The Problem Was Right in Front of Me
After years of sitting in front of data — cleaning it, correcting it, building reports around it — I started noticing a pattern.
Every company I worked with had the same problem: data errors. Typos in customer names. Inconsistent date formats. Duplicate records. Missing values. These weren't exotic edge cases — they were everywhere, quietly corrupting the insights that executives were using to make decisions.
The tools available to fix this were either too technical (SQL queries, Python scripts) or too manual (find-and-replace in Excel). There was no elegant, business-user-friendly solution that combined data cleaning with reporting.
So I built one.
What CrushErrors Does
CrushErrors is a SaaS platform that helps organizations:
- Identify and resolve data quality issues across multiple data sources
- Generate clean, reliable reports with AI-driven anomaly detection
- Reduce the time finance teams spend reconciling and cleaning data
- Build confidence in the numbers before they reach the C-suite or board
The core insight: you can't make good decisions with bad data. And most companies are making decisions on worse data than they think.
The Patent
In October 2022, we received US Patent #11,475,026 for the core technology behind CrushErrors. That was a proud moment — not just for the intellectual property value, but as validation that we'd built something genuinely novel.
Building While Consulting
I won't pretend it's easy to build a product while simultaneously running CFO engagements. It requires discipline, ruthless prioritization, and a willingness to do things imperfectly.
But the two roles feed each other in unexpected ways. Every client engagement gives me new insights into what businesses actually struggle with. And building CrushErrors keeps my technical skills sharp in ways that make me a better CFO.
What's Next
We're continuing to expand CrushErrors' capabilities — deeper AI integration, more data source connectors, and a new reporting module launching later this year. If your organization struggles with data quality, check it out.