Hi, I'm S Ravi Kumar

Solutions Architect & Product Owner

I help enterprise .NET teams adopt agentic AI.

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22+

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Technical articles

About

A short introduction — the full story lives on the resume page.

I help enterprise .NET teams adopt agentic AI — the messy, real version: guardrails, verification gates, RAG over your own data, and the process changes that decide whether the tooling actually gets used. 22+ years as an architect across managed cloud services, healthcare, insurance, travel, and HR. Today I own architecture and AI-adoption strategy for a multi-cloud management platform — driving discovery, reference architectures, and executive solution reviews, and running LLM workloads on Azure AI Foundry and AWS Bedrock inside enterprise security constraints. What I've learned the hard way: AI adoption fails on enablement, not capability. Rolling out an AI-first development process taught me that the framework is the easy half — onboarding, champions, and verification discipline are the half that decides the outcome. That lesson now shapes everything I build. In public: ► AI-First Development Playbook — a seven-phase agentic SDLC for teams, with verification gates and a command library ► TechieRag — a .NET RAG library (NuGet) ► TechieFlow — the solo-architect edition of the same philosophy Founder, NCR Cloud & Core Techies (500+ member developer community). C# Corner MVP. Conference speaker on .NET, cloud architecture, and AI application development. If you're a CTO, engineering leader, or architect trying to make agentic AI real inside a .NET estate — that's the conversation I want to have.

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Latest from the blog

TfLens: My AI Observability Dashboard

I build software with AI coding agents, and every run costs tokens or dollars. TfLens is the dashboard I built to see where that money goes: per project, per phase, per model, per tool. It reads the five metrics files TechieFlow writes into every repository and turns them into nine pages: coverage, gate outcomes, harness comparison, routing and economics, misses and rework, phase effort, price providers, snapshot export. Free, live at tflens.techierathore.com, with every number next to the file it came from.

By S Ravi Kumar Oct 29, 2026 8 min read

Latest Articles

Fresh from the editor — browse all articles. Earlier writing lives in the Blog-Archive.

TfLens: My AI Observability Dashboard

S Ravi Kumar Oct 29, 20268 min read

General

The Simulator Said 301. The Phone Said 155

Last month I counted 119 mistakes that got through because somebody checked the wrong thing. This is one more, caught in time. AI agents built the part of my library that runs a language model on the device itself, and they tested it on an iPhone simulator: 301 tokens a second. The real phone gave 155. The pretend Android phone was wrong in the other direction. Four real devices, one table of measured numbers, and what it taught me about the word "tested".

S Ravi Kumar Oct 06, 20265 min read

General

AI Put Me on a Pedestal. Then I Looked Down

I spent a year building software with AI, then built a tool to measure whether it was working. The first number it gave me was humbling. In eleven days it logged 268 misses, meaning things the AI got wrong or left out. My automated checks caught 46 of them. Agent reviews caught 82. I caught 122. By opening the app and clicking around, like it was 2010. The most common reason something slipped through, 119 times, was not that nobody checked. It was that somebody checked the wrong thing. And the worst number is the one I do not have. I only

S Ravi Kumar Sep 07, 202614 min read

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Greater Noida, India

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