AI Software Architect Course with the SVILUPPATORE MIGLIORE method
Stop adding AI as a decorative feature. Learn to design intelligent, reliable and scalable systems with direct mentoring from an architect with 25 years of enterprise project experience.
1-on-1 Mentor On YOUR code Measurable results
AI has entered companies. But in most cases it has not entered the architecture: it has been glued on top.
Chatbots pasted onto APIs. OpenAI calls inserted into controllers. RAG pipelines built without a coherent data model. Token costs exploding without monitoring. Hallucinations not handled that reach production.
The problem is not AI. The problem is that nobody has taught how to design an intelligent system.
Those who have designed microservices, DDD and CQRS know that complexity is governed with deliberate architectures, not with trial and error. The same applies to AI: Semantic Kernel, RAG, agents, vector search and multi-model orchestration require architectural decisions, not just API calls.
If you are a software architect, CTO or senior developer evaluating how to integrate AI into production systems, this programme was designed for you.
A curriculum built by someone who has been designing enterprise AI architectures for years, not someone who read the OpenAI documentation.
Why AI architectures fail in production
Most AI systems fail in production not because of model limitations, but due to wrong architectural decisions made in the first few weeks.
Here are the patterns we see most often:
- LLMs called directly from controllers without an abstraction layer
- RAG pipelines without chunking strategy, resulting in poor retrieval quality
- Hardcoded prompts in code without versioning
- No fallback when the model does not respond or responds poorly
- Unmonitored API costs growing silently
- No separation between deterministic logic and generative logic
Each point is an architectural risk, not an operational problem. It is solved in design, not in hotfixes.
This programme teaches you to recognise them, avoid them and design systems that hold up under real production load.
What you learn: the complete programme
3 phases, 10 modules of intensive mentoring on YOUR system.
Each module works on real code from your architecture, not academic demos. The result is an enterprise-grade AI system that your team knows how to maintain, evolve and measure.
3 phases, 10 modules of enterprise AI architecture mentoring.
PHASE 1: Architectural Foundations for AI Systems (Weeks 1-3)
- 1
How not to build AI systems: the 7 architectural mistakes found in production
Before building, you need to recognise what makes an enterprise AI system fragile. This module analyses the most common wrong patterns and teaches how to diagnose them in existing code. Those who build AI systems without having seen one fail in production replicate the same mistakes. This module compresses years of experience into a diagnostic checklist applicable immediately.
Programma
- LLM in the controllerWhy coupling AI and HTTP endpoints is the recipe for an unmanageable system and how to separate them
- Hardcoded promptsPrompt versioning, code separation and test strategies without calling the model
- No fallbackHow to design degraded mode for when the model is slow, expensive or unreliable
- RAG without chunking strategyHow wrong chunking ruins retrieval quality regardless of the model used
- Unmonitored costsHow to insert token tracking at the architectural level before the bill becomes a problem
- No observabilityHow to trace prompts, responses and latency with Application Insights and structured logging
Risultato
After this module you can diagnose an existing AI system in 30 minutes. You know what to fix before writing a single line of new code.
- 2
The AI layer: how to isolate artificial intelligence from the domain
The AI layer is the abstraction that separates generative logic from domain logic. Without it, every change to the model or provider impacts the entire system. The principle is the same as any other infrastructure component: depend on the abstraction, not the implementation. But in AI the boundary is less obvious because the model is also a service, a behaviour and a cost.
Programma
- IAiService and implementationsHow to define AI interfaces that decouple the domain from Semantic Kernel, OpenAI SDK or any provider
- AIContext and AIRequestHow to model AI requests as domain objects instead of strings and dictionaries
- Result pattern for AIHow to handle partial responses, model errors and timeouts without unhandled exceptions
- Dependency injection of the AI layerHow to configure the container to swap providers between environments (mock in test, OpenAI in staging, Azure OpenAI in prod)
- Testing without a real modelHow to write unit tests on AI code with fakes and stubs without paying test tokens
- AI behaviour versioningHow to manage differences between model versions that change the expected output
Risultato
Your AI system has a clear boundary. You can change provider, update the model or insert a mock without touching the domain. Tests pass without an internet connection.
