The company relied on highly manual Excel-driven workflows for job matching, grading, and job description creation, where analysts spent 30–40+ hours weekly managing fragmented data across multiple spreadsheets. This process limited productivity to only 10–12 job evaluations per analyst per day, creating operational delays, inconsistent outputs, and high labor costs.
30H
Weekly time cost
Average time spent job reviewing per week
2X
More Business Cost
Higher business cost to Recruite Analyst and Train them.
20%
Only Accuracy
Higher risk of job evaluations due to Excel manual workflows.
As business demand scaled, maintaining a workforce of nearly 10,000 analysts became financially unsustainable. The lack of automation, audit visibility, and AI-assisted decision-making increased human errors and slowed delivery quality, making it difficult to scale operations efficiently while controlling business
The Core Problems
Everyone hates spreadsheets
Analyst are asking reviewers to fill in spreadsheets to log translation issues, but it's clearly ineffective and hard to organize.
Impossible to review all Datapoints
For larger customers, reviewing over +170 datapoints for one single job title which is unmanageable, even though they want to.
Different reviewers, different standards
Different reviewers have different standards, creating many false-positive issues and making it tough to measure quality accurately.