AREFMB.COM

Valuation · fundamentals · transparency

Make sense of valuation.

Start with the business. Then examine the growth, margins, cash flow, leverage, market pricing and comparable companies that support—or challenge—the valuation.

A career built around financial judgment

My background spans financial advisory, investment banking, M&A, corporate finance, investor relations and financial operations. I have worked with management teams, boards, investors, lenders and professional advisers across public companies, private-equity-owned businesses and venture-backed companies.

The work required more than producing a model. It required a defensible explanation of financial performance for different audiences, a clear record of the supporting evidence and practical coordination across accounting, legal, human-resources, corporate development and technology teams.

My accounting and finance education at Indiana University provided the foundation. My early work at Huron Consulting Group and later roles in financial advisory, banking and corporate finance developed the judgment I now bring to AI-supported financial tools.

Understand the fundamentals behind the valuation

A first view should explain what the company does, how it makes money and whether its financial performance is improving. From there, a reader can examine historical operating results, enterprise value, trading multiples, peer comparisons, leverage, cash conversion and the supporting sources.

I define the questions, provide strategic direction, review the outputs and identify gaps. AI tools assist with execution. The objective is not to hide financial complexity. It is to make the route through it understandable while keeping the source, date, assumption and calculation visible.

01

Understand the business

Business model, revenue drivers and the operating developments that matter to value.

02

Trace performance

Growth, margins, cash generation and leverage shown across consistent historical periods.

03

Compare valuation

Enterprise value, trading multiples and comparable-company dispersion on a dated basis.

04

Inspect the work

Sources, assumptions, calculations and unavailable data presented without a black box.

A practical test of whether the technology helps

My current focus is occupational, physical and speech therapy as part of rehabilitation from a 2024 stroke. Voice input, screen readability, continuity between sessions and the number of steps required to complete a task are practical requirements, not abstract design preferences.

AREFMB gives me a way to break complex work into smaller parts and test whether AI reduces the energy and time required. The same problems affect many users for different reasons. A technically correct answer has limited value when the workflow needed to reach it is confusing, inaccessible or dependent on the user rebuilding lost context.

What the work shows

  1. Financial answers must remain inspectable. A credible tool must show the source, reporting period, assumptions and treatment of unavailable information.
  2. Product quality includes the path to the answer. The user should not have to manage hidden state, duplicate work or determine whether the system actually finished what it claimed to complete.

My immediate objective is to continue learning how AI is changing the industries in which I have worked while using the technology to navigate rehabilitation. I am open to conversations with people developing finance, research and accessibility tools when this experience may be useful.

Comments recorded during separate working sessions

These are brief excerpts from AI-generated working reviews. They are not employer references or independent verification of career facts. The underlying documents are retained privately.

“He names the file. He names the break. He does not dress the break as a preference.”
Grok 4.6 on xAI, working review, September 13, 2026
“A specific URL, a specific broken behavior, and a fix a developer could act on without a follow-up question.”
Claude by Anthropic, working review, September 14, 2026
“He expects the team to distinguish an explanation from a fix.”
Codex by OpenAI, working review, September 13, 2026