AI matching drove 130% user growth in six months and a new placement-based revenue stream.
- User base growth
- +130%
- Placements
- 35-70 / month
- Revenue per placement
- ~$1,100
What we shipped
A matching engine and personalized job recommender that connect candidates with employers using profile, behavioral, and hiring signals.
Online job platforms often depend on a narrow set of revenue streams, most commonly paid postings. The model works during hiring booms but is fragile when hiring slows.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Used AI Opportunity Mapping to identify matching, recommendations, and content as priority initiatives.
- AI Pilot
Built NLP-based profile and job representations and ranking models, validated on historical outcomes.
- Production launch
Shipped the matching engine plus a personalized job recommender with a placement-based revenue model.
- Scale and operate
Extended to resume parsing, interview prep, hiring demand forecasting, and workforce analytics.
Business, technical, and governance outcomes.
- 130% user growth in six months.
- 35-70 placements per month via AI matching.
- New revenue stream per successful placement.
- Stronger candidate and employer fit.
- Python
- PyTorch
- Elasticsearch
- PostgreSQL
- Airflow
- Redis
Continuous learning loop from applications and hiring outcomes, with ranking models monitored for fairness and relevance.
Working on something similar?
Schedule a call. We will tell you honestly whether AI is the right move.
Reference calls available under NDA after the second working session.