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Why Your AI Isn’t Working: Lessons From Cleaning Up 20 Years Of Dirty Data

LinkedIn appeared over 10 different ways ("LinkedIn", "linkedin", "Linked In")

We’ve all heard the phrase "data is the new oil." But what if your "oil" is buried under decades of inconsistent labels, duplicated records, and legacy systems that don’t talk to each other? 

That’s exactly what we found when a national technology staffing firm came to us for help. 

They had 20 years of recruitment data: Fortune 500 clients, a cutting-edge tech stack, and still they couldn’t answer basic questions like: Which job boards actually bring in hires? or How long does our process really take?

I’m going to walk you through what we uncovered, how we tackled it, and - just as importantly! - how you can apply what we learned to your next big tech or data project.

Meet the Client: A Company Ready to Scale Smarter

This company wasn’t behind the curve. On paper, their infrastructure looked advanced.

But when we started asking simple strategic questions, their systems gave lackluster outputs. That’s when we knew: they were data-rich, insight-poor.

The biggest offenders?

  • LinkedIn appeared over 10 different ways ("LinkedIn", "linkedin", "Linked In")

  • 84% of candidate profiles were never updated

  • 100,000+ duplicate profiles existed in the system

  • Location data like "United States" appeared as "USA", "us", "United States of America"

Takeaway for your next project: Audit your data inputs before upgrading your tech. Build a small team to assess how many variations exist for core fields like source, location, and skills. Create a short report highlighting top inconsistencies and how they affect reporting.

A recent Gartner study found that poor data quality costs businesses an average of $12.9M per year. 

The Problem: When Bad Data Becomes a Bottleneck

Their messy data wasn’t just annoying—it was expensive.

  • Recruiters wasted hours managing duplicates

  • No clear ROI on expensive job boards

  • They couldn’t build the AI features their clients were asking for

The CEO was feature-obsessed, constantly pitching innovation to clients, but the foundation wasn’t there. He had the ambition, the market, and the team but he didn’t have trustworthy data to build on.

Takeaway for your next project: Run a quick test: have your team answer three business-critical questions using existing data. If they can’t do it quickly or the answers vary wildly, it’s time to invest in a data health assessment.

You can read the whole blog post (and learn exactly what we did and how we did it) here

This wasn’t just a one-off cleanup. It was a strategic reset.

And they didn’t just fix their database. They transformed how they think about and use data across their business. From intake forms to performance reviews, everything became an opportunity to learn.

And that’s the big shift: data isn’t just an asset, it’s an advantage.

Want more insights from past projects? How we helped our client scale personalized care to millions of people, how we helped another client remove a major bottleneck, and how we helped a client automate the boring stuff

“I can’t even think of the money and time we wasted trying to build our product before we found Neutech. With their dedicated and bespoke approach, we’ve accomplished more in the last 8 weeks than we have in the last year. I’ve now got transparency and a team I know I can really trust - a founder’s dream.” - Jesse Link, CEO Rella