Dalīl Group helps organisations test AI before release, monitor it after deployment, and produce decision-ready evidence for technical, risk, and leadership teams.
Organizations test AI in English and assume Arabic will follow. In practice, three distinct problems emerge — often invisibly, and often after launch.
Arabic responses can be shorter, less complete, and more likely to omit critical requirements — even from the same model that performs well in English. These are recurring patterns we explicitly test for.
Gender assumptions, cultural stereotypes, and regional blind spots are embedded in AI training data. They surface differently in Arabic — often in ways that English-only evaluation will never catch.
Regulators in the UK and GCC are asking harder questions about AI fairness and accountability. Most organizations deploying Arabic AI have no structured evidence to answer them. Dalīl provides that evidence.
Our structured evaluation framework tests AI systems across 40+ dimensions — factual accuracy, hallucination rate, bias indicators, instruction-following, and cultural integrity — side by side in both languages.
We don't produce abstract scores. Each finding is named, evidenced, and classified by severity — so your technical, legal, and governance teams can act on it.
Every engagement ends with a readiness classification, deployment conditions, and a prioritised remediation plan — not a score out of 100, but a structured opinion: ready for controlled pilot, conditional pilot, restricted use, or not ready for deployment.
From a rapid readiness check to a full pilot with governance built in — each service is designed to answer a practical question about deployment risk.
Benchmark Arabic–English AI performance across key dimensions. Understand whether a system is ready for pilot use, restricted use, or requires further work before any deployment decision.
Learn more →Identify inconsistency, bias, hallucination risk, and language-specific failure patterns. Each finding is named, evidenced, and classified by severity — not buried in a score.
Learn more →Assess whether an AI system handles Arabic and regional cultural context appropriately in public-facing or high-trust use cases — including GCC-specific norms, dialectal variation, and local legal framing.
Learn more →Move from assessment to a bounded, monitored pilot. We design the rollout conditions, embed the guardrails, and deliver the governance documentation needed to launch responsibly.
Learn more →Most clients begin with a single assessment and grow into ongoing assurance as their AI estate matures.
Begin with a multilingual readiness assessment to understand current performance and deployment risk.
Address identified weaknesses, compare providers, and run a bounded pilot with clear controls.
Monitor model releases, prompt changes, document updates, and cross-lingual drift through recurring assurance reviews.
Understand what bilingual deployment involves before committing. Start with a Readiness Assessment.
Readiness Assessment →Evidence its bilingual behaviour with a Bias & Reliability Audit, then keep it monitored with Continuous Assurance.
Audit & Continuous Assurance →Baseline testing, release gates, and recurring regression review for fine-tuned and proprietary systems.
Internal Model Assurance →Most AI firms focus on building assistants or integrating models. We focus on a different question: is the system actually ready to be trusted — in Arabic, and in English?
Our work is especially relevant for organizations operating across Arabic and English in sectors where trust, consistency, and accountability are non-negotiable.
We take one synthetic or non-confidential scenario, run a structured bilingual evaluation on one model, and deliver a one-page snapshot — at no cost and with no commitment. Most organisations find it sufficient to decide whether they have a problem worth solving.
Talk to us about your use case, your risk concerns, and where multilingual performance matters most.