industries Scale on Mixpeek
From startups to enterprises, teams use Mixpeek to build powerful multimodal applications
Media & Entertainment
Media companies handle massive volumes of video content.
- Improve content discovery and monetization
- Dynamically tag video segments
Retail & E-commerce
Retail companies maintain massive asset libraries.
- Enable visual product search
- Automate product tagging
Advertising & Media
AdTech platforms process millions of creative assets daily.
- 90% faster creative analysis
- Automated brand safety checks
Education Technology
EdTech platforms manage diverse learning materials across multiple formats.
- 80% faster content organization
- 65% higher student engagement
Hassle-free multimodal search
Focus on building great applications. We'll handle the complex infrastructure.
Automatic scale
When your traffic spikes, Mixpeek automatically scales to handle the load. When traffic drops, we scale down to zero - you only pay for what you use.
Pay for what you use
Only pay for active search operations. No charges for idle time or unused capacity.
Forget about infrastructure
Building multimodal search is complex. We handle the heavy lifting - vector stores, model serving, query optimization, and scaling. You focus on your application logic.
Logging & monitoring
Get detailed insights into your search performance. Monitor query latency, throughput, and relevance metrics. Debug and optimize with comprehensive logs.
What will you build?
Harness the power of multimodal data to create experiences that were impossible yesterday but essential tomorrow. Transform how your users interact with content across text, images, video, and audio.