Resume Ranker
Read every résumé the same way, then rank candidates against the role. Built by ToktoAI on models we trained ourselves.
From a pile of files to a shortlist
Before generative AI, reading résumés and comparing them with each other was slow manual work. Resume Ranker was built to do it with trained machine learning models.
Each résumé goes through the same steps, so the shortlist depends on skills and experience, not on who read the pile first.
What it extracts
A separate named entity recognition model is trained for each field, so every one can be tested and improved on its own.
- Skills
- Education
- Institutions
- Designations
- Employers
- Dates of joining
- Years of experience
Parse
Text is pulled out of PDF and Word résumés, whatever the layout.
Extract
NER models tag skills, education, employers and dates.
Rank
Candidates are scored against the job and listed best match first.
Why teams use it
Faster screening
Large batches are read in one pass instead of one résumé at a time.
Consistent criteria
The same fields and the same scoring apply to every candidate.
Fits your process
We tune the fields and ranking to your roles and connect it to your systems.
Have data and a question? Let’s talk.
Tell us what you need to read, see or predict. We will get back to you directly.