Core Insights - Orderfox Schweiz AG's Gieni AI platform was showcased at Microsoft Build 2025 as a reference case for vertical AI integration, demonstrating the capabilities of Microsoft's new Model Context Protocol (MCP) [1][5] - Gieni AI is one of the first vertical AI agents to offer an MCP Connector on the Microsoft Marketplace for Copilot Studio, providing market, competition, and risk intelligence directly within Microsoft 365 tools [2][6] - The integration of Gieni AI with Microsoft Copilot aims to enhance decision-making processes for businesses by delivering real-time insights within their existing workflows [3][9] Company Overview - Orderfox Schweiz AG, based in Zurich, specializes in developing AI-based platforms for the industrial and B2B sectors, including Gieni AI and Partfox [10] - Gieni AI processes data from over 380 million web pages and 5 million company profiles, utilizing proprietary semantic search and classification systems [8] - The platform is designed to help companies make smarter decisions, accelerate go-to-market strategies, and maintain a competitive edge by transforming data into actionable intelligence [9]

AI Development Capabilities - The industry is focusing on bringing AI development capabilities through Copilot, starting with code completion and moving towards chat interactions for complex prompts and multi-file changes [1] - Agent mode enables complete task execution with deep interaction, allowing for building apps or refactoring large codebases [2] - Agent mode can interpret readme files, including project structure, environment variable configurations, database schemas, API endpoints, and workflow graphs (even as images), to implement tasks [3][4][5] Model Context Protocol (MCP) - MCP is an open protocol (API for AI) that allows LLMs to connect to external data sources for general or account-specific information [9] - VS Code can be configured to use specific MCPs, allowing Copilot to select the appropriate MCP for a task and connect to it, whether local or remote [11][12] - Developers need to grant permission for Copilot to connect to MCPs, ensuring data access is controlled [20] - GitHub has its own MCP server, enabling actions like committing changes to a new branch and creating pull requests directly from the IDE [26][31] Workflow and Best Practices - Copilot Instructions, a specially named file, can be used to pre-inject standards and practices into every prompt, such as code style guidelines and security checks [28][29][30] - Including a change log of everything the agent has done provides a clear record of each step taken [30]

MCP and AI Agent Development - MCP is presented as a way of interfacing between AI and external resources, enabling functionalities like database access and complex computations [3] - The industry is currently focused on building internal demos and connecting them to APIs, but needs to move towards robust authentication and authorization [9][10] - The industry needs to adapt existing tooling for MCP due to its dynamic client registration, which can flood developer dashboards [12] Enterprise Readiness and Security - Scaling MCP servers requires addressing free credit abuse, bot blocking, and robust access controls [12] - Selling MCP solutions to enterprises necessitates SSO, lifecycle management, provisioning, fine-grained access controls, audit logs, and data loss prevention [12] - Regulations like GDPR impose specific logging requirements for AI workloads, which are not widely supported [12] Challenges and Future Development - Passing scope and access control between different AI workloads remains a significant challenge [13] - The MCP spec is actively developing, with features like elicitation (AI asking humans for input) still unstable [13] - Cloud vendors are solving cloud hosting, but authorization and access control are the hardest parts of enterprise deployment [13]