Atlas AI

Models that read, compare and check.

Built for Arabic and Latin scripts and for documents from all over the world. This page says what each technology does today, and what is still in preparation.

languages read: Arabic, French, English
3
scripts: Latin and Arabic
2
identity document types, any issuing country
4
built-in document types, plus your custom types
10
face and liveness models
4
presentation-attack signals
6

Our technology

Eight building blocks, one verification.

Each building block is part of one or more products. Anything not yet available is marked "Coming soon".

Document verification

Authenticity, consistency and validity

The document is recognised (country, type, version), its fields are extracted, then checked against each other and against what the customer declared.

  • ID cards, passports, residence permits, driving licences
  • Document type and country compared with the customer's choice
  • Expiry date checked

Multilingual OCR & VLM

Arabic, French and English

Character recognition and vision-language models read the document in its own language and tie each value to the right field, with a confidence level.

  • Arabic, French and English, bilingual documents included
  • Confidence per field, review below your threshold
  • Business validations: check digits, dates, amounts

MRZ & chip reading

MRZ checked; NFC chip coming soon

The machine-readable zone is read, its check digits verified, then cross-checked with the printed fields. Chip reading will happen in the mobile SDK.

  • MRZ check digits verified
  • Mismatch between MRZ and printed fields: review
  • NFC chip: data signatures verified on the server, reading from the mobile SDK coming soon

Face recognition

Selfie matched to the document photo

The face is detected on the document and on the selfie, then compared: the score is set against your threshold, and the result stays explainable.

  • Selfie matched to the document portrait
  • Selfie matched to a known customer's reference faces
  • Threshold adjustable per customer

Liveness detection

Passive, against presentation attacks

Passive detection on a single selfie: no gestures required. It flags known presentation attacks.

  • Replayed screen
  • Printed or photocopied photo
  • Document portrait presented as a selfie
  • Several faces, no face, weak capture

Deepfake detection

Coming soon

In preparation

Detection of generated faces and of video streams injected into the camera is in preparation. It is not available yet.

  • Generated or altered faces
  • Video stream injection attacks

Document tampering detection

Photocopies, screen replays, MRZ mismatches

The document image is analysed to spot captures that are not the original, and the MRZ gives away edited fields.

  • Photocopies and replayed screens detected
  • Glare, blur and low resolution flagged
  • MRZ inconsistent with the printed fields
  • Tampering of supporting documents: in preparation

Arabic/Latin name transliteration

One name, recognised in both scripts

The same name is spelled several ways in Latin letters, and differently again in Arabic. Screening brings these variants together and explains every score.

  • Arabic and Latin compared directly
  • Spelling variants and particles (Ben, Bin, Al, El)
  • One score per entry, with an adjustable threshold

How we work

Models you can explain.

  • Every model is inventoried

    Bundled models are listed with their source, licence and checksum, and verified on every build.

  • Scores, not black boxes

    Each check returns a status, a score and readable reasons, the same in the API, the console and the webhooks.

  • Evaluated before announced

    Our models are evaluated on synthetic test sets; an evaluation on real data, collected with consent, will come before any published figure.

  • A human when needed

    Ambiguous cases go to review: your analysts decide, and their decisions stay logged.

See it in action

Test the models on synthetic identities.

The sandbox provides synthetic identities and documents: you see every check, its score and its reasons, without any real data.