dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use.
With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.
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Iris provides integration-ready identity and cyber protection solutions that help organizations add powerful customer security capabilities directly into their existing products — without building from scratch. Designed for modern digital platforms, Iris makes it easy to embed identity protection into apps, portals, and customer experiences at scale.
Identity Protection API
Iris’ API suite delivers a multitude of protection solutions—including dark web monitoring & alerts, credit services, risk assessment tools, device protection, and more—into your platform. Teams can fully control the user experience, data flows, and customer journeys while leveraging Iris’ underlying technology and data aggregation.
Micro-Experiences
Prebuilt, customizable UI components that can be embedded directly into your application. These lightweight modules allow teams to quickly deploy identity protection features — such as alerts, dashboards, and monitoring tools — with minimal development effort.
Built for flexibility, Iris supports multiple integration approaches, enrollment methods, and data handling models, so organizations can choose how information flows between users, their systems, and Iris. The platform is designed to scale across large user bases while maintaining strong security and performance standards.
By making identity protection a native part of the user experience, Iris helps organizations increase engagement, strengthen trust, and deliver meaningful, always-on protection to their customers.
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DataChain
DataChain serves as a bridge between unstructured data found in cloud storage and AI models alongside APIs, facilitating immediate data insights by utilizing foundational models and API interactions to swiftly analyze unstructured files stored in various locations. Its Python-centric framework significantly enhances development speed, enabling a tenfold increase in productivity by eliminating SQL data silos and facilitating seamless data manipulation in Python. Furthermore, DataChain prioritizes dataset versioning, ensuring traceability and complete reproducibility for every dataset, which fosters effective collaboration among team members while maintaining data integrity. The platform empowers users to conduct analyses right where their data resides, keeping raw data intact in storage solutions like S3, GCP, Azure, or local environments, while metadata can be stored in less efficient data warehouses. DataChain provides versatile tools and integrations that are agnostic to cloud environments for both data storage and computation. Additionally, users can efficiently query their unstructured multi-modal data, implement smart AI filters to refine datasets for training, and capture snapshots of their unstructured data along with the code used for data selection and any associated metadata. This capability enhances user control over data management, making it an invaluable asset for data-intensive projects.
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Gesund.ai
Gesund stands as the pioneering compliant AI factory dedicated to facilitating the introduction of clinical-grade AI solutions into the market. In order to meet regulatory standards, our platform meticulously audits and validates third-party medical AI solutions, ensuring their safety, effectiveness, and fairness. Gesund seamlessly manages the entire AI/ML lifecycle for all participants by integrating models, data, and expertise within a user-friendly no-code environment. We offer standardized, cohesive, and diverse data tailored to meet your machine learning requirements and regulatory obligations. By evaluating the validation needs of models, Gesund.ai supplies an optimal combination of high-quality data sourced from its extensive network of clinical partners. Model owners can share their clinical studies with Gesund.ai to curate the necessary datasets, subsequently uploading their models onto Gesund.ai's federated validation platform, which can be situated on hospital premises or within a private cloud. Each model undergoes evaluation against a validation dataset that has been specifically curated on the hospital side, ensuring that the results are relevant and reliable, ultimately enhancing the quality of healthcare solutions. Through this comprehensive approach, Gesund not only supports compliance but also accelerates the path to effective AI deployment in clinical settings.
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