OORT DataHub
Data Collection and Labeling for AI Innovation.
Transform your AI development with our decentralized platform that connects you to worldwide data contributors. We combine global crowdsourcing with blockchain verification to deliver diverse, traceable datasets.
Global Network: Ensure AI models are trained on data that reflects diverse perspectives, reducing bias, and enhancing inclusivity.
Distributed and Transparent: Every piece of data is timestamped for provenance stored securely stored in the OORT cloud , and verified for integrity, creating a trustless ecosystem.
Ethical and Responsible AI Development: Ensure contributors retain autonomy with data ownership while making their data available for AI innovation in a transparent, fair, and secure environment
Quality Assured: Human verification ensures data meets rigorous standards
Access diverse data at scale. Verify data integrity. Get human-validated datasets for AI. Reduce costs while maintaining quality. Scale globally.
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Windocks
Windocks is a leader in cloud native database DevOps, recognized by Gartner as a Cool Vendor, and as an innovator by Bloor research in Test Data Management. Novartis, DriveTime, American Family Insurance, and other enterprises rely on Windocks for on-demand database environments for development, testing, and DevOps. Windocks software is easily downloaded for evaluation on standard Linux and Windows servers, for use on-premises or cloud, and for data delivery of SQL Server, Oracle, PostgreSQL, and MySQL to Docker containers or conventional database instances.
Windocks database orchestration allows for code-free end to end automated delivery. This includes masking, synthetic data, Git operations and access controls, as well as secrets management.
Windocks can be installed on standard Linux or Windows servers in minutes. It can also run on any public cloud infrastructure or on-premise infrastructure. One VM can host up 50 concurrent database environments.
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Bitext
Bitext provides multilingual, hybrid synthetic training datasets specifically designed for intent detection and LLM fine‑tuning. These datasets blend large-scale synthetic text generation with expert curation and linguistic annotation, covering lexical, syntactic, semantic, register, and stylistic variation, to enhance conversational models’ understanding, accuracy, and domain adaptation. For example, their open source customer‑support dataset features ~27,000 question–answer pairs (≈3.57 million tokens), 27 intents across 10 categories, 30 entity types, and 12 language‑generation tags, all anonymized to comply with privacy, bias, and anti‑hallucination standards. Bitext also offers vertical-specific datasets (e.g., travel, banking) and supports over 20 industries in multiple languages with more than 95% accuracy. Their hybrid approach ensures scalable, multilingual training data, privacy-compliant, bias-mitigated, and ready for seamless LLM improvement and deployment.
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