Detect fabricated experience before it costs you a hire.
Interviews validate performance.
They do not validate history.
And hiring decisions are made on history.
This candidate may look strong.
If their experience is fabricated,
you will not detect it in interviews.
And you will only discover it after the hire.
In an AI-saturated hiring environment, output is no longer evidence.
Paste a candidate resume. See what others miss.
Paste a candidate resume or experience claims below and run the analysis.
Adding a JD exposes role-specific fabrication signals.
↑ PASTE RESUME ABOVE
Your seven-dimension analysis will appear here.
Seven-Dimension Framework
These are not resume checks.
These are forensic signals designed to expose fabricated experience.
Timeline feasibility, experience depth relative to tenure. Is the claimed accumulation of seniority possible within the stated time?
Claimed technical breadth versus realistic depth. Full-stack mastery plus ML plus infra in 36 months is a fabrication signal, not a strength.
Specificity, plausibility, and causal attribution of impact numbers. Precise metrics without ownership context are orphaned data.
"Led" and "drove" without org structure, headcount, or tradeoff context are noise. Real ownership leaves an evidence trail.
Logical career progression versus opportunistic keyword accumulation. Real seniority leaves a coherent arc. Fabrication leaves a portfolio of trends.
Vocabulary consistency with claimed seniority. AI-assisted authoring produces uniformity across years and roles that genuine experience does not.
Evidence of tradeoffs, resource limits, and failure modes. Real engineers have scars. Fabricated experience does not account for them.
Five AI Fabrication Signal Patterns
Suspiciously consistent formal register across roles and years. Genuine career documents accumulate stylistic drift. AI-assisted ones don't.
Precise impact numbers lacking causation, attribution, or context. "Reduced latency 40%" with no baseline, method, or measurement owner.
Vague ownership language without org structure, decision authority, or accountability evidence. "Led" is not evidence.
Identical claimed depth across multiple disparate technologies. Genuine mastery produces depth inequality. Fabrication produces a flat list.
Too much claimed within too short a window. Senior IC growth has recognizable pacing. Fabricated experience ignores it.
How It Works
Drop resume text, LinkedIn summary, or raw experience claims into the Career X-Ray tool above. No account required.
Claims are evaluated across all seven dimensions simultaneously, cross-referenced for internal consistency and AI fabrication patterns.
Receive a Provenance Risk Score, fabrication flags, and targeted validation probes. Full reports are human-reviewed before delivery.
Every full report is reviewed by an examiner. This is not an automated verdict.
Built For
At Staff+ and Principal IC level, one misrepresented hire doesn't just cost salary. It costs team credibility, architectural decisions, and 12–18 months of compounding damage before anyone names what happened.
TechCredalyst is not a background check. It's a provenance system — the methodology of art authentication applied to engineering experience.
You Should Be Here If
Data Handling Statement
Experience claims, resume text, or LinkedIn summaries submitted for analysis. No candidate PII is required to run an assessment.
Input is analyzed against the seven-dimension framework. No data is stored, sold, or used for model training. Analysis is session-scoped.
Full Examiner's Assessment reports are human-reviewed before delivery. Final assessment is professional judgment, not automated output.
AI is used as a private signal-surfacing tool within the examiner's workflow. All conclusions reflect human professional judgment.
If that experience is fabricated,
you will not detect it before the hire.
Validate before you proceed with the hire.
Because you will not get a second decision on this hire.