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Language-aware document intelligence
OCR and understanding for Arabic and mixed Arabic/English documents — right-to-left layouts, diacritics, and complex legal and administrative structure.
Research
Applied AI research, grounded in real documents and workflows.
Qomrah’s research focuses on trusted, language-aware document intelligence — combining OCR, retrieval, vision-language models, and rigorous evaluation to make AI dependable on sensitive, high-value work.
Focus Areas
Where we invest our research.
Our work is workflow-led: we study the problems that stand between capable models and trustworthy production systems.☍
Grounded generation & retrieval
Connecting model outputs to approved sources, so answers carry citations, evidence, and a verifiable trail rather than unsupported claims.
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Multimodal understanding
Combining text, layout, tables, and images — vision-language models applied to real documents, forms, and operational scenes.
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Evaluation & benchmarking
Measuring accuracy, faithfulness, and exception behaviour on domain data, so systems are judged on the work they actually do.
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Human-in-the-loop systems
Review, correction, and escalation as first-class parts of the pipeline — keeping people in control of critical decisions.
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Responsible & private AI
Privacy-preserving deployment, governance, and transparency for sensitive enterprise and public-sector data.
From research to production
Research only matters when it survives contact with real work.
We move ideas from prototype to production with the same discipline throughout: grounded outputs, measured accuracy, human oversight, and privacy by design. Findings feed directly into the capabilities our clients deploy.
Grounded
Outputs linked to trusted sources and evidence.
Measured
Evaluated on domain data, not generic benchmarks alone.
Language-aware
Built for Arabic and mixed-language documents.
Accountable
Human review, audit trails, and governance throughout.
Collaborate
Working on a hard document or workflow problem?
If your organisation has complex documents, sensitive decisions, or language-specific challenges, we’d like to hear about it.