Tarjama CEO Nour Al Hassan Unveils Next-Gen Arabic LLMs at MenaML26 in UAE and Jordan
Nour Al Hassan, founder and CEO of Tarjama and Arabic AI, took the stage at MenaML26 to reveal the next generation of Arabic large lang
Tarjama CEO Nour Al Hassan Unveils Next-Gen Arabic LLMs at MenaML26 in UAE and Jordan
Nour Al Hassan, founder and CEO of Tarjama and Arabic AI, took the stage at MenaML26 to reveal the next generation of Arabic large language models. These models promise superior handling of dialects and cultural nuances that global systems overlook. Her presentation underscores Jordan and UAE-based innovation filling gaps in enterprise AI for Arabic markets.
From Amman roots to Gulf expansion, Al Hassan's ventures lead Arabic language technology. At the event, she highlighted advancements tailored for businesses and governments.
From Amman Startup to Arabic AI Powerhouse
Tarjama began in Amman, Jordan, in 2008, when Nour Al Hassan spotted a need for high-quality Arabic content.1 The company grew into a leader in translation and linguistic services. It now operates across the UAE, Saudi Arabia, and the US.
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Al Hassan expanded Tarjama methodically, adding offices in key markets. By 2025, she launched Arabic AI as the world's leading enterprise Arabic AI firm.2 This move built on years of data accumulation for custom models.
We built Tarjama into a global leader in language solutions and launched Arabic AI as the premier enterprise Arabic artificial intelligence company.
Chief Technology Officer Iyad Ahmad drives the tech at Arabic AI. With over 25 years of experience, he leads development of models outperforming global benchmarks on Arabic tasks.3 Their work targets Fortune 500 clients in Arabic-speaking regions.
At MenaML26, Al Hassan's keynote focused on next-generation Arabic LLMs.4 These models handle enterprise needs like legal and medical applications with high accuracy. Arabic AI's Pronoia LLM sets benchmarks above GPT-4 on Arabic tasks.
The event spotlighted how such models address dialect diversity across MENA. Global LLMs often falter here, lacking cultural depth. Jordan and UAE firms like Tarjama bridge this divide.
Arabic AI provides full-stack solutions, fine-tuning LLMs for Arabic from 32 billion to 200 billion parameters.
Ahmad's contributions ensure robust performance. Businesses gain tools for hyper-local engagement. This positions Arabic AI as a MENA frontrunner.
Closing the Gap: Dialects and Localisation in MENA AI
Gulf states like the UAE and Saudi Arabia pour funds into AI infrastructure. Yet enterprise solutions for Arabic dialects lag. Tarjama and Arabic AI tackle this with culturally attuned models.
Jordan's innovation hub status aids such efforts. Tarjama's expansion to UAE and Saudi Arabia enables testing in diverse markets. Global LLMs struggle with nuances like Gulf dialects or Levantine variations.
Key challenges include data scarcity for dialects. Arabic AI counters this through proprietary datasets. Results show gains in accuracy for business use cases.
Challenge
Global LLMs
Arabic AI Solution
Dialect Handling
Poor recognition
Custom fine-tuning
Cultural Context
Generic output
Localised training data
Enterprise Scale
High costs
Sovereign full-stack
Accuracy Benchmarks
Below par on Arabic
Outperforms GPT-4
Recognition and Leadership in Arabic Tech
Nour Al Hassan earns praise as a top tech leader by Forbes Middle East.5 She also secured the EY Entrepreneur of the Year award. These honours reflect her impact on Arabic AI.
Tarjama established itself as an industry leader early.6 Al Hassan's vision evolved it from human translation to AI-driven tools. Arabic AI now serves governments and enterprises.
Her commitment extends to talent development, especially for women in tech. This aligns with MENA's push for inclusive growth. Iyad Ahmad complements with technical prowess.
The AI in Arabia View: We see Arabic AI's launch as a defining moment for MENA enterprise tech. Global giants neglect Arabic dialects, leaving billions underserved. Pioneers like Nour Al Hassan from Jordan and UAE deliver precision that powers real economic gains. Our position: Gulf investments must partner with such locals to avoid wasteful duplication. This model scales Arabic AI across sectors, from Egypt Has Launched the KARNAK Arabic LLM, and Cairo Has Just Become North Africa to oil deals like ADNOC and SLB Have Struck a $1.2 Billion AI Deal for Upstream Modelling, and Ira. True regional leadership demands dialect mastery now.
MENA Context: Gulf Ambitions Meet Local Innovation
Risks involve data privacy in regulated markets. Over-reliance on proprietary data could limit openness. Competition from Jais or Falcon grows.
Sustained investment in talent remains key. Al Hassan's model offers a blueprint. MENA must balance speed with accuracy.
By The Numbers
2008, bTarjama founded in Amman, Jordan, in 2008, bootstrapped into MENA's largest translation provider before expanding to UAE, Saudi Arabia, and US markets.1
2025Arabic AI launched in 2025 as the world's leading enterprise Arabic AI company, delivering tools to businesses, governments, and Fortune 500 firms.2
5 Nour Al Hassan recognised by Forbes Middle East as a top tech leader and EY Entrepreneur of the Year for pioneering Arabic language solutions.5
25+ Iyad Ahmad, CTO at Arabic AI, leads innovation with 25+ years experience, developing LLMs that outperform GPT-4 on Arabic benchmarks.3
2008Tarjama evolved from human translation in 2008 to AI-driven ecosystem by 2016, powering Pronoia for legal, medical, and business use.4
26 k MenaML26 keynote unveiled next-gen Arabic LLMs, addressing dialect gaps global models ignore in diverse MENA markets.
4,Arabic AI's Pronoia agentic platform emerged from stealth, benchmarking above GPT-4, Deepseek, and Cohere on Arabic tasks.
16 Over 16 years, Tarjama built data foundations enabling scalable Arabic AI for hyper-local enterprise applications across Gulf states.
AI Terms in This Article6 terms
LLM
A large language model, meaning software trained on massive text data to generate human-like text.
agentic
AI that can independently take actions and make decisions to complete tasks.
fine-tuning
Training a pre-built AI model further on specific data to improve its performance on particular tasks.
parameters
The internal settings an AI model learns during training. More parameters generally means more capable.
AI-driven
Primarily guided or operated by artificial intelligence.
next-generation
The upcoming, improved version.
Frequently Asked Questions
What makes Arabic AI's LLMs superior for MENA enterprises?
Arabic AI's models excel in dialect handling and cultural localisation, outperforming globals like GPT-4 on Arabic tasks. Built on Tarjama's vast datasets, they suit sectors from healthcare to oil. This enables precise engagement in UAE, Saudi, and Jordan markets, where generic tools fail.
How did Tarjama evolve into Arabic AI?
Tarjama, founded in 2008 in Jordan, grew from translation services to AI tech. Expansion hit UAE and Saudi Arabia, leading to Arabic AI's 2025 launch. Nour Al Hassan's vision shifted focus to enterprise-grade Arabic LLMs like Pronoia.
Who leads the technical efforts at Arabic AI?
Iyad Ahmad, CTO, brings 25+ years to spearhead LLM development. His team fine-tunes models up to 200 billion parameters for Arabic accuracy. This supports applications in business and government across MENA.
What risks face Arabic LLM development in MENA?
Data scarcity for dialects and privacy regulations pose challenges. Competition from Gulf-funded models like Jais adds pressure. Success hinges on local talent and partnerships to maintain edge over globals.