- 3
Semantic Kernel in enterprise architectures: beyond the tutorials
Semantic Kernel is not just an SDK for calling OpenAI. It is an orchestration framework with plugins, memory, planner and function calling. Using it well requires understanding how it fits into an existing DDD or CQRS system. Most tutorials show Semantic Kernel in isolation. In production you insert it into a system that already has repositories, services, domain events and authorisation layers. This module teaches real integration.
Programma
- Kernel as singleton serviceCorrect lifecycle configuration, connection management and thread safety
- Plugins from domain servicesHow to expose business logic as function calling without violating domain boundaries
- Memory and VectorStoreWhen to use volatile vs persistent memory and how to choose the right vector store (Azure AI Search, Qdrant, in-memory)
- Chat history and contextHow to manage conversational history without exploding the token count
- Planner and agentsWhen it makes sense to use an automatic planner and when manual orchestration is better
- Metrics and observabilityHow to connect Semantic Kernel to OpenTelemetry and Application Insights
Risultato
Semantic Kernel stops being a black box and becomes an architectural component. You insert it where needed, test it like any other service, monitor it with the same tools as the rest of the system.
PHASE 2: RAG and Retrieval Architectures (Weeks 4-7)
- 4
Enterprise RAG: from prototype pipeline to production system
RAG works in tutorials. In production it needs a chunking strategy, a robust indexing pipeline, calibrated retrieval logic and a quality evaluation system. The difference between a RAG that impresses in demos and one that works in production is entirely architectural: how documents are structured, how they are split, how relevance is calculated and how the response is handled when retrieval fails.
Programma
- Chunking strategyFixed-size vs semantic vs hierarchical chunking and how to choose based on document type
- Indexing pipelineHow to build an asynchronous, idempotent and monitorable process for indexing large volumes
- Hybrid searchHow to combine vector and full-text search for more precise retrieval
- Re-rankingWhen and how to add a second scoring step to improve result quality
- Context management in the promptHow to structure retrieved chunks in the prompt without exceeding the context window
- RAG evaluationHow to measure precision, recall and faithfulness without manual human intervention
Risultato
Your RAG pipeline is no longer a prototype. It has a documented indexing strategy, monitorable quality metrics and predictable behaviour on documents it has never seen.
- 5
Vector databases in production: Azure AI Search, Qdrant and architectural choices
Choosing a vector database is not a technology preference: it is an architectural decision with implications for costs, scalability, latency and operability. Azure AI Search and Qdrant are not interchangeable. They have different cost models, different scalability curves and different integrations with the .NET ecosystem. Choosing without understanding the differences means having to migrate in production.
Programma
- Azure AI SearchWhen to use it, how to configure hybrid indexes, semantic ranking and metadata filters
- QdrantSelf-hosted vs cloud deploy, collection configuration, payload filtering and performance tuning
- Document schemaHow to design vector and metadata structure to support filters, authorisations and multi-tenancy
- Migration between vector storesHow to design the abstraction that allows changing backends without rewriting the pipeline
- Costs and scalabilityComparative cost models and how to estimate budget for a production RAG system
- Security and authorisationsHow to implement row-level security in vector stores for multi-tenant systems
Risultato
You know which vector store to choose for your use case, how to configure it for production and how to abstract it to avoid being locked in by the initial choice.
- 6
Embeddings, models and cost optimisation
Embeddings are the core of semantic retrieval. The choice of embedding model, caching strategy and re-indexing management directly impact quality and costs. text-embedding-ada-002 is not always the right choice. Smaller, specialised models can give better retrieval at lower costs for specific domains. But this requires an evaluation strategy, not a benchmark from a blog post.
