The Embedding API analyzes an HR object (a profile, a job, an experience, an education, a list of skills, a summary, a project, and more) and returns a numerical vector representing the input HR object in a 1024-dimensional space.
Focus on delivering high-quality
AI algorithms and not features engineering
The vectors of similar HR objects will be close to each other in the 1024-dimensional space. Therefore, AI developers can use the Embedding API for filtering, indexing, ranking, and organizing HR objects according to semantic similarity.
Broad use cases
Similarity analysis, Search and retrieval, Machine transfer learning.
A large portfolio of vector families to satisfy your use cases: Profile2Vec, Job2Vec, Experience2Vec, Education2Vec, Skills2Vec, Interests2Vec.
Fairness is not the default; that‘s why we built inclusive models that measure and mitigate unintended bias in the HR data.
Supports sequences of adjacent words that have a sense together.
We enrich word vectors with relevant sub-word information.
Supports 32+ languages (Arabic, Chinese, Dutch, English, French, German, Italian, Japanese, Portuguese, Romanian, Spanish, and more.)
Your return on investment
To solve your HR challenges
To scale globally
To focus on impact
The HrFlow.ai Embedding API lifts a heavy workload on your R&D team
Benefit from 6 years of expertise in representing HR objects.
Don't take our word for it
Job matching accuracy
« HrFlow.ai Embedding API allowed us to build quickly a bias-free staffing AI algorithm that has surpassed our historical human performance. »