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What is SSAS?

When implemented as an on-premises server, SQL Server Analysis Services offers extensive support for multiple model types, such as tabular models at different compatibility levels depending on the version, multidimensional models, data mining features, and Power Pivot functionalities for SharePoint. The typical implementation process consists of establishing a SQL Server Analysis Services instance, creating either a tabular or multidimensional data model, deploying this model as a database to the server instance, processing it to fill it with data, and setting up user permissions to enable data access. After this setup is finalized, client applications compatible with Analysis Services can readily access the data model as a source. These data models often aggregate information from external systems, primarily retrieving data from data warehouses that utilize SQL Server or Oracle relational database engines; however, tabular models are also capable of connecting to various other data sources. This flexibility and range of capabilities underscore the strength of SQL Server Analysis Services as a formidable resource for analytics and business intelligence, allowing organizations to derive meaningful insights from their data. Ultimately, such robust functionality positions SQL Server Analysis Services as an essential component for enterprises aiming to enhance their analytical capabilities.

What is Boost.space?

Boost.space is a no-code platform designed to transform fragmented business data into a structured, synchronized context layer for AI agents and automation systems. Acting as an Agentic Database, it centralizes information from CRM platforms, ecommerce tools, billing systems, marketing channels, and support software into a unified Single Source of Truth. This consolidation eliminates duplication, inconsistencies, and outdated records that typically prevent AI from operating effectively. Through continuous two-way synchronization, Boost.space ensures all connected systems remain aligned in real time. The platform enhances unified datasets with built-in AI enrichment capabilities, automatically classifying records, normalizing fields, generating structured attributes, and translating content at scale. With workflow integrations for tools like Make and planned support for Zapier and n8n, users can build automation scenarios directly on top of standardized data. Its Model Context Protocol (MCP) connects large language models to live business data, allowing AI agents to retrieve computed answers and execute cross-system actions without relying on static exports. This shifts AI from being a passive chatbot to becoming an active operator within business processes. Boost.space supports common use cases in ecommerce product information management, CRM synchronization, multichannel outreach, and performance marketing powered by first-party data. Security and compliance standards such as ISO 27001, SOC-2, GDPR, and Data Act alignment provide enterprise confidence. The platform is trusted by thousands of teams worldwide seeking scalable AI readiness without adding operational overhead. By orchestrating data centralization, enrichment, synchronization, and AI connectivity, Boost.space enables organizations to unlock real AI execution across their entire technology stack.