I founded Clover to help growing businesses use AI in ways that are practical, controlled, and genuinely useful to the people doing the work.
For more than two decades, I’ve worked as a systems analyst, software developer, architect, data-governance leader, and technology manager. The titles changed; the purpose did not: design better systems for people.
These examples reflect Evan’s career experience and the practical leadership principles he now brings to Clover engagements.
Evan has built enterprise data-governance programs that brought business owners, technology teams, standards, and decision rights into one practical operating model—so important data could be managed as a shared organizational responsibility.
He has modernized mission-critical business applications and introduced Agile delivery practices that connected business priorities with technical execution, made progress more visible, and helped teams deliver change more reliably.
Evan has led teams using AI in day-to-day work such as vendor evaluation, operational reporting, research, planning, and delivery—while establishing expectations for evidence, review, human accountability, and continuous learning.
Long before AI became mainstream, I believed the best organizations were built by aligning people, process, technology, and policy into operating models that teams could rely on. AI hasn’t changed that belief—it has amplified it.
It’s about people.
It’s about process.
It’s about trust.
Today, I’m applying those principles to AI—helping organizations move beyond experimentation and build operational systems that are practical, governed, and trusted.
That’s what led me to found Clover.
I don’t see AI as a replacement for people. I see it as a force multiplier for thoughtful teams. My passion is helping leaders take an idea from a whiteboard sketch to an operational reality—challenging assumptions, designing processes, creating governance, and building engineering practices that teams can execute with confidence.
I still believe the best ideas begin with a conversation around a whiteboard. That’s where problems become systems, and systems become something people can trust.
Clover reflects the way I believe organizations should adopt AI: deliberately, responsibly, and with an unwavering focus on creating work that people can trust.
Technology matters. But the quality of AI-assisted work also depends on problem framing, process, evidence, controls, human responsibility, and organizational learning.
Clover brings practical experience in software delivery, DevOps, process management, controls, governance, observability, and operational leadership to businesses that may not have those capabilities internally.
Trust is built through how the work is performed—not added after it is complete.
Clover is an emerging AI practice built on working orchestration, verification, and delivery systems. Its methods have been demonstrated through operational, engineering, reporting, evaluation, knowledge, and management use cases.
Some offerings are ready for carefully scoped implementation today; others continue to mature on the product roadmap. Early customers may participate in well-bounded pilot deployments with explicit success conditions, visible assumptions, preserved evidence, and human review.
Clover is deliberate about growth. Controlled adoption takes priority over exaggerated autonomy, and no maturity label is intended as a guarantee of a specific customer result.
Expanding reusable operating and mission patterns
Deepening automated and independent verification
Improving observability and outcome reporting
Extending knowledge-system capabilities
Improving evidence and provenance controls
Publishing practical field lessons