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About Arbiter

A lean agentic AI installation and managed-services shop for teams that need practical automation inside real workflows.

// Principal
Daniel Evans portrait

Daniel Evans

Founder & Principal

Arbiter is led by Daniel Evans, a Detroit-based analyst and builder with a background in equity research, financial modeling, risk assessment, data analytics, and practical AI-agent operations.

The work starts with a simple idea: AI systems only matter when they survive real workflows. Arbiter exists to install useful agent stacks, connect them to the work, and keep them stable after launch.

Daniel Evans outdoors

Built Around Installation, Not Slideware

Arbiter exists for teams that do not need a giant consulting apparatus. They need someone who can scope the workflow, install the agent stack, harden the operating rules, and stay around when something breaks.

Wide Range, One Operating Standard

The work spans AI agents, reporting, research, dashboards, lead operations, and web workflows because useful automation rarely stays in one lane. The standard is not novelty. The standard is whether the system reduces wasted motion and gives operators leverage.

Analytical Background, Practical Bias

Daniel's background includes equity research, modeling uncertainty, pricing risk, and building source-faithful operating reports. That carries into AI work: separate signal from story, define the human review point, and make the workflow explicit.

Best Fit

Arbiter works best with founder-led teams, operators, clinics, agencies, boutique firms, and smaller enterprises that want agents doing real internal work without naming private customers publicly. If you need ten committees and a 90-slide deck before anyone moves, this is probably the wrong shop.

"The point is not to sound smart. The point is to make the next move clearer."

Scope an Installation

If you have a workflow that should be supported by agents, send the use case, constraints, and what success would look like. No customer names required.