Artificial Intelligence is no longer a feature enhancement—it’s a paradigm shift. In a world increasingly driven by automation and data intelligence, an AI-First approach to software development means rethinking architecture from the ground up. Inspired by McKinsey’s AI-first architecture for digital banking, this guide adapts the model to suit the .NET ecosystem, helping teams modernize, innovate, and scale intelligently.

Here’s how to implement an AI-first blueprint for enterprise-grade .NET applications, across four foundational layers:

1️⃣ User Layer – Smart Interface & Context-Aware Experiences

The user interface is no longer just about aesthetics—it's the front line of intelligent interaction.

🔧 Key Components:

• UI Frameworks:

  • Blazor (WASM/Server) – for modern SPA experiences with C#.
  • ASP.NET Core MVC / Razor Pages – for robust, server-rendered apps.
  • MAUI – for building cross-platform native mobile + desktop apps.

• Multimodal Input:

  • Integrate services like Azure Cognitive Services, OpenAI Whisper, or Google Speech-to-Text for voice commands.
  • Use Azure Computer Vision or OCR APIs for image-based interactions.

• AI-Powered Personalization:

  • Capture user behavior using Application Insights or Azure Monitor.
  • Apply embedded ML models (via ML.NET, ONNX Runtime) to adapt UI layouts, suggest actions, or hide irrelevant features.

💡 Example: A Blazor-based dashboard that reorders UI components based on user role, engagement level, or usage frequency.

2️⃣ Intelligence Layer – Embedded AI, LLMs & Smart Agents

This layer brings dynamic intelligence directly into the application’s core logic.

🧠 Key Technologies:

• Model Integration:

  • Train and run models using ML.NET, Azure Machine Learning, or ONNX Runtime.
  • Consume OpenAI models via Azure OpenAI for summarization, classification, and NLP.

• Domain-Specific Use Cases:

  • Finance: Real-time fraud detection with anomaly detection models.
  • E-commerce: Personalized recommendations using user embedding vectors.
  • SaaS: Onboarding chatbots and dynamic UI flows generated by GPT models.

• Developer & Ops Copilots:

  • Use GitHub Copilot, Azure DevOps GPT extensions, or internal copilots that scaffold code, write tests, or generate API docs.

💡 Imagine a command-line tool that uses an LLM to generate application modules from a user story—services, DTOs, and EF entities included.

3️⃣ Infrastructure Layer – AI-Ready Data, LLM Pipelines, and Scalable Hosting

Your infrastructure must support scalable data flow, model experimentation, and rapid deployment.

🏗️ Key Stack Elements:

• Data Management:

  • ORM: Entity Framework Core, Dapper for efficient access.
  • NoSQL: Cosmos DB, Redis for unstructured and session data.
  • Streaming: Event Hubs, Kafka, or Azure Data Explorer for real-time ingestion.

• Semantic Search & Vector Databases:

  • Use Qdrant, Weaviate, or Pinecone to power:
    • Retrieval-Augmented Generation (RAG)
    • Personalized semantic search
    • Document clustering and tagging

• CI/CD + MLOps:

  • Extend pipelines with GitHub Actions, Azure DevOps, and MLflow:
    • Automate model training + deployment
    • Enable versioning and rollback
    • Monitor inference performance

💡 Version AI models just like microservices—A/B test, observe, and roll back when needed.

4️⃣ Operating Layer – Org Structure, Governance & Monitoring

AI-first success is just as much about people and processes as it is about code.

🧑‍💼 Organizational Shifts:

• New Roles:

  • AI Architects – system-level design + integration.
  • ML Engineers – manage pipelines, training, and MLOps.
  • Prompt Engineers – craft reusable, context-rich instructions for LLMs.

• Cross-Functional Teams:

  • Squads with .NET developers, data scientists, DevOps, and product managers.
  • Use Domain-Driven Design (DDD) to decentralize ownership of intelligent modules.

• Monitoring & KPIs:

  • Build dashboards using Power BI, Grafana, or Azure Monitor to track:
    • Model accuracy + latency
    • Usage of AI-powered features
    • AI contribution to business KPIs (e.g., churn reduction, increased activation)

💡 Run quarterly AI audits to catch model drift, evaluate prompt effectiveness, and re-align features with product goals.

✅ Summary: .NET + AI = Future-Ready Architecture

Being AI-first isn’t just about sprinkling in some machine learning. It’s about designing around intelligence—end to end.

| Layer | Focus Areas | |-------|-------------| | User Layer | Blazor, MAUI, adaptive UI with telemetry and ML | | Intelligence Layer | ML.NET, Azure AI, OpenAI integrations | | Infrastructure Layer | Vector DBs, CI/CD for models, scalable APIs | | Operating Layer | Org roles, DDD teams, monitoring and AI impact tracking |

🎯 Final Thoughts

.NET developers are uniquely positioned to lead the AI-first transformation—thanks to Microsoft's investment in Azure AI, .NET ML tools, and ecosystem readiness.

