AI shouldn’t be treated as an IT project. It should be part of your overall strategy and IT architecture — making data, processes, and knowledge accessible across platforms in a secure, contextual, and controlled way.
AI Service Foundation
As with any real-life construction project, the foundation determines long-term stability. At the core of the architecture is our AI Service Foundation, which manages access, context, and integration across systems.
It’s built on the Model Context Protocol (MCP), ensuring that different AI agents — public, external, and internal — can work on the same datasets with the correct authorization.
Through components such as:
- AI Search Semantic Vector Engine – semantic search across internal and external data sources.
- Data Injection Pipelines – automated data ingestion and embedding using Azure AI Document Intelligence and OpenAI text-embedding models.
- Data Lookup Services – controlled access to CRM, ERP, CDP, and document repositories.
- Operation Services – enabling AI agents to perform business actions.
...both internal and application-specific agents (such as Microsoft Copilot, Jira’s Rovo, or website chatbots) can access your organizational knowledge — but only within the defined access levels that you control.
Controlled Access and Data Security
Access is governed through MCP Access Control, which enforces principles such as “On Behalf Of”. This ensures AI agents only see the data and perform the actions they’re authorized to.
For example, a public agent on your website might only have access to product information, while an internal agent in Jira can access technical documents and project data.
From Websites to Internal Systems
When we design your architecture, it enables AI agents to operate across multiple environments:
- Public websites and extranets with contextual search and conversational interfaces.
- Application-specific AI agents (e.g., Copilot, Rovo) directly connected to internal data sources.
- Internal agents within service and development environments, with full context from CRM and ERP systems.
Why Embedding AI in the Architecture Matters
The goal is to operationalize AI as a context-aware technology layer — connecting data, processes, and users through shared protocols and semantics.
It may sound technical, but it’s ultimately about making AI a natural part of your digital infrastructure — not a separate tool, but an embedded component of your operations, used correctly and effectively across the organization.
Let’s talk about how you can best leverage AI within your architecture.
Contact us to learn how we can help.
ROLAND VILLEMOES
CTO
