Question 1

What types of documents can Turing Verify check?

Accepted Answer

Turing Verify supports diplomas, university degrees, transcripts, professional certifications (AWS, CPA, CFA, PMP, CompTIA), government IDs (national IDs, driver licenses, healthcare IDs), insurance cards, employment verification letters, offer letters, and trade-association credentials.

Question 2

How accurate is Turing Verify at detecting fake documents?

Accepted Answer

Turing Verify runs a 50-pattern forensic analysis framework called V50-MASTER+ID. It examines visual, typographic, metadata, data-integrity, and AI-generation signals, and returns a confidence score per pattern so reviewers can audit the verdict instead of getting a black-box answer.

Question 3

How long does document verification take?

Accepted Answer

Most documents finish in under 30 seconds. The pipeline runs visual forensics, metadata inspection, internal data-logic checks, and registry cross-reference in parallel — far faster than the 3-to-14 days a manual registrar verification typically takes.

Question 4

Is Turing Verify free to use?

Accepted Answer

Yes. The free tier includes 3 verifications per month. Paid plans start at EUR 9 per month (Personal, 15 verifications), EUR 29 per month (Pro, 150 verifications), and EUR 99 per month (Business, 500 verifications with API access). Enterprise pricing is custom.

Question 5

Is Turing Verify GDPR-compliant?

Accepted Answer

Yes. Inference runs on EU-resident infrastructure. Documents are processed in memory and not retained unless the user explicitly opts in. The DPA and sub-processor register are public on the Trust page.

Question 6

Who uses Turing Verify?

Accepted Answer

HR and talent-acquisition teams screening candidate credentials, university admissions and registrar offices verifying transcripts and foreign degrees, banks and fintechs running KYC, insurance teams detecting claims fraud, and licensing boards authenticating professional credentials.

Question 7

Can Turing Verify detect AI-generated fake documents?

Accepted Answer

Yes. The pipeline targets diffusion-model and GAN signatures, synthetic-text patterns, and statistical anomalies in pixel distributions — failure modes that purely visual reviewers miss.

The people behind every verification verdict.

Jeff Hu

Areas of expertise

Why named authorship matters

The people behind every verification verdict.

Jeff Hu

Areas of expertise

Why named authorship matters