RaimaDB
RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.
RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.
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Ango Hub
Ango Hub is an all-in-one, quality-oriented data annotation platform that AI teams can use. Ango Hub is available on-premise and in the cloud. It allows AI teams and their data annotation workforces to quickly and efficiently annotate their data without compromising quality.
Ango Hub is the only data annotation platform that focuses on quality. It features features that enhance the quality of your annotations. These include a centralized labeling system, a real time issue system, review workflows and sample label libraries. There is also consensus up to 30 on the same asset.
Ango Hub is versatile as well. It supports all data types that your team might require, including image, audio, text and native PDF. There are nearly twenty different labeling tools that you can use to annotate data. Some of these tools are unique to Ango hub, such as rotated bounding box, unlimited conditional questions, label relations and table-based labels for more complicated labeling tasks.
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Mendable.ai
Mendable is an innovative platform that harnesses AI to assist businesses in developing tailored chat applications by leveraging their existing technical assets, such as documentation and knowledge repositories. This approach not only cultivates AI-driven assistants that can efficiently handle inquiries from both customers and employees, but it also alleviates the burden on support teams while improving user interaction. The platform allows for smooth integration with a variety of data sources, including GitHub, Notion, Confluence, among others, which streamlines data ingestion and synchronization processes. Users have the flexibility to customize their AI models by choosing from base models like GPT-3.5-Turbo or GPT-4, and they can enhance response accuracy through answer corrections and prompt modifications that align with their brand's unique voice. Mendable also prioritizes enterprise-level security through features such as SOC 2 Type II certification, Single Sign-On (SSO) capabilities, role-based access control (RBAC), and the option to bring your own key or model (BYOK/BYOM), thus ensuring robust data protection and regulatory compliance. This comprehensive approach not only empowers organizations to build effective AI solutions but also fosters trust and security in the management of sensitive information.
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Vello AI
Vello is an exceptional platform for engaging with AI, offering rapid, robust, and collaborative multi-player AI chat that consolidates leading models and human partners in one accessible location. It is compatible across web, mobile, and desktop interfaces, providing a swift, keyboard-driven experience that enhances productivity. By integrating models from OpenAI, Anthropic, Google, Facebook, Cohere, and its proprietary web and document models, Vello facilitates immediate research with accurate source citations, effortless document composition, editing, and code refinement. The platform's team spaces foster collaboration between humans and AI, enabling users to work collaboratively in shared environments and chat rooms to complete tasks efficiently. Additionally, users have the ability to craft various AI personas, train them using diverse file types (including PDF, DOCX, code, CSV), and deploy them either individually or within multi-persona chat rooms, resulting in intricate workflows through round-robin model responses. This flexibility allows for a dynamic and highly customizable approach to utilizing AI technologies in a multitude of applications.
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