
320B tokens, 43 languages, bias-monitored from training to deployment.
Building a Human-level Matching Algorithm: Our Approach for AI & Fairness
Recruiting tech has been stuck on two technologies since the nineties: keyword Boolean search and a 1939 occupation taxonomy that miss cultural fit, soft skills, and growth potential, so HrFlow.ai trained Cerebro on 320 billion tokens across 43 languages, with bias-monitoring built in at training and after deployment.
Trusted by Customers, Partners & the AI Ecosystem

The framework behind production-grade recruiting AI
The 7 regulatory requirements for high-risk AI in hiring mapped to the right architecture.
Five biases, named and tracked: gender, ethnicity, stereotype, age, location, audited at both the data layer and the model layer.
The hiring funnel as a train line: a visual builder labels every stage positive, neutral, or negative, the conversion goal the model optimizes against.
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Trusted by fast-growing HR Tech and Global Enterprise
The hardest problem in the industry
For 30 years, recruiting has leaned on keyword Boolean search and a 1939 occupation taxonomy, neither of which captures cultural fit, soft skills, or growth potential.
Building a human-level Matching algorithm is the most important & the most difficult challenge of the Recruiting and Human Capital software industry.
A hiring process is a train line
Recruiting funnels are sequential, candidates can exit at any stage, and the work is helping each one progress to the next.
We've designed our visual builder with our philosophy that a hiring process or talent experience is akin to a train line. Candidates can exit at any stage, and the aim is to help each candidate progress as far along the journey as possible.
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