Modern AI Architecture - Layers, Not Patches

We approach AI systems the same way we approach any critical enterprise platform: clear layers, well-defined service boundaries, controlled data flow, security by design, and full observability for the long run.

The goal is an infrastructure you can grow with - add models, services, data sources, and new use cases without rebuilding the foundations every quarter.

Core Architectural Components

Web & API Layer

ASP.NET / .NET 6+, REST/GraphQL, authentication and authorization, secure connectivity between clients, internal systems, and the LLM layer.

Service Layer / Microservices

Logically separated services for users, workflows, computation, and integrations - each with clear boundaries and independent scaling.

Data Layer

SQL Server, Azure SQL, blob storage, Redis, data lakes, vector store - schema design, indexing, partitioning, and reporting-ready models.

AI / LLM Layer

Dedicated AI services, integration with LLMs, vision and NLP, orchestration, context management, prompting, and caching.

Data Pipelines

ETL/ELT, batch and streaming flows, data cleansing, feature engineering, and consistent, controlled data delivery into models.

Messaging & Events

Message queues, event bus, pub/sub - decoupling between services, load smoothing, and real-time event-driven reactions.

Typical Technology Stack

  • Backend: ASP.NET Core / .NET 6+
  • Frontend: HTML5, CSS, JS, SPA where needed
  • Data: SQL Server, Azure SQL, Redis, Storage Accounts
  • Cloud: Azure (App Services, Functions, Container Apps)
  • Containers: Docker, Kubernetes at scale
  • Messaging: Azure Service Bus, Queues, Event Grid
  • AI: Azure OpenAI, dedicated models, self-hosted LLM when required
  • Security: OAuth2, OpenID Connect, Azure AD, secured logging
  • Observability: Application Insights, dashboards, alerts, MLOps

Engineering Process - End-to-End

  • Capture technical and non-functional requirements (performance, security, scalability).
  • Define high-level architecture and system diagrams.
  • Detailed design for every layer and service.
  • Proof-of-Concept for the LLM/AI layer and its integration points.
  • Incremental development, CI/CD, unit, integration and load testing.
  • Hardening, observability, and operational playbooks for day-2.

Related AI Services

This page is part of our broader engineering offering. Explore the parent AI Services hub, browse AI Solutions, review AI Integrations, visit the AI Hub, or jump straight to the AI FAQ.

Need a Technical Partner to Design and Build Your AI Architecture?

If you are looking for more than a "smart model" - a full system that holds load, stays secure, and remains maintainable - this is exactly the space we work in every day.

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