Case Study
~$30,000 a year in fraud, now screened before it ships.
How Providence AI built custom fraud screening and operational intelligence systems for a Wisconsin-based American manufacturer.
Presented anonymously at the client's discretion. Details are accurate; the company is not named pending their approval to use it publicly.
The Client
A Wisconsin-based American manufacturer of specialty consumer and institutional products, serving a customer base that ranges from individual consumers to large institutional buyers. The company runs lean — a small full-time team handling a high volume of orders, especially during seasonal peaks — with no dedicated IT or data function.
- Industry
- Specialty manufacturing — consumer & institutional products
- Location
- Wisconsin, USA
- Team
- Small full-time team, plus seasonal staff at peak
- In-house IT / data
- None — a lean operation
The Problem
Over the prior year, the company had absorbed roughly $30,000 in losses to fraudulent orders. With a small team stretched across operations, there was little capacity to manually review every order for fraud signals — and higher-risk orders could slip through to fulfillment, turning into chargebacks and lost inventory after the fact.
Beyond fraud, the business faced operational blind spots common to growing companies: no systematic way to track competitors or market movement, purchasing decisions driven by intuition rather than order data, and little time left over for strategic research and product development.
The Approach
Rather than recommending an enterprise fraud platform that would cost more than the losses themselves, Providence AI built fitted systems tailored to the company's actual operations — and designed them with security as a first-order constraint.
A defining decision: the fraud-screening system was architected to operate outside the company's core network. Order information is routed to an isolated environment where an AI agent evaluates each order against custom risk criteria — so the analysis never requires opening up the systems that run the business. Security shaped the architecture, not the other way around.
Over several weeks, we built four complementary systems:
- 1
Order Risk Screening
Incoming orders are automatically routed to an isolated environment, where an AI agent scores each one against custom risk criteria and flags high-risk transactions for human review before fulfillment.
- 2
Competitive Intelligence Agent
A weekly automated system that monitors the company's top competitors, surfaces product-line gaps and market trends, and delivers a leadership-ready strategic brief every Monday morning.
- 3
Purchasing & Trends Agent
Connected to the order log, this system compiles the week's orders and delivers a data-driven purchasing report to operations every week — enabling trend-based inventory decisions instead of guesswork.
- 4
Operations Productivity Layer
A custom workflow that centralizes task management, tracking, and reporting — built first as a proof of methodology before the larger systems were deployed.
The Outcome
All four systems are operational and running in production. The order risk screening system is live and has processed well over a hundred orders, flagging higher-risk transactions for human review before they ship. The competitive intelligence agent delivers weekly strategic briefs to company leadership, and the purchasing & trends agent began running automated weekly reports during the company's busiest season.
On the numbers — stated honestly
The company had been losing roughly $30,000 a year to fraud before these systems existed. The system has been live only a short time, so we do not claim it has already recovered a specific dollar amount. What we can say is that the $30,000/year in fraud exposure is now actively screened rather than going unreviewed — with a human review step where, before, high-risk orders could pass straight through. The proven-dollars-saved figure will come with time and data, and we won't claim it before then.
- —~$30,000/year in fraud exposure now actively screened on every order — before fulfillment
- —Hours of manual competitive research replaced with an automated weekly Monday brief
- —Data-driven purchasing decisions replacing intuition-based ordering
- —Operational visibility increased without adding headcount
What This Means For Your Business
This engagement validated something we believe broadly: small and mid-market businesses don't need enterprise AI platforms. They need fitted systems, built around their actual operations, designed to be maintained by their actual teams, and architected with security in mind from day one. The patterns developed here — risk screening, intelligence agents, purchasing optimization — translate directly to healthcare practices, trades businesses, and specialty retailers facing similar operational challenges.