Teams that architect with AI at the center, not just as an addon, will lead the next generation of enterprise-grade applications—smart, scalable, and self-improving.

💬 Don’t just use AI. Design for it.

Key Speakers:

• Alper Ebicoglu: ABP Core Team, provided updates on the ABP Framework and community contributions.

• Halil İbrahim Kalkan: ABP Core Team, presented on modular system design and implementation with ABP Framework.

• Ariful Islam: Co-founder and COO of Wafi Solutions, shared insights on using ABP in real-world projects.

I. ABP Community News (Alper Ebicoglu)

• Highlighted recent articles on the ABP Community platform covering topics like:

• Multilanguage functionality in ABP Framework.

• Cookies vs. local storage in web applications.

• .NET 9 features and ABP Framework compatibility.

• Integration of ABP with Inertia.js, React, and Vite.

• SignalR enhancements in .NET 9.

• Announced the release of ABP Framework version 8.3 with numerous bug fixes and improvements.

• Recognized top contributors to the ABP Community platform.

II. Modular Monolith Application Development (Halil İbrahim Kalkan)

A. Why Modularity?

• Reducing and managing code complexity: Enables parallel development, simplifies code maintenance, and allows for easier onboarding of new developers.

• Modular deployment: Allows customized deployments based on client needs, including or excluding specific modules.

• Microservice migration: Provides a foundation for future migration to a microservice architecture.

B. Module Types:

• Business Modules: Core functionalities directly related to the application’s business logic.

• Aggregator Modules: Combine data from multiple modules, typically for reporting or dashboards.

• Infrastructure Modules: Provide shared functionalities like settings management, permission management, background jobs, etc.

• Generic Modules: Offer cross-cutting concerns like audit logging, GDPR compliance, etc.

C. Architectural Considerations:

• Database: Single database: Easier for reporting and transactions, with potential scaling limitations.

• Separate databases: Improved data isolation, scalability, and more complex reporting and transactions.

• Codebase: Single solution: Easier for small teams and rapid development, potential for tighter coupling.

• Separate solutions: Encourages module isolation and increased complexity for multi-module development.

• Monorepo vs. multirepo: Monorepo simplifies versioning and deployment, while multirepo allows for more independent development.

• User Interface:

  • Unified UI: Simpler to develop pages utilizing multiple modules, less modular UI.

  • Modular UI: Promotes UI module isolation and challenges in integrating UI across modules.

  • Component-based UI: Balances modularity with cross-module integration.

D. Module Integration:

• Dependencies: Modules should depend on each other’s contracts (interfaces, DTOs, events), not implementations.

• Communication patterns: Direct API calls are suitable for read operations and prioritize caching.

• Integration events: Best for side effects triggered by changes in one module affecting another.

E. ABP Framework Tutorial:

• Demonstrated a step-by-step tutorial available on the ABP Framework website, covering:

• Creating a new solution with ABP Studio.

• Building modules with DDD principles.

• Implementing module integration using services and events.

• Performing database operations across modules.

III. Real-World ABP Experiences (Ariful Islam)

• Shared experiences from using ABP Framework for various projects, including large-scale SaaS applications.

• Highlighted the benefits of ABP in addressing challenges like:

• Multi-tenancy implementation.

• Supporting both cloud and on-premise deployments.

• Implementing recurring payments with Stripe.

• Building modular applications tailored to diverse customer needs.

• Emphasized the value of ABP’s:

• Pre-built modules and features (multi-tenancy, roles, settings, identity).

• DDD implementation and architectural guidance.

• Documentation and open-source projects as learning resources.

• Customization capabilities through extension methods and data filters.

IV. Q&A Session

• Addressed various community questions, including:

• Creating projects with multiple modules and Angular UI.

• Availability of simplified module templates.

• Choosing between modular monolith and single-layered architecture.

• Security controls are available in ABP Framework.

• Plans to move away from jQuery in ABP’s UI.

V. Key Takeaways:

• ABP Framework provides a robust foundation for building modular monolith applications with .NET.

• Modularity offers significant advantages for managing complexity, enabling flexible deployment, and easing potential microservice migration.

• The ABP Framework community is active and offers valuable resources for learning and sharing experiences.

• The ABP team continuously evolves the framework, adds new features, and addresses community needs.

Quotes:

“I honestly did not find any alternative to ABP for SaaS applications. It is very easy to get started.” — Ariful Islam.

“ABP provides us with nice architecture and nice documentation. We learn a lot from ABP documentation and the projects.” — Ariful Islam.

“ABP Studio is not a replacement for ABP Suite. You can use ABP Suite to create CRUD pages, and it’s inside ABP Studio.” — Alper Ebicoglu.

This briefing document provides an overview of the key discussions and insights from the ABP Community Talks. For more detailed information, it is recommended that you review the full recording and access the resources mentioned.