Srinivas Thangalapally // AI Systems Consultant
I build AI workflows and systems that turn ideas into finished work — LLM-powered tools, content pipelines, and everything in between. Not prompting — engineering: prompt stacks, multi-agent workflows, visual pipelines, format libraries. The AI does the heavy lifting. I bring the taste.
How I Work
AI as Thinking Multiplier
I chain LLMs to explore angles, counter-angles, and edge cases — Claude for reasoning, multi-agent setups for the hard problems. The AI accelerates the work; I override with taste and judgment.
Vibe-Coded Systems
I build reusable systems, not one-off outputs — LLM workflows, prompt stacks, agent pipelines, format libraries. Each build compounds into tooling I reuse.
Platform-Native Thinking
I build to the platform, not around it. Each system is shaped by where it runs — its APIs, constraints, data shapes, and delivery targets. The environment drives the architecture, so the output is native to where it lives instead of retrofitted.
Same System, Any Domain
The AI-native workflow is domain-agnostic. The system — prompt stacks, agent pipelines, visual pipelines, format libraries — transfers to any vertical. Here's how it maps to business content:
| What I've Built | Business Content Application |
|---|---|
| Multi-round prompt engineering | Data visualization hooks, before/after business transformation visuals |
| Poetry translation + adaptation | Dense technical concepts → low-entropy, high-signal docs, specs, and prompt instructions |
| Format-native design | Platform-native outputs — web UIs, API responses, CLI tooling, structured JSON |
| AI-to-video pipeline | Business data → animated infographics, deal breakdowns |
| LLM workflow building | Script generators, hook libraries, A/B test variants, retention-optimized rewrites |
| Audience-first content | Entrepreneurship content for business buyers/operators ($2M-$50M) |