Introducing Kubrick
Existing Solutions
Section titled “Existing Solutions”TwelveLabs provides an end-to-end video search and understanding solution, powered by multimodal embeddings and video understanding models. MomentsLab also provides a similar solution, though their approach relies primarily on AI-powered metadata extraction rather than multimodal semantic embeddings, which limits the types of queries users can perform.
Enterprise solutions like those offered by TwelveLabs and MomentsLab offer active support, are more fully-featured, and offer integrations with other tools like Premiere Pro.1 However, these solutions are managed, which means less control over and visibility into the data pipeline. For teams that want to build and optimize a product utilizing an existing search platform, this can create unnecessary dependencies on external architecture and customer support.
Users are also required to store their entire video library on these platforms, which may not be desirable for use cases where data privacy or cost minimization is a priority. This can also result in architecture that is tightly coupled with these external platforms and their proprietary models. Given that multimodal embedding models are rapidly evolving, teams may prefer a more modular solution that allows for easy future migration.
Given this gap in the market, we built Kubrick for teams who need more control over their data and architecture but don’t have the resources to build a semantic video search platform from scratch. Kubrick provides essential functionality without extraneous features while maintaining a modular architecture that helps teams stay future-proof and avoid vendor lock-in.
Kubrick’s Solution
Section titled “Kubrick’s Solution”Kubrick is designed for smaller teams who want to enhance their applications or organizations with scalable and performant semantic video search while retaining full ownership of their data and architecture. By focusing on the core challenge of accurate and efficient retrieval of relevant video content, Kubrick remains powerful yet flexible enough to adapt to a wide range of use cases.
Example Use Cases
Section titled “Example Use Cases”Internal Training Videos
Section titled “Internal Training Videos”Consider an organization with a large library of training videos looking to make them instantly accessible to staff on demand. Without Kubrick, they might rely on keyword search, hoping users know the exact title or metadata to find the right content. With Kubrick, the organization can ensure their staff can quickly access the right videos when they need them through its semantic search capabilities.
Legal Recordings
Section titled “Legal Recordings”A law firm stores thousands of hours of recorded depositions, hearings, and compliance training sessions. Lawyers and compliance officers often need to search across these recordings for key statements or evidence tied to specific cases. Kubrick enables precise, context-aware search directly within their secure AWS environment, maintaining control of sensitive data. The serverless model handles unpredictable query loads, like during a high-profile case or regulatory audit, without requiring constant infrastructure oversight.
Copyright Compliance
Section titled “Copyright Compliance”A video sharing site needs to ensure that user-submitted content does not contain copyrighted material from its library of licensed shows. With Kubrick’s multimodal search, compliance teams can check for matches using a single frame from a video, a short audio sample or clip of dialogue. Kubrick then scans the entire content library for semantically similar matches. The site is able to set the minimum similarity score to prevent false positives while also catching infringements that simple fingerprinting might miss.