I spent 5+ years inside a multi-channel fulfillment startup - onboarding merchants, debugging carrier integrations, leading support operations, shaping product from the operations side. Then I used that knowledge to design the AI systems that now power those same workflows.
Rob Klouda
Business operator who designs AI systems.
Not the other way around.
About
Focus
AI-ready operational context. I build MCP servers, retrieval layers, and knowledge structures that connect LLMs to real support and fulfillment systems, so teams can investigate issues with full context.
Recent build: MCP support toolkit connecting LLM workflows to ticketing, order, inventory, and tracking systems, reducing investigation time by ~70-80%.
Integration reliability across sales channels. I have diagnosed recurring failures in order sync, fulfillment updates, and tracking push across Shopify, Amazon, Walmart, eBay, and related channel integrations.
Systems view: most failures happen at integration boundaries between channel APIs, fulfillment logic, and carrier events, not in one isolated screen.
Startup execution with multi-role ownership. I regularly bridge support, operations, product, and engineering to convert repeated incidents into architecture and workflow improvements.
Impact
70-80%
Reduction in
investigation time
investigation time
10+
Channel integrations
diagnosed
diagnosed
7+
Years in startup
operations systems
operations systems
900+
Jira issues analyzed
for root cause
for root cause
Tools
I use AI coding and data tooling to move quickly from concept to reliable prototypes, with strong focus on retrieval quality and operational correctness.
Python
PostgreSQL
pgvector
MCP
Claude Code
Cursor
Codex
Docker
Cloudflare Workers