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From monolithic AI licenses to managed orchestration: A new architecture for B2B and B2G
The era of blanket purchasing of user AI licenses (SaaS seat-license models) in the corporate sector and government is facing a harsh reality. Organizational structures face inefficient costs for unused accounts, risks of data leakage outside the internal network, and technological dependence on a single supplier.
The complex infrastructure of large organizations requires a fundamental shift in approach – moving from isolated chat tools to a robust integration and orchestration layer. Our new GEM solution for AI transformation defines an architecture that combines advanced cost management with uncompromising data sovereignty.
Key technological pillars of modern enterprise AI:
Interface standardization using Model Context Protocol (MCP) Instead of ad-hoc APIs, we deploy the open MCP standard. This allows LLM models to access corporate data sources (filing services, ERP, CRM) securely, bidirectionally, and in context, without violating existing security policies and access rights defined within IAM (Identity and Access Management / Keycloak).
Hybrid Orchestration via Azure AI Foundry A fixed tie to a single LLM model is unsustainable from a cost and performance perspective. The orchestration layer dynamically routes sub-prompts to the most appropriate models (e.g. GPT-4o, Claude 3.5 Sonnet) based on the nature of the task. Enterprise governance ensures that data remains within an isolated corporate domain and queries are never used to train public models.
FinOps and Budget Cap We are replacing flat user fees with a pay-per-token model (payment for actual processed data). To eliminate the risk of uncontrolled cost increases, a financial ceiling mechanism (Budget Cap) is integrated into the orchestration layer. The administrator defines a maximum monthly budget for the entire organization or a specific department, which brings 100% predictability of the IT budget.
Vector Optimization (RAG) for Legislative Compliance For complex tasks, such as assessing large-scale RFPs against standards, inserting entire documents into a context window is financially unaffordable. We use semantic segmentation and storing texts in vector databases. The model uses Retrieval-Augmented Generation (RAG) to only retrieve the most mathematically relevant passages, which radically reduces token complexity and increases the accuracy of the outputs.
AI in a regulated environment is no longer an experiment, but a critical infrastructure that must meet strict audit, efficiency, and security criteria.
You can find a complete overview of our technological solutions here: https://www.gemsystem.cz/reseni-a-sluzby/
An overview of implemented projects and technological integrations is available here: https://www.gemsystem.cz/reference-11/
Detailed technical specifications for integration modules, case studies from the automotive and public administration sectors, and examples of MCP protocol integration in a production environment are being prepared in follow-up materials. Follow us so you don't miss practical examples of the next generation architecture.
#ModelContextProtocol #AIOrchestration #AzureAIFoundry #EnterpriseArchitecture #FinOps #GovTech #RetrievalAugmentedGeneration #DataSovereignty #B2BTech #InformationSecurity #GEMSystem
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