WHY WE BUILT IT
Why Gaugely exists
Hiring is broken from both sides: recruiters drown in unread applications, formatting admin, and a fraud wave, while candidates face undisclosed AI, auto-rejection, and silence. Gaugely was built to fix both at once — AI does the screening labor, humans make every decision, and nobody leaves without feedback. The numbers behind each pain are below, sourced.
Every statistic on this page is documented with named sources on our AI interview statistics reference.
The recruiter's week we watched break
The unread pile
High-volume roles collect hundreds of applications; without screening capacity the realistic outcome is an unread pile and ghosted candidates — not a thoughtful human review of each CV.
What we built: Every CV is parsed and scored 0–100 against the JD with explicit skill matches and gaps, so the pile becomes a ranked, evidence-backed shortlist in minutes. Every candidate gets heard by the AI interview; your hours go to the finalists.
The admin tax
Recruiters spend 5–8 hours a week turning interview notes into hiring-manager summaries (15–25 screens at 20–30 minutes each); practitioners report AI freeing 30–45% of their week — almost all of it downstream of sourcing.
What we built: Scorecards write themselves during the interview: same rubric, quoted evidence, comparison view, recording. There are no loose notes to format — the write-up step is gone, not accelerated.
Inconsistent screens
Manual phone screens give candidate #1 and candidate #40 different questions, different energy, and different bars — the inconsistency structured interviewing exists to remove, and a real fairness and defensibility risk.
What we built: Every candidate gets the same structured interview scored against the same rubric, with every score citing their own words. Structured, evidence-based screens are also what EU AI Act auditability expects.
Fake candidates
Deepfake interview fraud grew ~1,300% between 2023 and 2024; 38.5% of candidates admit some form of interview cheating; one in three managers has caught a fake identity or proxy mid-process. Recruiting leaders rank fraud the #1 hiring threat of 2026.
What we built: Layered defenses: optional government-ID + liveness verification before the call, live follow-up probing that breaks scripted answers, presence and plagiarism flags during it — all logged as evidence for a human, never auto-rejection.
Tooling priced for enterprises
Incumbent AI interviewing runs HireVue-style custom contracts (~$35,000+/yr) or Karat-style per-interview billing ($300–450 / interview) — and most vendors will not publish a number without a sales call.
What we built: Published flat pricing from $0 (free tier, ~12 interviews/month, no card) to $75 and $199/month, billed as an interview-time pool. No per-seat fees to read a scorecard, no setup fee, cancel anytime.
The candidate experience we refused to ship
38% of job seekers have walked away from a hiring process over an AI interview — but only 19% want less AI in hiring. Read those two numbers together: candidates are not rejecting AI interviews, they are rejecting undisclosed, unaccountable, human-free ones. Each quit-driver in the data is a design choice, and we made the opposite choice on every one.
Undisclosed AI
27% of candidates who quit an AI-interview process did so because the company failed to disclose how AI would be used; one in five only discovered AI was involved after the interview had started (Greenhouse, 2026, n=2,950).
What we built: Disclosure is structural: the invite says an AI conducts the interview, and the consent screen lists what is recorded, why, and for how long — before anything records.
No human in the loop
The single biggest quit-driver (33%) is pre-recorded video scored by AI with no human present. Candidates reject unaccountable AI, not AI — only 19% want less AI in hiring overall.
What we built: A named recruiter reads the evidence and decides. Auto-rejection does not exist in the product — that is an architectural rule, not a policy promise.
The silence
38% of job seekers have abandoned a hiring process over an AI interview — and the deeper, older pain: most rejected candidates hear nothing at all, ever.
What we built: When a job closes, every opted-in candidate gets honest, personalized feedback drawn from their own interview — strengths, gaps, and a study plan in a private portal. No more silent rejections.
Scheduling friction
The phone screen a candidate cannot book until next Thursday loses more applicants than any AI ever did — scheduling is the quiet funnel-killer of high-volume hiring.
What we built: Interviews are async: candidates start from their invite link whenever ready — 9pm after work counts — in the browser, no app, no calendar dance.
I'm Asad Mahmood, a software engineer in Lahore, Pakistan. I build the AI that screens you — and that sentence is exactly why the rules in this product are hard-coded: consent before recording, a human on every decision, evidence behind every score, feedback instead of silence, and a price anyone can read without a sales call. Gaugelywas built to the EU AI Act's high-risk bar before the December 2027 deadline made it mandatory, because the bar is simply what responsible screening looks like.
Questions people ask
Who built Gaugely?
Asad Mahmood, a software engineer, founded Gaugely in 2026 in Lahore, Pakistan. It is founder-led and founder-built — the person who wrote this page also wrote the consent gate. Building AI that screens people is a responsibility; that is why the human-decision and disclosure rules are architectural, not marketing.
Why not just use HireVue or another incumbent?
If you are an enterprise running tens of thousands of interviews a year with budget for a ~$35,000+/yr contract, the incumbents are credible choices. Gaugely exists for everyone below that line: startups, SMEs, and agencies who need the same screening leverage at published prices, starting free.
Is an AI interviewer even ethical?
Done wrong — undisclosed, auto-rejecting, surveillance-heavy — no, and 38% of candidates walking away proves the market agrees. Done right, it is more ethical than the status quo: every applicant actually gets interviewed (instead of an unread pile), the same questions and rubric apply to everyone, a human makes every decision, and rejected candidates get real feedback instead of silence.
What does Gaugely deliberately NOT do?
No AI sourcing (practitioners report it wastes more time than it saves — we published that analysis), no automated rejection (architecturally impossible), no emotion inference or personality scoring (prohibited under the EU AI Act, and pseudoscience anyway), and no hidden pricing.