32AI is a sophisticated platform designed to democratize the understanding of financial data through its proprietary Deep Factor AI technology. It processes and analyzes vast amounts of financial data to provide actionable insights, enabling users to make informed decisions with ease. Its technology assists in breaking down complex data into understandable and usable information, making financial analysis accessible to users of all expertise levels.

Constellab™ is an all-in-one open data platform designed to democratize data accessibility and utilization. It leverages AI to transform data into actionable insights, speeding up innovation in digital health, life sciences, agronomy, nutrition, and environmental industries. The platform provides tailored data labs and collaborative spaces, ensuring a user-friendly and secure data experience. Its goal is to enhance productivity and support the digital transformation of businesses by connecting data, AI, scientists, and the public.

Data Neko is a revolutionary no-code platform that facilitates data analytics and visualization. It eliminates the need for advanced technical skills, allowing users to effortlessly create insightful data visualizations. Businesses and individuals can leverage Data Neko to make data-driven decisions, streamline processes, and uncover actionable insights using intuitive tools and interfaces. This platform democratizes data analysis, making it accessible to a wider audience without sacrificing functionality or depth.

DXYFER is an augmented intelligence platform designed to transform data into actionable insights. By leveraging advanced AI technologies such as AskData, AskDocs, and AutoDash, users can seamlessly analyze, visualize, and enrich data. The platform is industry-agnostic and aims to democratize data intelligence, making it accessible to users of all skill levels. Its core features help to streamline data interactions and simplify complex data into comprehensible insights, empowering users to make informed, data-driven decisions.

The Enterprise Data Catalog aids organizations in managing their data landscape effectively by providing detailed metadata, facilitating data discovery, and supporting compliance initiatives. Users can effortlessly search and find data sets, understand data lineage, and collaborate across departments, leading to improved decision-making and operational efficiency. This tool enhances data democratization and empowers users with self-service analytics, contributing significantly to the overall data governance strategy.

Kyligence Zen is a low-code, AI-powered platform designed to centralize and manage all your business metrics in one place. It enables users to define, reuse, and analyze metrics efficiently, providing accurate decision support tailored for modern enterprises. The platform helps to democratize data analysis, making complex data accessible and actionable for every team member, thus facilitating timely and strategic decisions.

Select Star is an automated data catalog and discovery platform aimed at streamlining data governance processes. It provides organizations with a reliable single source of truth for their data, automatically analyzing and documenting data lineage. With its intuitive web interface, users can quickly explore and categorize their data assets, ensuring that all data consumers have access to the knowledge base necessary for effective decision-making. The platform promotes data democratization by making it easier for all users, regardless of technical expertise, to find and utilizes the data they need.

SQLPilot is a cutting-edge SQL editor that leverages artificial intelligence to help users create SQL queries efficiently. By allowing you to input queries in natural language, SQLPilot translates them into optimized SQL code. It connects seamlessly to various database sources, ensuring that you can get accurate results without needing to memorize complex syntax. Perfect for developers, data analysts, and anyone requiring SQL capabilities, SQLPilot aims to democratize data access and enhance existing workflows with faster query generation.

DataAgent leverages advanced AI agents built on top of LLMs to explore datasets, generate insights, and assemble machine learning pipelines automatically. Users point DataAgent at a CSV, SQL table, or Pandas DataFrame and pose questions in natural language. The agent interprets queries, executes analysis code, visualizes results, and even writes modular Python scripts for ETL and modeling tasks. It streamlines the entire data science workflow by reducing boilerplate coding and accelerating experimentation.