I run a credit startup and an automation engineering practice — simultaneously, with a small team. This is the OpenClaw setup that makes it possible: 8 workflows, a hardened security architecture, and one Windows machine doing the work of three people. Full case study now live.
Rimba is an AI automation platform for Operations, Supply Chain, and Compliance teams in energy and industrials. Built for Oil & Gas, Power & Utilities, Mining & Metal, Supply Chain and Logistics, Chemicals, and Manufacturing.
For this project, I designed the brand and product interface to turn complex, high stakes workflows into something clear and easy to trust. The result is a system that feels as solid and dependable as the industries it serves.
If you are looking for a designer who can bring clarity to complex products, I would love to work together.
24-Hour AI Workflow Blueprint — From Business Problem to Practical AI Plan
I developed a practical workflow-review system designed to help small businesses identify where AI can genuinely save time — and, just as importantly, where human judgement should remain in control.
The process starts with a structured business questionnaire covering repetitive tasks, existing software, time-consuming processes, desired improvements and areas that should not be automated.
That information is then analysed to identify bottlenecks, quick wins and realistic opportunities for AI assistance. The resulting Blueprint provides prioritised recommendations, practical workflows, suggested tools and prompts, implementation steps, and clear human/AI boundaries.
I also built and tested the supporting intake and delivery workflow so the complete review can be produced within 24 hours of receiving the required information.
The objective isn't “AI everywhere”. It's less repetitive work, clearer processes and practical improvements that a business can actually use.
This workflow was developed and internally tested as part of a live 30-day commercial AI experiment. No invented client results or hypothetical savings are presented here.
AI Agent Orchestrator decisions determine whether a useful assistant stays useful after day one. For MeetClaw, I design which channels it inhabits, which model provider it uses, and where its state lives before the installation begins. I have organised 3 self-funded buildathons around OpenClaw, Claude Code and Hermes, so I have seen the failures that a topology has to prevent. I work with approvals, read-back and live integrations so the operator can inspect the system. Message me with the workflow that needs coordination.