The problem
3%
Average interview callback rate today
Source · CareerPlug 2024
About
Why Veraxis exists.
Today, a candidate applies to 100 jobs and hears back from 3. That is not a talent problem — that is a system problem.
Look at it from the other side and it’s the same story backwards. A single opening draws around 180 applications, of which a handful are genuinely worth interviewing. Recruiters aren’t ignoring people out of indifference. They’re drowning.
And the two halves feed each other. When nobody tells you where you stand, applying everywhere is the rational move — which is exactly what makes the pile unreadable. Everyone inside that loop is behaving sensibly. The loop is still broken.
Veraxis cuts it where it can be cut: before the effort is spent. One honest number against one specific role. The same number both sides see, with the reasons attached. Above the bar, you apply with evidence. Below it, you get a named gap and a path to close it — instead of silence.
No randomness. No ghosting. No guessing. Just the right person in the right role.
The system, by the numbers
From broken to engineered.
Three numbers describe the problem we are solving and the future we are building.
The noise
180:1
Applications per hire on average
Source · CareerPlug 2024
The future
90%
Our target interview callback rate for matched candidates
The goal the system is engineered toward — not a promise we’ve earned yet.
Where we are
What’s live, and what’s coming.
We’d rather tell you than have you find out. Here’s the honest split.
Available now
Relevance scoring — a 0–100 score for every candidate against every open role, with named strengths and named gaps
Skill gap analysis — the specific distance between you and the bar, ranked by what matters, with time-to-close
Learning roadmaps — three tiers built from a verified course catalogue; every course real, checked, and linked
AI interview coach — a mock interviewer that has read your resume and the actual job, ending in a scored debrief
Practice engine — SQL, coding and verbal questions generated on demand, graded against real execution
Recruiter shortlists — job-scoped, quality-floored, showing the same reasons the candidate saw
Building toward
Proctored certification — verified, tamper-resistant skill credentials
Native interview hub — live interviews hosted on-platform with rubric scoring
Offer SLA enforcement — time-stamped commitments, so nobody is left waiting
Recruiter automation — pipeline agents, alerts, and hiring funnel analytics
Everything in the second column is on the roadmap, not on the platform. When it ships, we move it.
How we build
Four rules we don’t bend.
01
Fairness by construction, not by policy
Our scoring engine is never shown a candidate’s name, home city, or university. One reaches the model only when an employer has explicitly stated it as a requirement. Two versions of the same candidate are the same input, so the score cannot move because of identity. We test this by running the same candidate twice with only those details changed, and checking the score doesn’t move. It doesn’t. Not because the model was told to ignore them — because they were never there to see.
02
One number, both sides
A candidate and a recruiter see the same score, the same strengths, the same gaps, off the same record. Neither side gets a different story. And there is no global candidate directory — every view hangs off a specific role, because a relevance number is meaningless without a role to be relevant to.
03
Measured before it’s trusted
Every AI surface is checked against a written standard before it reaches anyone. Some checks block a release outright: a quoted line must exist in the transcript it came from, a recommended course must exist in the catalogue, and identity must not move a score. We don’t relax a check to make it pass.
04
We publish what’s still wrong
We keep a written record of where the system is weak and by how much — including the findings that make our own work look harder. If we can’t measure a claim, we don’t make it.
A note from the founder
The worst part of looking for a job isn’t rejection. It’s not knowing.
You spend an evening on an application — tailoring the resume, rewriting the cover letter, second-guessing the phrasing — and then nothing. No reply, no reason, no sense of whether you were close or nowhere near. So you do it again the next night, and the night after, learning nothing each time.
I kept coming back to how strange that is. Every other part of life gives you a signal. You know if you’re passing a course. You know if you’re winning a game. Only here, at the thing that decides your income and your next five years, do you get silence.
And the people on the other side aren’t villains. A recruiter with 180 resumes and one afternoon isn’t ignoring you — they physically cannot read them properly. The system asks both sides to guess, and then punishes both for guessing wrong.
Veraxis is my attempt to replace the guessing with a number.
One honest score against one specific role. Named strengths, named gaps, and a path to close them. If you’re a match, apply knowing it. If you’re not, find out before you spend the evening — and get told exactly what would change the answer.
The rule I care most about: that number is the same on both sides of the table. The candidate and the recruiter see the same score and the same reasons. Nobody is shown a flattering version. A hiring decision either survives being explained to the person it’s about, or it shouldn’t have been made.
We’re early. But that’s the product, and it’s the whole reason it exists.
Kapil Vaishnav
Founder, Veraxis · August 2026
For candidates
Know where you stand before you spend the evening.
For hiring teams
See a shortlist that has already cleared the bar.