Practical AI in HR: How Evrone Optimized Salary Parsing

Artificial intelligence in HR often promises automation at scale. Evrone chose a focused experiment: improving salary expectation parsing inside its ERP.

Recruiters at Evrone struggled with inconsistent salary formats:

• “200/year”

• “$3000”

• “min 180”

• “open to your range”

Manual normalization drained time and motivation. Evrone decided to engineer a sustainable solution.

Step 1: Resume Parsing with Qwen

Evrone deployed Qwen with structured output. Unlike traditional parsers, Qwen processes long CVs and delivers strictly formatted JSON. Evrone’s ERP now receives:

• Category

• Grade

• Location

• English level

• Salary expectations

Step 2: Fine-Tuning YandexGPT

Evrone extracted 10,000 salary records and fine-tuned YandexGPT using LoRA. The model focuses on two parameters:

1. Amount

2. Currency

Accuracy reached 95%, with USD detection at 97%.

Results

Within 3 months:

• 90% fewer external tool lookups

• Faster candidate matching

• Improved recruiter focus

✨ Evrone proves that AI adoption works best when companies start small, measure impact, and iterate carefully.

Evrone’s Journey from Manual Data Entry to Intelligent ERP.