RaimaDB
RaimaDB is an embedded time series database designed specifically for Edge and IoT devices, capable of operating entirely in-memory. This powerful and lightweight relational database management system (RDBMS) is not only secure but has also been validated by over 20,000 developers globally, with deployments exceeding 25 million instances. It excels in high-performance environments and is tailored for critical applications across various sectors, particularly in edge computing and IoT. Its efficient architecture makes it particularly suitable for systems with limited resources, offering both in-memory and persistent storage capabilities. RaimaDB supports versatile data modeling, accommodating traditional relational approaches alongside direct relationships via network model sets. The database guarantees data integrity with ACID-compliant transactions and employs a variety of advanced indexing techniques, including B+Tree, Hash Table, R-Tree, and AVL-Tree, to enhance data accessibility and reliability. Furthermore, it is designed to handle real-time processing demands, featuring multi-version concurrency control (MVCC) and snapshot isolation, which collectively position it as a dependable choice for applications where both speed and stability are essential. This combination of features makes RaimaDB an invaluable asset for developers looking to optimize performance in their applications.
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AddSearch
AddSearch transforms the way organizations connect users with information. More than just a traditional site search, AddSearch now offers AI Answers and AI Conversations, enabling businesses to deliver direct, conversational, and context-aware responses to user queries. These advanced capabilities complement AddSearch’s proven site search and content recommendation solutions, helping organizations create effortless, engaging, and personalized digital experiences.
With AddSearch, you can choose between AI-driven answers, conversational interfaces, or lightning-fast search results—all fully customizable for websites, e-commerce platforms, or web applications. Our Crawler and Indexing API ensure your content is always up-to-date, while our expert implementation services save valuable developer time and maximize results.
Today, nearly 2,000 customers worldwide—across Media, Telecommunications, Government, Education, E-commerce, and more—trust AddSearch to provide best-in-class search and AI-driven discovery.
AddSearch product portfolio includes:
- AI Answers – instant, accurate, and direct responses powered by generative AI.
- AI Conversations – natural, chat-like interactions for deeper user engagement.
- Autocomplete & Smart Ranking – predictive suggestions and optimized result ordering.
- Personalized Search – tailored experiences based on behavior and preferences.
- Content & Product Recommendations – boost engagement and conversions.
- Advanced Analytics – insights into user behavior
- Flexible Content Controls – include/exclude content, synonyms, filters, and facets, promote
- Enterprise Features – SSO, organizational user management, audit logs, SLA up to 99.999%.
- Seamless Implementation – works with any CMS, via crawler or API
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SQL Index Manager
SQL Index Manager offers an intuitive method for evaluating the condition of your indexes and pinpointing which databases need attention. It gives users the flexibility to either perform maintenance through a graphical interface or generate a T-SQL script suitable for use in SQL Server Management Studio. The tool effectively identifies index fragmentation and provides a detailed report outlining the severity of the fragmentation along with the root causes. Users have the option to target specific indexes for repair, with SQL Index Manager taking care of the required operations seamlessly. You can choose to either reorganize or rebuild indexes while setting custom thresholds for each method, and those utilizing SQL Server Enterprise Edition can take advantage of online index rebuilds. Moreover, it facilitates long-term monitoring by enabling the export of findings, which helps in tracking and analyzing fragmentation trends over time. It also produces T-SQL scripts automatically to streamline the fragmentation-fixing process, allowing users to initiate the rebuilding or reorganizing of selected indexes with a single click, thereby improving efficiency. The combination of these features not only simplifies database maintenance but also enhances its effectiveness, making it an invaluable tool for database administrators. Additionally, the ability to customize maintenance strategies further empowers users to optimize their database performance.
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Zilliz Cloud
While working with structured data is relatively straightforward, a significant majority—over 80%—of data generated today is unstructured, necessitating a different methodology. Machine learning plays a crucial role by transforming unstructured data into high-dimensional numerical vectors, which facilitates the discovery of underlying patterns and relationships within that data. However, conventional databases are not designed to handle vectors or embeddings, falling short in addressing the scalability and performance demands posed by unstructured data.
Zilliz Cloud is a cutting-edge, cloud-native vector database that efficiently stores, indexes, and searches through billions of embedding vectors, enabling sophisticated enterprise-level applications like similarity search, recommendation systems, and anomaly detection.
Built upon the widely-used open-source vector database Milvus, Zilliz Cloud seamlessly integrates with vectorizers from notable providers such as OpenAI, Cohere, and HuggingFace, among others. This dedicated platform is specifically engineered to tackle the complexities of managing vast numbers of embeddings, simplifying the process of developing scalable applications that can meet the needs of modern data challenges. Moreover, Zilliz Cloud not only enhances performance but also empowers organizations to harness the full potential of their unstructured data like never before.
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