Programma
- Model comparisonAda, text-embedding-3-small, text-embedding-3-large, local open source models: when to use what
- Embedding cachingHow to reduce API calls with distributed cache without impacting data freshness
- Incremental re-indexingHow to handle frequent document updates without re-indexing everything
- Batch processingHow to optimise indexing calls to reduce latency and costs
- Local models with OllamaWhen it makes sense to generate embeddings locally and how to integrate it into Semantic Kernel
- Budget and monitoringHow to configure alerts on embedding costs and correctly size the Azure plan
Risultato
You have a documented embedding strategy with estimated costs, configured caching and active monitoring. Your RAG system costs what it should, not whatever happens.
PHASE 3: Agents, Governance and Production (Weeks 8-10)
- 7
AI agents in .NET: architecture, orchestration and control
AI agents are autonomous systems that use LLMs to make decisions and call tools. In production they require a deliberate architecture: controllable reasoning loops, tools with clear contracts and explicit interruption mechanisms. An agent without architecture is a system that does things you cannot predict. In production this is not acceptable. Control over agent behaviour is designed in, not added later.
Programma
- Agent architectureReAct, Plan-and-Execute, tool use: patterns compared with architectural pros and cons
- Tools as domain servicesHow to expose business logic to agents with clear contracts and input validation
- Interruption and human supervisionHow to insert human-in-the-loop into critical flows without blocking automation
- Multi-agent orchestrationHow to coordinate specialised agents with Semantic Kernel Agent Framework
- Agent testingHow to write deterministic tests for non-deterministic behaviour
- Audit trailHow to log every agent decision with context, tools used and output for audit and debugging
Risultato
Your agents have documented, testable and monitorable behaviour. You know what they do, why they do it and how to stop them when necessary.
- 8
Observability and monitoring for enterprise AI systems
An AI system without observability is a blind system. Model latency, retrieval quality, cost distribution by feature and fallback rate are metrics that must be visible in production. It is not enough to know the system works. You need to know how much it costs, how slow it is, where it fails and why. AI observability is different from traditional observability because it includes semantic metrics, not just technical ones.
Programma
- OpenTelemetry with Semantic KernelConfiguring distributed traces for every AI call across layers
- Token tracking by featureHow to measure token consumption by functionality, user and tenant
- Latency and timeoutsHow to configure latency dashboards and alerting for degraded SLAs
- RAG quality in productionHow to measure retrieval relevance on real samples with implicit feedback
- Fallback monitoringHow to track cases where the system returned to deterministic logic
- Cost dashboardHow to build an AI cost view per business unit with granular budget alerts
Risultato
You have an AI system that monitors itself. Every anomaly in cost, latency or quality generates an alert before it becomes a problem for users or budget.
- 9
Security, content filtering and responsible AI in enterprise contexts
AI in production requires security controls that go beyond standard authentication: prompt injection, data leakage, out-of-policy responses and misuse are real risks that require architectural solutions. Azure AI Content Safety and Semantic Kernel guardrail mechanisms are not added at the end of the project. They are designed at the beginning as part of the security architecture, not as optional filters.
Programma
- Prompt injectionWhat it is, how it works and how to mitigate it architecturally rather than just with filters
- Data leakage in RAGHow to prevent sensitive documents from ending up in prompts of unauthorised users
- Azure AI Content SafetyConfiguring category filters, blocklists and thresholds for enterprise contexts
- Output validationHow to validate model responses before showing them to users with schemas and domain rules
- PII detection and maskingHow to detect and anonymise personal data in prompts and responses
- Audit and complianceHow to build an immutable log of AI interactions for regulatory requirements
Risultato
Your AI system has a documented security posture. You know which risks have been mitigated, how, and you have an audit trail that meets compliance requirements.
- 10
Deployment, CI/CD and lifecycle management of AI systems
Deploying an AI system is different from deploying a standard application: behaviour depends on the model, prompt version, vector index and parameter configuration. Each of these components has its own lifecycle. Deploying an AI system without prompt version governance is like deploying code without git. It seems to work until you need to roll back or understand why the behaviour changed.
