Built by someone who felt the problem
At many companies, work increasingly means directing AI. Most of them have no process for knowing where that's paying off and where it isn't. Roiva is built to be that process.
At Ethos, the data platform I founded and later sold to ACA Group, we rolled out a lot of AI. New tools, new workflows, moving fast. Then came the question everyone eventually asks: "Is this actually paying off?" I didn't have a good answer. Which of it was working? Which wasn't? Why?
We had spreadsheets, gut feel, and anecdotes. What we didn't have was a process: a baseline for how the work was done before, a measure of what it cost and returned after, and a record of what we decided and the figures we decided on.
I started talking to other operators: heads of AI and technology, finance leaders, transformation teams. The story was the same everywhere. At many of their companies, work increasingly meant directing AI (writing the prompt, reviewing the output, deciding what ships), and almost nobody had a process for knowing where it was paying off. Budgets got set, tools got renewed, and nobody could say what came back.
The tools to use AI are everywhere. What was missing was the process around them: one place that measures what each AI initiative costs and returns, keeps the call to scale, fix, or cut it with the figures it was made against, and traces every number to a formula, a data source, and an approval.
That's why I built Roiva.
Luke Wilcox
Founder, Roiva · Ex-McKinsey
A former McKinsey consultant, Luke founded Ethos in 2019 — a data, analytics, and reporting platform serving financial advisors and institutions. Ethos was acquired by ACA Group in 2022, where Luke led the Ethos platform as a Partner.
What we believe
AI needs a number you can defend
Visibility beats instinct
Good decisions about AI require real data. Gut feel and anecdote aren't sufficient when CFOs start asking questions.
ROI is not a vanity metric
We care about realized value — actual dollars saved or earned — not usage stats, adoption rates, or AI maturity scores.
A process, not a one-off
AI isn't measured once at rollout. Set the baseline, measure what it returns, make the call, and come back to it on a schedule. That's how AI experiments turn into compounding advantage.
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