Test the path,
not the answer.
AI can produce a flawless CV and a clean first answer. It cannot fake lived experience. CheckAnyCandidate turns each candidate's own claims into the evidence your panel needs to establish, then tracks what the interview actually proved, what stayed uncertain, and what to test next. A human always decides.
You run the interview and make the call. We lay out the evidence, never a score or a verdict, and never watch the candidate.
Most technical interviews fail before the room
A recruiter hands over the CV. The hiring manager knows the role risk. The interviewer is left to turn both into a useful technical conversation, and that handoff is usually too vague.
CheckAnyCandidate creates a Technical Evidence Brief for the screen: which claims matter, what evidence to collect, what strong answers sound like, and what stays untested afterwards.
What the evidence runtime tracks
A question generator hands you a list and a rating. The runtime tracks the evidence each claim requires, what the interview has established, and what is still uncertain, so the next question targets the biggest open gap.
Evidence slots, not model answers
Each claim defines what must be demonstrated, not what the answer should sound like. A candidate can solve a different but valid problem and still land the evidence.
Coverage state, live
Every claim reads covered, partial, or untested against the evidence it required. It scores the interview, never the person.
Source-anchored evidence events
Each piece of evidence points back to the candidate's own words and the dimension it satisfies, confirmed by the interviewer before it counts.
Calibrated to seniority
The same claim requires more at staff than at junior. A design decision at staff has to reach invalidation: when it would stop being the right choice.
Corroboration for the hard claims
Abstract claims like ownership need several independent, compatible events across different contexts before they count as established.
Provenance you can audit
Every evidence requirement is tagged with where it came from and how far to trust it, from a formal standard down to an expert heuristic.
From a claim to the evidence it needs
Each probe is anchored to a specific claim from the candidate's own CV, and exists to establish one piece of evidence.
“Led migration from batch ETL to a real-time streaming pipeline.”
Walk me through the first production issue after the migration. What broke, how did you notice, and what did you change?
Now assume downstream consumers require near-real-time data, but the source system sends duplicates. What changes in your design?
Idempotency, deduplication, replay, event time vs processing time, monitoring, rollback, data correctness.
Only listing Kafka or Spark, no failure modes, no ownership detail, no trade-off discussion.
An interview platform asks questions. An evidence runtime tracks what you proved.
Note-takers and scorecards record what was said. AI interviewers ask the questions and score the person. CheckAnyCandidate does neither. It standardises the evidence a role requires, preserves where each piece of evidence came from, and tracks what is still uncertain.
AI interview platforms ask and score questions. CheckAnyCandidate tracks what technical evidence has actually been established, what remains uncertain, and what to test next. A human always decides.
Works alongside your existing ATS, scorecard, or AI note-taker.
Sharper interviews, not more work
A custom interview for every candidate sounds like more effort. It is the opposite. Generating a probe set takes under a minute: upload the CV and job description, and the probes, follow-ups, and required evidence are ready.
During the screen you are not improvising or deciding what to ask next. You are following a structure built from that candidate's own claims. Same hour, far more signal: you leave knowing whether they can do the work, not just whether they interview well.
Polished answers are cheap. Lived experience still has texture.
AI can help candidates prepare cleaner CVs and better first answers. That does not mean the candidate is dishonest, and it does not mean AI should be banned.
But it does mean technical interviews need to move past static questions. CheckAnyCandidate helps interviewers ask about the messy parts of real work: the failure, the constraint change, the rejected trade-off, the incident, the thing that broke in production.
We do not ban AI. We test the judgment that responsible AI use still requires.
How it works
From CV paste to live interview in four steps.
Paste the CV and job description
CheckAnyCandidate extracts concrete, testable claims and the evidence each one needs to establish, calibrated to the role and seniority.
Compile the required evidence
Each claim becomes evidence to establish, with a probe and a follow-up perturbation to reach it. You see what strong evidence looks like before the call.
Run a human-led screen
Work through the evidence with the candidate. The runtime tracks what each answer established, not whether it sounded polished.
See the evidence state
Every claim reads covered, partial, or untested, with the evidence events behind it and what stays uncertain. The decision stays fully human.
Not a question generator. A claim-to-evidence path.
Generic tools produce a list of questions. CheckAnyCandidate compiles the evidence each claim requires, then tracks whether the interview established it. Four mechanics collect that evidence, each anchored to the candidate's own CV claims.
Pose a realistic scenario from their domain, then change a constraint partway through. Tests whether they re-reason or recite a memorised path.
Surface two claims from their CV that are in tension and ask them to reconcile. Shows whether the CV reflects real, coherent work.
Show a broken version of the kind of thing they said they built. Hands-on familiarity is immediately visible. Surface description is not.
Show a plausible but flawed AI-generated solution and ask them to critique it. Tests judgment, the one thing AI cannot outsource in real work.
Your best candidates will respect it
Strong candidates are not filtered by keywords or judged by a bot. They are invited to talk about real work: the incident, the trade-off, the thing that broke in production. The people you most want to hire are exactly the ones this kind of interview rewards.
The right friction reveals depth. CheckAnyCandidate adds structured follow-up pressure, so polished claims become testable evidence, not a memory test and not a trap.
What CheckAnyCandidate is - and is not
Built for
- Technical hiring managers
- Engineering leads
- Data and platform leads
- Anyone running senior technical screens
We help you
- Turn CV claims into structured technical probes.
- Test whether candidates can reason through claimed work.
- Capture evidence from a human-led interview.
We do not
- Detect AI use or cheating.
- Score, rank, or reject candidates.
- Use webcam, audio, keystroke, or behavioural monitoring.
- Predict job performance with a model.
- Automate a hiring decision.
We structure answer evidence, never timing or behaviour.
Built first for senior technical screens
CheckAnyCandidate currently supports curated probe libraries for:
- Senior Backend Engineer
- Senior Frontend Engineer
- Senior Full-stack Engineer
- Senior Data Engineer
- Senior DevOps / Platform Engineer
- Engineering Manager
Coming next:
- Machine Learning Engineer
- Data Scientist
- Platform / SRE variants
- Mid-level technical roles
Ready to improve your technical interviews?
Request access to our claim-anchored interview platform. We are onboarding teams in small cohorts and will be in touch by email.
No recordings. No proctoring. No AI verdicts.