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 design orchestration systems, retrieval layers, and knowledge structures that connect LLMs to real support and fulfillment systems, so teams can investigate issues with full context.
Recent build: AI Support Orchestration System combining webhook-driven enrichment, retrieval across internal and public knowledge bases, and bounded AI actions across ticketing, order, inventory, and tracking workflows, 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
My strongest results have come from applying AI to operational bottlenecks: assembling missing context before a human has to investigate, using historical ticket data to redesign retrieval and knowledge structures, and turning repeated support pain into more autonomous systems.
70-80%
Faster investigations
through AI context assembly
through AI context assembly
900+
Historical issues analyzed
to improve retrieval design
to improve retrieval design
10+
Commerce and carrier flows
wired into AI-assisted workflows
wired into AI-assisted workflows
7+
Years of domain knowledge
translated into AI systems
translated into AI systems
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
RAG
Claude Code
Cursor
Codex
Docker
Cloudflare Workers
Postman
AWS
REST/GraphQL