Welcome to CodableAI, where we revolutionize the way you handle and search your data. Our managed vector database service provides robust solutions for document search, enhanced AI context, and more.
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Effortlessly create, manage, and interact with advanced vector databases tailored to your needs.
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Build and utilize indexes that are optimized for fast and accurate vector searches.
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Enjoy peace of mind with comprehensive encryption for data at rest and in transit.
Learn MoreAt CodableAI, we specialize in AI-driven data solutions, enabling businesses to leverage the power of vector databases without the complexities of database management. Our mission is to provide secure, scalable, and efficient services that enhance your AI capabilities.
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At CodableAI, we leverage our proprietary CATCH (Contextual Association Through Conversational Hypervectors) approach enhanced with the LVC (Linked Vector Context) method. This cutting-edge technology allows us to manage and recall the context of conversations, documents, and chat histories more effectively and efficiently than traditional AI models.
The CATCH with LVC method transforms significant parts of conversations, documents, or long text sequences into high-dimensional vector embeddings. These embeddings encapsulate the semantic essence of the text, which are then stored in a vector database that excels in querying data based on similarity.
By storing conversation fragments as vector embeddings, our system can efficiently retrieve contextually relevant parts of a conversation, document, or chat history based on semantic similarity.
Our approach preserves the value from full file embeddings paired with context segmentation embeddings, providing rich, relevant, and contentful data from vector searches.
The CATCH approach balances computational efficiency with strong privacy norms, ensuring rapid processing and scalability.
By storing only vector representations and not the actual conversation, document, or chat history, we ensure that no sensitive or personally identifiable information is explicitly stored.
The LVC method adapts to the nature of the text, fine-tuning the mapping function periodically and dynamically adjusting the size and boundaries of text blocks.
Our system is designed to scale with your needs, allowing you to handle growing volumes of data efficiently without compromising performance.