contact@catheros.ai

David Godin · Catheros

Show me what’s slowing your business down.

I learn how your business works, find a practical improvement, and help put it to work, using AI and automation where they help, and a check that the result is better.

Advisory · Workflow design Prototyping · Evaluation · Adoption Priced on outcomes

Where Catheros fits

Close to small teams. Credible in the boardroom.

The Catheros founder leaning on a brick wall, arms crossed
Owner-operated companies Mid-market teams Enterprise divisions

The sweet spot is the organization that knows AI should be changing something by now, but cannot yet say exactly what. Often that is a ten-person company with no in-house AI expertise and no appetite for a failed experiment. Sometimes it is a division of ten thousand with too many options and too few grounded recommendations.

The method does not change with size: narrow the question, design the workflow around it, test cheaply, and let evidence decide what earns further investment. Smaller organizations get answers they can act on without building a research function. Larger ones get recommendations grounded in their own operations rather than a vendor's slide deck.

Catheros is based in metro Detroit, on-site across Southeast Michigan and remote everywhere else. The practice draws on twenty-five years in IT: solving problems for customers, accelerating outcomes, and telling organizations when the honest answer is “not yet.”

What we do

From first question to useful change.

A single engagement may draw on several of these disciplines; most begin in one place.

01

AI Strategy & Opportunity Design

Identify where AI can meaningfully improve a workflow, decision, customer experience, or operating process.

Opportunity map
02

Workflow & System Design

Translate business goals into clear requirements, process flows, decision points, controls, and measurable outcomes.

Requirements & controls
03

Applied AI Prototyping

Explore and test promising solutions with contemporary tools and AI-assisted development methods, before committing to large-scale implementation.

Working prototype
04

AI Evaluation & Decision Support

Compare approaches, surface failure modes, weigh tradeoffs, and decide what is worth pursuing and what is not.

Decision memo
05

Adoption & Commercialization

Connect technical capability to the people, processes, operating models, and outcomes required for real-world use.

Adoption path

How we think

How we think

AI is most valuable when it improves the system around the work, not simply when it produces another answer.

A model choice rarely decides anything on its own. Outcomes come from how intelligence, workflow, human judgment, information, controls, and measurable outcomes fit together; move one and the others move with it.

So engagements start with the system, not the software: understand the workflow before proposing technology, define where human judgment stays in charge, settle the controls before the demonstration, and measure whether the change actually happened.

The goal is not AI for its own sake. The goal is useful change.

THE WORK INTELLIGENCE WORKFLOW HUMAN JUDGMENT INFORMATION CONTROLS MEASURABLE OUTCOMES

Fig. 1. The system around the work.

Intelligence Workflow Human judgment Information Controls Measurable outcomes

Engagements

Selective, scoped, time-boxed.

Catheros takes on advisory, research, workflow-design, and applied-AI exploration engagements, selectively and always with a defined end.

Pricing

We only charge for the outcome.

Fees attach to the change: the workflow that runs, the decision made with evidence, the result you can measure. Not hours, not activity, not tooling.

Advisory

Ongoing counsel for leaders making AI decisions: where to act, what to defer, and what to stop.

Research

Structured exploration of a question worth answering before the organization commits budget, attention, or reputation.

Workflow design

Translation from business goal to working process: requirements, decision points, controls, and the outcome measures attached to each.

Applied exploration

Time-boxed prototypes that put a promising approach in front of real conditions, ending in evidence rather than opinion.

Tool-agnostic by design. Engagements draw on commercially available models, software, and AI-assisted development tools as the problem requires: the choice follows the work, never the reverse.

Narrow beginnings, clear endings. An engagement starts with one workflow or one question and closes with a grounded recommendation: adopt, adapt, or abstain.

Start with a conversation.

Name the workflow that should be working better. That is enough to begin.

Email contact@catheros.ai