Investigating what actually works in production AI systems.
Not what papers claim. Not what companies market.
What actually ships and stays reliable.
I'm Sai Harsha Kondaveeti — independent analyst and builder.
I study what actually happens when AI systems hit production constraints: where they break, why, and what fixes them. Garvaman AI is where that analysis lives.
The SAAL framework is how I think about AI systems as composable infrastructure. RAG Axis and Cespace are what I build from it.
Shipping systems at scale. You understand production problems. You want to learn from others doing the same.
You want to understand systems thinking. Here's how real production systems are built, where they break, and why.
You're looking for builders who understand systems, ship products, and think beyond the demo.
You're looking for technical founders who don't need managing — someone with a systems perspective and a track record.
Think of AI systems like infrastructure, not products. A framework for decomposing production AI into modular, independently deployable layers.
How I deployed Strapi on a Hetzner dedicated server, connected it to a host PostgreSQL 17 instance, fixed three Dockerfile failures, and got a production CMS running for multiple platforms at near-zero marginal cost.
The way we're building AI today is going to break at scale. Not because the models are wrong but because the architecture around them is. Introducing SAAL: AI Systems As A Layer.
Multi-agent systems look elegant on paper. In production they fail at handoffs. Here's the pattern and the fix.
AI systems are marketed as intelligent.
In reality, they fail because reliability is treated as an afterthought.
I've seen this pattern repeat:
This is the Production AI Reliability Paradox.
And it's solvable.
How I deployed Strapi on a Hetzner dedicated server, connected it to a host PostgreSQL 17 instance, fixed three Dockerfile failures, and got a production CMS running for multiple platforms at near-zero marginal cost.
The way we're building AI today is going to break at scale. Not because the models are wrong but because the architecture around them is. Introducing SAAL: AI Systems As A Layer.
Multi-agent systems look elegant on paper. In production they fail at handoffs. Here's the pattern and the fix.
Research explains mechanisms. Production teaches consequences. The lessons that only come from actually shipping.
Over 80% of AI projects never reach meaningful production. Here's the five-layer architecture model that explains why — and what to do about it.
What nobody tells you about the platform tax — latency, cost, and consistency — once you leave the demo environment.
Treating your RAG layer as a UI decision, not a backend tuning problem.
Think of AI systems like infrastructure, not products.
Just as SaaS and PaaS decomposed software into modular services, AI systems can decompose into modular layers — each independently buildable, deployable, and scalable.
Retrieval Layer as a Service
Production-ready retrieval system. Not a library. Not a research project. A deployable service.
Single-click deployment · handles real production constraints
Community Hosting Platform
Social layer for AI products. Built-in community, not bolted-on. Designed for AI-first products.
Creators · collaboration · knowledge