AI Engineering FoundryLoading…
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Build. Engineer. Ship AI.
Practical AI engineering for developers building real-world systems with .NET, RAG, AI agents, LLMs, Vector Databases, Python, FastAPI, SQL Server, and production infrastructure — documented version by version, as it's actually built, not rewritten after the fact.
Every article ties back to a working implementation — not an explainer rewritten from someone else's docs.
ASP.NET Core, C#, LLM APIs, Ollama, Semantic Kernel, streaming responses via SignalR.
Explore →§02Embeddings, chunking, Qdrant, hybrid search, reranking, and production retrieval architecture.
Explore →§03Tool calling, memory, planning, multi-agent orchestration, security and evaluation.
Explore →§04Model selection, prompting, context windows, structured output, cost and latency tradeoffs.
Explore →§05Text-to-SQL, schema discovery, query validation, and giving an LLM safe database access.
Explore →§06Security, observability, evaluation, cost control, and deployment onto real infrastructure.
Explore →Ask a SQL Server database a question in plain English and get back a validated, schema-aware SQL answer — built in five measured versions, from a naïve Text-to-SQL prototype to a secured, knowledge-augmented, tool-calling agent.
Every version ships as a real article: architecture, implementation, what broke, and how it was fixed. No step is skipped for the sake of a clean narrative.
Build roadmap
One layer, one job. Nothing is added before the phase that needs it actually ships.
| Layer | Choice | Why |
|---|---|---|
| Public site | Next.js + TypeScript + Tailwind + MDX | Static-first, SEO-friendly, zero database for content. |
| Flagship backend | ASP.NET Core (C#) | Hosts the AI Database Agent — Text-to-SQL, RAG, agents. |
| Python AI services | Python + FastAPI | Async, streaming AI APIs — backs the AI Knowledge Base Chat RAG app. |
| Accounts & billing | ASP.NET Core + SQL Server | One relational store for identity, tokens, sessions, subscriptions. |
| Vector search | Qdrant | RAG for articles and the database agent. |
| LLMs | Ollama (local) + provider-agnostic API | Keeps the brand independent of any single model vendor. |
| Reverse proxy / TLS | Nginx + Let's Encrypt | Standard, transparent VPS pattern — no managed host required. |
| CI/CD | GitHub Actions → SSH deploy | Push to main, service restarts — no platform lock-in. |
SQL Server and Qdrant never face the internet — only the API is allowed to speak to them.
The connection profile currently wired to this build — Windows-integrated, encrypted, developer machine only. Production swaps this for a scoped service account behind the internal Docker network.
Browser-based utilities for everyday AI and backend work: format and convert JSON and SQL, generate C# models, turn PDFs into Markdown for RAG ingestion, and decode JWTs. Nothing is uploaded.
Format, validate, and minify JSON in your browser.
Open tool →C# / .NETPaste JSON and get C# model classes or records, ready for System.Text.Json or Newtonsoft.Json.
Open tool →SQLFormat and beautify SQL queries for SQL Server (T-SQL), PostgreSQL, MySQL, Oracle, and more.
Open tool →SQLTurn a JSON array into a CREATE TABLE with inferred column types and batched INSERT statements for PostgreSQL, MySQL, SQL Server, or SQLite.
Open tool →ConvertConvert a PDF to Markdown: font size is used to guess headings, and bullet or numbered lines are kept as list items.
Open tool →RegexTest JavaScript regular expressions against your text with live match highlighting, capture groups, named groups, and find-and-replace.
Open tool →AuthDecode a JSON Web Token to read its header and claims, see when it expires, and verify its signature with a secret, a public key, or a JWKS — all without the token leaving your browser.
Open tool →EncodingEncode text or any file to Base64, or decode Base64 back to text.
Open tool →Build an MCP server in C#: read-only SQL Server tools, a T-SQL parser gate, cost and row limits, and setup for Claude Desktop, VS Code and Claude Code.
Read article →Build a RAG API in C#: chunk by page and heading, embed with Ollama, search Qdrant with access filters, stream cited answers, and measure the results.
Read article →Run a local LLM with Ollama and call it from C#: HttpClient, OllamaSharp and IChatClient, streaming, JSON output, embeddings, plus troubleshooting.
Read article →