Reads a resume like a great recruiter would. Scores it on 6 dimensions. Explains every score. Catches bias.
Average time a recruiter spends per resume. That's scanning for logos, not reading.
Applications per role. Snap judgments based on company names, college prestige, and keyword density.
Transparency. Traditional tools give a pass/fail with no explanation. No way to interrogate the decision.
The core failure: A candidate who "built a Chrome extension used by 50,000 people" gets rejected because their resume doesn't say "frontend engineering." Someone who copy-pasted the job description sails through.
Resume and job description go in. A detailed, explainable scorecard comes out.
Do they have the skills this job needs?
Job needs React. Candidate built a Chrome extension with 50K users. System recognizes that as frontend expertise.
Is their experience relevant and substantial?
2 years of high-impact work beats 8 years of routine work. Recent experience counts more than old.
Did they make a measurable difference?
'Reduced page load time by 40%' scores higher than 'improved performance.' Numbers and scope matter.
Is their education relevant?
Weight drops automatically for senior roles. Never over-rewards prestigious college names.
Is their career heading toward this role?
An engineer gradually shifting to product scores higher than a random lateral jump.
Can they express ideas clearly?
Evaluated from the resume itself. Clear writing and specific details signal strong communication.
'Built a Chrome extension used by 50K people' = frontend expertise. No keyword matcher catches that. This system does.
Checks if college prestige, company names, gender, or career gaps influenced the score. Every bias gets flagged transparently.
Every score comes with the evidence behind it. Hiring managers can agree, disagree, and override with full context.
A FAANG engineer switching to PM has relevant skills. The system connects the dots across domains.
'Owned P&L for $10M line' beats listing 'P&L management' as a skill. Substance over keywords.
B.Tech, CGPA/10, JEE ranks, CTC conventions, Hindi-English mixed text. Built for the Indian context.
'Built fraud detection processing 2M txns/day' — system infers ML, streaming, distributed systems expertise.
Staff Engineer applying for Junior role? Flagged as over-qualified — different signal, different action.
78 for a career changer = 78 for a traditional candidate. Reference-point calibration keeps scores meaningful.
Feed actual interview pass/fail data back into scoring. Over time, the system gets better at predicting which candidates succeed.
Before scoring resumes, check if the JD itself is vague, unrealistic, or gendered. A bad JD produces bad matches.
Across all applicants, show which skills are easy to find and which are rare. If nobody has a requirement, the JD needs to change.