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Overview
TwelveLabs enables teams to search, analyze, and understand large volumes of video through multimodal AI that interprets speech, text, audio, and visuals. While domain-specific customization requires model training, its capabilities support media organizations, security teams, and advertisers who need deeper context and faster insights into content.
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Starting Price
Custom
TwelveLabs Specifications
Visual Recognition
Entity Extraction With Text Analytics
Context Awareness
Smart Data Discovery
What Is TwelveLabs?
TwelveLabs is a video-native AI software platform designed for organizations that need to search, analyze, and understand large volumes of video content. It provides multimodal capabilities that interpret speech, text, audio, and visuals to reveal context and identify scenes. The platform supports production, analysis and discovery workflows across various sectors, including media, advertising, and security. It helps teams work more efficiently by enabling faster retrieval, deeper insight extraction, and streamlined handling of extensive video libraries.
TwelveLabs Pricing
The TwelveLabs cost is structured around the following pay-as-you-go customizable plans:
- Free
- Developer
- Enterprise
Disclaimer: The pricing is subject to change.
TwelveLabs Integrations
TwelveLabs supports integration with various applications, including:
- Databricks
- Snowflake
- Pinecone software
- Langflow
- Milvus
- Oracle
Who Is TwelveLabs For?
TwelveLabs software is designed for organizations across various industries and sectors, including:
- Media and entertainment
- Government and security
- Automotive
- Advertising
Is TwelveLabs Right For You?
If your organization works with large volumes of video and needs AI that can search, analyze, and interpret speech, text, audio, and visuals, TwelveLabs provides a video-native solution designed for deeper understanding and context awareness. Its infrastructure supports large-scale libraries and can be deployed on cloud, private cloud, or on-premises environments.
Trusted statements from NVIDIA and MLSE highlight its accuracy and domain-adaptable models. If these capabilities align with your operational needs, TwelveLabs may be a suitable fit.
Still not sure if TwelveLabs software is right for you? Our support staff is available at (661) 384-7070 to help you make the right choice.
TwelveLabs Features
TwelveLabs automatically segments lengthy videos into structured chapters by detecting contextual shifts. This feature generates clear titles and headings within seconds, enabling faster navigation, improved organization, and more efficient analysis of long-form content across varied media workflows.
After indexing, the platform creates endless context-aware prompts derived from video content. It transforms visual information into structured text, supporting enhanced search, tagging, summarization, and downstream automation for teams managing extensive or complex video datasets.
The software identifies precise moments across individual files or massive archives by interpreting visual and audio cues. It streamlines research, accelerates review cycles, and reveals critical insights by instantly surfacing scenes matching specific intents or thematic requirements.
TwelveLabs enables natural-language exploration of large-scale video repositories, including petabyte-level collections. It locates relevant assets through descriptive queries, reducing reliance on manual tagging and simplifying organization for media, research, or enterprise environments that handle large content volumes.
Models from TwelveLabs integrate seamlessly with retrieval-augmented generation pipelines to supply accurate, context-rich information. This capability improves response quality by ensuring large datasets are searched effectively, enabling downstream systems to generate more precise and relevant outputs.
TwelveLabs enriches training datasets for large language models by offering structured, high-quality video-based representations. Its IT model performance improves contextual understanding and supports more accurate outcomes across diverse machine-learning applications.
