Case study 04 · Online learning · Careers
Removing manual job-data processing.
~90%of manual parsing removed
2AI models working together
1clean, structured feed
The problem
The platform needed a steady supply of accurate job listings. Each one arrived in a different format and had to be read, cleaned up and entered by hand before it could be used.
Manual parsing limited how many jobs the team could publish and made the data inconsistent. Every new source meant more repetitive work.
What we changed
- An automated ingestion pipeline that collects job information as it arrives
- AI extraction of the key details from unstructured listings
- Enrichment that adds useful context and consistent categories
- Structured output ready for the platform's downstream workflows
The result
About 90 percent of the manual job parsing work is now automated, and listings arrive in a consistent structure. The team reviews instead of retyping.
The lesson: the right mix of AI models, fast where speed matters and strong where accuracy matters, keeps quality high and costs low.
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