Mohammad Abu Sheikh is transforming the AI landscape within the MENA region, driving a shift from passive consumption to sovereign innovation. As CEO of CNTXT AI and founding father of a $10 million AI fund, he has led three successful exits and secured over a billion dollars in funding. His work is laying the inspiration for an AI ecosystem rooted in language, culture, and data sovereignty.
CNTXT AI is a digital transformation company that gives cloud infrastructure, industrial software, and robotics solutions to assist organizations modernize operations and unlock data-driven insights across the Middle East and North Africa.
What inspired you to begin CNTXT AI, and the way did your vision for sovereign AI within the Arabic-speaking world begin?
We saw the abundance of underutilized data on this a part of the world. A variety of problems in scaling AI got here from the shortage of information readiness — which eventually meant a scarcity of AI readiness. That’s why we began CNTXT AI.
Initially, we were solving the identical problems we faced while constructing LocAI…We saw these challenges firsthand working with AI71, TII and G42 (IIAI). As we helped these entities solve those problems, the vision got clearer and the business just kept growing.
You’ve played a key role in constructing the most important Arabic digital library for AI training. What were a few of the biggest challenges in doing so, and the way did you overcome them?
Quality was certainly one of the most important challenges. One other was the limited availability of high-quality Arabic data online: Arabic is seriously underrepresented. Only a small portion of Arabic-language content has been digitized, and just 3–5% of all online content is in Arabic. That’s almost nothing. We overcame that problem by deploying data labelers, annotators, and data scientists to digitize, create, and curate the info ourselves.
CNTXT AI operates on the intersection of culture and computation. How do you balance cutting-edge AI innovation with the goal of constructing culturally relevant solutions for the MENA region?
We construct culturally grounded models from the bottom up. From infrastructure to final product, culture is embedded from the very starting — it’s not something we add later. We design, innovate, and construct with specific cultures, dialects, and wishes in mind from day one. Arabic is one language, nevertheless it carries many dialects and cultural contexts across the region, so we construct local products for local countries. And we do this by working with local annotators, people on the bottom, in their very own countries.
You’ve got also co-founded LocAI and lead the SMPL AI Fund. How do these ventures complement the mission of CNTXT AI?
LocAI is the appliance layer — the part people actually interact with. It sits right on top of the info and infrastructure built by CNTXT AI. That’s what made it successful: it transforms AI foundations provided by CNTXT AI into real-world solutions people can use.
SMPL AI, however, is about giving back to the community. It focuses on investing in early-stage startups and helping construct the regional AI ecosystem. We share the tools and lessons we’ve learned from constructing AI ourselves, so founders can grow faster and avoid common pitfalls.
Munsit has been called probably the most accurate Arabic speech recognition model on the earth. What drove the event of this model, and why now?
What drove the event of this model was easy: the necessity.
We at all times construct out of necessity. We checked out the market and saw the landscape was ripe — government agencies and personal clients were all asking for an answer like this.
The present models just weren’t as much as the duty. Most are built on English tech after which adapted. They aren’t designed for Arabic from the bottom up, and definitely not for the precise problems we’re solving.
So we decided to construct our own. It’s Arabic first — by design.
The research behind Munsit introduces a weakly supervised learning approach. Are you able to explain what which means and why it was essential for training Arabic ASR at scale?
Annotation is dear. So we had to maneuver beyond traditional methods that rely upon large amounts of manual transcription. Weakly supervised learning helped us scale without having to label every audio file by hand — which is particularly necessary for Arabic, a language with limited data and many various dialects.
As an alternative of using professionally transcribed audio, we began with 30,000 hours of unlabeled Arabic speech. We built an annotation pipeline that generates, filters and cleans the very best ones using automated checks. This gave us a high-quality 15,000-hour dataset — all without human transcription.
This approach made it possible to coach our model from scratch, capturing the richness of spoken Arabic across real-life situations, quickly and cost-effectively. Without this method, constructing an Arabic ASR system at this scale would have taken years and thousands and thousands in manual effort.
Munsit outperformed models from OpenAI, Microsoft, and Meta across multiple benchmarks. What does this achievement say in regards to the way forward for Arabic AI innovation?
The longer term of Arabic AI is in our hands; and that’s exactly what this achievement proves. We are able to not afford to depend on technologies we don’t own or rely upon third parties who don’t prioritize our region.
Munsit shows that we are able to construct world-class AI, from the region, for the region — using local talent to resolve local problems. It’s a transparent signal that the subsequent wave of Arabic AI innovation will come from inside.
How do you see Munsit evolving in future versions, and what are the subsequent frontiers for Arabic voice AI at CNTXT?
You’ll just need to wait and see. What I can say is that we’ve a fresh, latest suite of Arabic-first AI solutions on the way in which — all powered by Munsit and other models we’re currently constructing at CNTXT AI. That is only the start.
You frequently speak in regards to the importance of “sovereign AI.” What does that term mean to you, and why is it critical for the Gulf and broader MENA region?
To me, sovereign AI means having full ownership and control over the info, infrastructure, and models that shape our future. It’s critical because we’d like to own our own fate, and that starts with data.
Data sovereignty is every little thing. Data is precious, and we’d like to make sure that it stays in our hands.
We are able to’t afford at hand over our future and sit idle while others construct the technology for us. The longer term of AI on this region will come from this region. That’s exactly what we’re working toward.
How do you see CNTXT AI shaping the AI ecosystem within the Middle East over the subsequent five years?
By enabling true AI readiness. We go in, understand what corporations and governments need, construct the info and AI strategies, after which help them construct, test, deploy and scale.
That’s why we’ve built CNTXT AI to assist organizations clean, structure, and activate their data. Because that’s where real AI transformation begins.
Out of your vantage point as each an entrepreneur and investor, what advice would you give to other founders constructing AI startups in emerging markets?
Start now. Move quickly. Fail fast, learn faster, and keep iterating.
Most significantly, construct for real problems. Stay near the bottom — take heed to users, not only the hype. In emerging markets, relevance and flexibility are key.
