AI Systems Architecture
Build AI systems like an architect, not a tutorial-follower
AINexArch is a working engineer’s notebook on AI systems architecture — RAG, agents, and LLM apps in production — with real implementations in .NET and Python. No hype, no filler. Just designs that ship.
Written from real projects, not press releases
Every article comes from a system I actually designed, built, broke, and fixed — with the code to prove it.Architecture First
How to design RAG pipelines, agent systems, evaluation loops, and vector search — the trade-offs, the costs, and the decisions. Frameworks change every quarter; good architecture doesn’t.Two Stacks, Compared Honestly
Most AI content assumes Python. Most enterprises run .NET. I build the same systems in both — Semantic Kernel next to LangChain, FastAPI next to ASP.NET Core — and show where each one actually wins.Built in Public
I’m building SiteAuditPro, a real SaaS, and documenting every architectural decision: crawlers, queues, SSRF defenses, scoring engines, Azure deployment. Working code, real mistakes, real numbers.
Start Here
Three ways in, depending on what you’re working on right now.Designing AI systems?
Start with the architecture deep-dives: RAG reference designs, agent patterns, structured outputs, and LLM security. Browse architecture deep-dives →.NET developer adding AI or Python?
Read the dual-stack series: the same systems built in C# and Python, side by side, with a C# developer’s honest notes on learning Python. Read the dual-stack series →Preparing for senior interviews?
AI system design walkthroughs and senior .NET/Azure architect questions — with model answers, not just question lists. Open interview guides →Follow a Real SaaS Being Built
SiteAuditPro is an SEO and technical audit platform built with .NET 10, Angular, PostgreSQL, and Azure. Every meaningful decision becomes an article.
The Crawler
A resumable web crawler with EF Core and background workers — and why Playwright everywhere would have cost 5x more.
Read the crawler series →The Security Layer
Designing an SSRF-safe URL validator in C#: blocking private ranges, metadata endpoints, and DNS rebinding — with tests.
Read the security posts →The Scoring Engine
Turning 60 audit rules into one transparent score users can trust — no arbitrary AI numbers, every point accounted for.
Read the scoring post →One System, Two Stacks
The same AI systems, built in C# and Python
Semantic Kernel vs LangChain. ASP.NET Core vs FastAPI. Agents in .NET vs LangGraph. Built twice, compared honestly — so you can pick the right stack for your team instead of the loudest one.
Latest from the Build Log
Architecture decisions, dual-stack comparisons, and lessons from building real systems.
The Stack Behind the Articles
Everything on this site is built and tested with these tools — the same ones covered in the build log..NET 10 & Semantic Kernel
The backbone of SiteAuditPro and every C# implementation in the dual-stack series.Python, FastAPI & LangChain
The other half of every comparison — learned in public, from a C# developer’s perspective.Azure OpenAI & App Service
Where the systems actually run: model deployments, background workers, and queues.Claude & Claude Code
AI-assisted development done seriously — how I use it, where it fails, what it costs.PostgreSQL & pgvector
Relational data and vector search in one place, before reaching for a dedicated vector DB.Playwright
Rendering JavaScript-heavy pages in the crawler — used sparingly, for cost reasons covered in the build log.
Why AINexArch
Almost all AI engineering content assumes you start from Python and a blank slate. Most of us start from an enterprise stack and a production system that can’t go down.First-Hand, Not Aggregated
Every article comes from something I built. You get the design, the code, the mistakes, and the bill — not a rewrite of someone else’s documentation.Senior-Level by Default
No “what is AI” explainers. The starting assumption is that you can code, and what you need is architecture judgment: trade-offs, costs, and failure modes.Enterprise Reality Included
Security reviews, observability, deploys that can’t drop jobs, and stacks your company already runs. AI engineering as it happens inside real organizations.
About AINexArch
I’m a senior .NET and Azure engineer learning in public: building AI systems, adding Python to a C# toolkit, and writing down what actually works.
AINexArch exists because the gap between AI tutorials and AI systems that survive production is enormous — and almost nobody writes about crossing it from the .NET side.
If you’re a developer or architect making that crossing too, you’re in the right place.
More about AINexArch