Programma
- Prompt versioningHow to manage prompts as first-class artefacts with versioning, review and rollback
- CI/CD for AI systemsHow to integrate automatic model evaluation into GitHub Actions or Azure DevOps pipelines
- Blue/green for modelsHow to do gradual rollouts of model or prompt updates without impacting all users
- Feature flags for AIHow to enable AI features for subsets of users and measure impact before full rollout
- Vector index managementHow to synchronise index updates with application deploys
- AI disaster recoveryHow to design fallback to alternative providers or deterministic logic in case of model outage
Risultato
Your AI system has a managed lifecycle like any other enterprise software component. You can deploy with confidence, roll back in seconds and measure the impact of every change.
The method: Architecture of Progressive Mastery applied to AI
This is not a course about AI. It is an architectural mentoring programme on integrating AI into enterprise .NET systems.
The difference is substantial: you do not learn what Semantic Kernel does, you learn how to insert it into a clean, testable and scalable architecture without destroying what you already have.
What makes this programme different
Most AI courses show you how to call an API. This programme shows you how to design the system around that call:
- How to isolate the AI layer from the business domain
- How to make AI pipelines testable without calling real models
- How to manage costs at the architectural level, not just operationally
- How to build fallbacks and degraded mode for critical systems
- How to observe and trace AI behaviour in production
The 5 pillars of the method
Architecture before implementation: First design the structure, then write the code. Each module starts with architectural decisions, not tutorials.
Work on real code: We bring sessions to YOUR system: your stack, your constraints, your business objectives. Zero decontextualised demos.
Integration with the existing ecosystem: Nothing is thrown away. We learn to integrate AI into DDD, CQRS and microservice architectures that already exist.
AI observability and governance: Every AI system must be monitored. The programme includes Application Insights, token tracking, audit trail and content filtering.
Documented evolution plan: At the end you have a written architectural plan: what has been implemented, why, and how to evolve it over the next 12 months.
Choosing between Semantic Kernel, LangChain.NET and custom approaches
| Criterion | Semantic Kernel | LangChain.NET | Custom approach |
|---|---|---|---|
| Native .NET integration | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Azure OpenAI support | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| Agents and orchestration | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ |
| Learning curve | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐⭐ |
| Architectural control | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐⭐ |
Who this programme is for
CTO or Tech Lead designing AI systems
You are responsible for bringing AI to production and do not want to repeat the mistakes you see every day in failed projects. You are looking for an architectural method, not a tutorial.
Software architect who wants to master the AI domain
You know enterprise architectures but AI is new territory. You want to understand how the principles you already apply translate into reliable and scalable LLM, RAG and agent systems.
Team lead with a team experimenting with AI
Your team is writing AI code in an unstructured way. You want to introduce a shared architecture, project standards and testing practices before technical debt becomes unsustainable.
Who is Matteo Migliore
What professionals who have followed the programme say
Investment and programme
Programmes are tailored to system complexity, number of participants and architectural objectives.
Fill in the form to receive the complete programme and a preliminary analysis of your AI architecture with the priorities to work on immediately.
Individual Path
1 participant, personalised mentoring
- 10 complete modules (4 months)
- Bi-weekly live sessions 1-to-1
- Architecture review of your AI system
- Continuous support via chat
Team Path
2-4 participants, team transformation
- 10 complete modules (4 months)
- Work on your corporate AI codebase
- Initial architectural workshop
- Platform access for 12 months for all
AI Architecture Intensive Workshop
2 days of design on a real system
- Architectural assessment of the existing system
- Design session with documented output
- Up to 5 participants
- Follow-up session at 30 days
Ready to become an AI software architect?
Fill in the form and receive the complete programme with a preliminary analysis of your AI architecture. We will assess together the priorities to work on right away.
Free analysis We assess your current AI architecture and main critical areas
Talk to an architect Not a salesperson, but someone who has built real AI systems
Practical guidance You receive useful guidance even if you decide not to proceed