Preston Monk

Founder, Pruvara · Ph.D., Economics

Hi, I'm Preston Monk.

I'm an economist and the founder of Pruvara. We build and run the online presence of growing businesses — the website, the search visibility, the measurement — and improve it from evidence, continuously. This page is who's behind that work.

Visit Pruvara ↗ Why I built it
Preston Monk
  • 01 Build
  • 02 Measure
  • 03 Improve
Pruvara

Why I built it.

Most growing businesses are flying blind online. They have a website; they don't know what it's doing — where visitors come from, what brought them in, which of them tapped to call. Guesswork fills the gap, and guesswork is expensive.

Pruvara is the alternative I wanted to exist: a first-class website built around one outcome — more customers — with measurement wired in from day one. Every visit, call tap, and form send is recorded as it happens by a first-party measurement system I built myself, and the work goes where the evidence points. It's the operating discipline of an enterprise intelligence team, run for businesses that wouldn't otherwise have one.

That alternative is running today: client work is live, the measurement runs continuously on the sites Pruvara operates, and what Pruvara does, what it costs, and the work itself are all public.

See the work at pruvara.com

Method

How I work.

Evidence before opinion

My training is in economics — a discipline about decisions under uncertainty: what causes what, what a measurement really captures, how much weight a result can bear. Economists are taught to treat a claim nobody can verify as no claim at all. I hold my own work to that standard.

Measurement first

I don't bolt analytics onto finished work — I build the instrument before the thing it measures. Pruvara runs on a first-party event pipeline I built for exactly this: every visit and action recorded as it happens, attributable to the source that produced it, with visitor identity protected by design.

Improve from what's true

A site is never finished; it's operated. Changes are chosen from what the evidence says is the biggest opportunity, shipped, and then measured like everything else. When the data says I was wrong, the data wins — that's the entire point of collecting it.

Before Pruvara

The enterprise version.

The discipline predates the company. Before Pruvara, I built product intelligence for a national distributor — the same principle at enterprise scale: answers that carry their evidence.

Enterprise product intelligence

Simplify

Context

A national imaging and printing distributor with thousands of SKUs and hundreds of thousands of transaction records. Sales representatives needed reliable answers to distinct classes of question — what's compatible with a given machine, what supersedes a discontinued item, what an account is due to reorder — and each class requires different evidence.

System

I built the platform on a knowledge graph of product relationships — compatibility, supersession, complementarity — constructed from structured transaction data and unstructured technical documentation. Every request is resolved to a specific intent before retrieval begins, the intent selects the retrieval path (graph traversal, SQL, or documents), and generation happens last, over evidence already retrieved and validated. Answers carry their sources.

Prediction

Retrieval answers the question in front of a representative; prediction decides which conversation is worth having. A recommendation engine built on five years of transaction history models purchasing behavior directly — which accounts are due, which are drifting toward attrition — and ranks what each account should be offered next. Each intent is evaluated against its own criteria, because an aggregate score would hide failure in any one of them.

Writing

Thinking out loud.

On building in a time when building is cheap — and what's still scarce when it is.

All writing

About

How I got here.

I completed my Ph.D. in Economics at Florida State in 2024, based at the DeVoe L. Moore Center. My dissertation linked millions of property transactions to demographic, economic, environmental, and public administrative records, building spatial models of how properties, neighborhoods, schools, crime, and infrastructure interact — and using difference-in-differences, matching, and spatial econometrics to identify how neighborhood shocks move property values and push households to relocate.

Both halves of that were demanding. Assembling records never designed to be joined took years, and every construction decision had to hold up under a committee. The modeling is what made it mean anything: an estimate is only as credible as the identification strategy behind it. The data and the model are not separable — a careless join produces a confident, wrong result.

That training is what I brought first into enterprise AI systems, and now into Pruvara. Whether the question is a doctoral committee's or a business owner's — is this working? — I treat it the same way: build the evidence first, then let it speak. It's why every claim Pruvara makes to a client traces back to something that was actually measured.

I live in Miami, where I'm living the American Dream with my wife Malaya and our son Rainer.

Ph.D. Economics — 2024
Florida State University
M.S. Economics — 2019
Florida State University
B.S.B.A. Economics — 2018
University of South Alabama