The thread behind the career
I studied computer science, then spent years inside mobile money, payments, customer success, sales, presales, and operations. In parallel, I designed and rebuilt Zendesk environments for companies with very different operating models.
What looked like separate disciplines kept revealing the same problem: businesses accumulate tools faster than they define ownership, context, decisions, and recovery. That is the system I now focus on.
What I notice first
I tend to detect mismatches before I can fully explain them: a field that cannot support the report, a handoff that loses authority, an automation with no owner, a proposal priced around labor instead of uncertainty, or an AI agent with tools but no operating mandate.
The work is to turn that mismatch into a model other people can see, test, implement, and govern.
Why CRM Scene exists
CRM Scene began from deep Zendesk delivery and is evolving into an AI-native business systems company. It is the vehicle for architecture reviews, operating transformation, middleware, automation, knowledge systems, AI agents, and complex implementation.
The customer service and sales track record remains important because it is where the discipline was proven under real volume, real handoffs, real customers, and real operational consequences.
The internal lab
I use CRM Scene as a live environment for the future I advise on.
The internal system includes a personal AI agent that can work with permissioned operational context from connected tools. This is where I test memory, tool use, authority, approvals, escalation, auditability, and the practical limits of delegation.
The simplest description
I turn operational complexity into systems that can be understood and acted on.
Sometimes that means Zendesk. Sometimes middleware. Sometimes an AI agent. The right answer depends on the operating model underneath.