Copilot for Architects: enterprise architecture practice using Azure AI
Through “Copilot for Architects”, I intend to share a glimpse of how the modern Enterprise Architecture practice looks like in the era of Generative AI for Enterprise Architects.
Note that Azure AI can now see, hear, speak, draw and chat.
I will take you through the Theory as well as the Practical example in this article.
Task: To build a Copilot for Architects.
Technologies used:
- Azure OpenAI Service
- GPT-4 Turbo with Vision (GPT-4V with temperature 0.1)
Enterprise Architecture practice use case:
Ingestion of architecture diagram, creation of architecture overview, creation of functional and non-functional requirements, creation of context diagram, explanation of context diagram, creation of integration architecture from the main architecture.
Theory:
The introduction of Azure Generative AI’s GPT-4 Turbo with Vision (GPT-4V) significantly transforms the practice of enterprise architecture, especially in creating and analyzing architectural designs. GPT-4V is a large multimodal model that combines the capabilities of natural language processing with visual understanding, enabling it to analyze images and provide detailed textual responses about them.
For enterprise architects, this means that GPT-4V can assist in interpreting and understanding architectural diagrams, blueprints, and visual data more efficiently. It goes beyond simple object recognition in images; it can understand context, provide elaborate image captions, offer rich contextual descriptions, respond to queries about visual content, or create diagrams. This advanced level of image understanding opens up new avenues for architects to interact with their designs and data visually.
One practical application of GPT-4V in enterprise architecture could be in analyzing handwritten notes or sketches. For instance, GPT-4V can convert handwritten architectural notes and sketches directly into digital formats, making it easier to integrate these ideas into digital project plans or presentations. This not only saves time but also enhances collaboration among team members by digitizing and centralizing access to information.
Moreover, GPT-4V’s integration with Azure AI Vision can amplify its capabilities by providing Optical Character Recognition (OCR) and object grounding features. The OCR feature can interpret dense text and numbers in images, which is particularly useful for analyzing documents like technical specifications or financial projections in an architectural context. The object grounding feature, on the other hand, can highlight important elements in images, providing a new layer of data analysis and user interaction. This can be particularly useful for identifying key components in architectural designs or for tagging and organizing architectural elements within a project.
The combination of GPT-4V with Azure AI Vision and Search also enables direct lookups from image inputs over organizational data, significantly improving the accuracy of natural language processing and image recognition tasks. This can enable new generative AI scenarios, such as using video inputs for comprehensive analysis of architectural walkthroughs or evaluations, thereby offering a more dynamic and interactive way to assess and present architectural projects.
In summary, Azure Generative AI’s GPT-4 Turbo with Vision offers enterprise architects a powerful tool to enhance their workflow, from digitizing handwritten notes or diagrams to conducting in-depth analyses of architectural designs and visuals. This integration of advanced AI capabilities with visual data analysis opens up innovative possibilities for creating, understanding, and communicating architectural concepts and designs.
Practical:
I uploaded a manually created diagram of Copilot Studio Architecture to the playground.
I input the relevant prompt (as shown below) to explain the architecture and derive functional and non-funtional requirements from thereon which will be used by an AI application powered by this Copilot Studio Architecture.
Azure OpenAI playground created the architectural description here.
It then derived the requirements as following:
As we know, in enterprise architecture, context diagrams are invaluable for setting the stage for more detailed architectural planning and analysis. They provide a foundational understanding of how a system fits within its broader environment, which is essential for designing effective and relevant architectural solutions.
I asked to create a context diagram from the given architecture as shown below.
Azure OpenAI created the context diagram and its description.
Furthermore, we know the importance of integration diagram in enterprise architecture. This diagrams extends to simplifying complex IT processes by providing a clear picture of the middleware or interface layers that enable communication between disparate systems. This simplification is essential for both technical and non-technical stakeholders to grasp the underlying architecture and its impact on business operations. By facilitating a common understanding, integration diagrams support more effective collaboration and decision-making, helping to streamline processes and maximize opportunities for innovation and growth within the organization.
So, I asked Azure OpenAI service (powered by GPT-4V LLM) to create the integration diagram leveraging the Copilot Studio architecture.
I could further create many more artefacts to support the enterprise architecture practice and the promise it delivers to ensure smooth delivery.
→ → → → Please share, clap and comment with your feedback. ← ← ← ←
Disclaimer: Personal post. Personal point of view.
Vishal Anand.
Chief Technologist, Master Inventor, Distinguished Architect, Fellow of BCS, Top Voice on AI.
