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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NINJIO
NINJIO is an all-in-one cybersecurity awareness training solution that lowers human-based cybersecurity risk through engaging training, personalized testing, and insightful reporting. This multi-pronged approach to training focuses on the latest attack vectors to build employee knowledge and the behavioral science behind human engineering to sharpen users’ intuition. Our proprietary NINJIO Risk Algorithm™ identifies users’ social engineering vulnerabilities based on phishing simulation data and informs content delivery to provide a personalized experience that changes individual behavior.
With NINJIO you get:
- NINJIO AWARE attack vector-based training that engages viewers with Hollywood style, micro learning episodes based on real hacks.
- NINJIO PHISH3D simulated phishing identifies the specific social engineering tricks most likely to fool users in your organization.
- NINJIO SENSE is our new behavioral science-based training course that shows employees what it “feels like” when hackers are trying to manipulate them.
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Pinecone
The AI Knowledge Platform.
The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems.
Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval.
Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters.
Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.
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Azure AI Search
Achieve exceptional response quality through a vector database specifically designed for advanced retrieval augmented generation (RAG) and contemporary search functionalities. Emphasize substantial growth with a robust, enterprise-ready vector database that inherently includes security, compliance, and ethical AI methodologies. Create superior applications utilizing advanced retrieval techniques that are underpinned by years of research and proven customer success. Effortlessly launch your generative AI application with integrated platforms and data sources, including seamless connections to AI models and frameworks. Facilitate the automatic data upload from an extensive array of compatible Azure and third-party sources. Enhance vector data processing with comprehensive features for extraction, chunking, enrichment, and vectorization, all streamlined in a single workflow. Offer support for diverse vector types, hybrid models, multilingual capabilities, and metadata filtering. Go beyond simple vector searches by incorporating keyword match scoring, reranking, geospatial search capabilities, and autocomplete features. This holistic approach ensures that your applications can meet a wide range of user needs and adapt to evolving demands.
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