Cubed's core functionality revolves around its ability to streamline the process of working with large datasets and complex analytics tasks. It provides a unified interface for interacting with various data sources, allowing users to easily integrate and analyze information from multiple origins. This capability is particularly useful for businesses dealing with diverse data ecosystems, as it eliminates the need for manual data integration and reduces the potential for errors in the analysis process.


One of the key strengths of Cubed is its AI API, which incorporates advanced features such as Retrieval Augmented Generation (RAG) and prompt engineering. This API allows Large Language Models (LLMs) to interface seamlessly with Cube's universal semantic layer, significantly simplifying the development of AI applications and natural language capabilities in data products. The AI API is designed to work out of the box with OpenAI, providing a turnkey solution for businesses looking to incorporate AI into their data analytics workflows.


Cubed also offers powerful data visualization capabilities. Its chart prototyping feature allows users to generate front-end TypeScript code directly from the platform's Playground 2.0, accelerating the time-to-market for embedded analytics use cases. Users can preview various chart types before embedding them in their applications and choose between native REST or GraphQL APIs for integration.


The platform's universal semantic layer serves as a crucial component in ensuring data trustworthiness and security. It provides the necessary context for LLMs to make sense of data and generate accurate outputs, addressing common issues such as hallucinations in AI-generated content. This feature is particularly valuable for businesses that rely on precise data interpretation for decision-making processes.


Cubed is designed with user-friendliness in mind, offering a low barrier to entry for users of all skill levels. Its natural language interaction capabilities allow even non-technical users to benefit from AI solutions through intelligent, conversational data interactions. This democratization of data analytics can lead to more widespread adoption of data-driven decision-making across organizations.


Key features of Cubed include:


  • Universal semantic layer for optimized data management
  • AI API with RAG and prompt engineering capabilities
  • Chart prototyping for rapid embedded analytics development
  • Support for multiple data sources and integration methods
  • Natural language processing for data queries
  • Automated conversion of pre-trained AI algorithms
  • Optimization for various neural network and machine learning models
  • Performance measurement and profiling tools
  • Support for quantized neural network formats
  • Relocatable option for standalone model updates
  • Compatibility with external flash memory and RAM for larger networks
  • Easy portability across different microcontroller series
  • Integration with popular deep learning frameworks
  • Support for classical machine learning models
  • Customizable query templates for common data operations

  • Cubed represents a significant advancement in making data analytics and AI integration more accessible and efficient for businesses of all sizes, enabling them to harness the power of their data more effectively.


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