Saudi Arabia-based Think has raised more than $8 million in pre-seed funding, marking what the company describes as the largest AI infrastructure and deeptech pre-seed round in the MENA region to date. The round was co-led by RAED Ventures and Wa’ed Ventures, with participation from Dhahran Techno Valley’s Venture Capital arm and a group of strategic angel investors.
The new capital will accelerate team expansion, manufacturing scale-up, product development, and commercial growth as Think expands across Saudi Arabia, the GCC, and selected international markets. The funding also reflects growing investor confidence that the next phase of artificial intelligence will be driven not only by better models, but by smarter infrastructure capable of deploying those models efficiently and securely.
Building the Infrastructure Behind AI
While much of the AI industry has focused on developing larger language models and more powerful GPUs, Think is solving a different problem: making AI infrastructure significantly more efficient.
Founded by Ahmed AlSharif, a former technology leader at Meta, Sony PlayStation Europe, and EA Games, together with enterprise infrastructure veteran Ammar Enaya, whose career includes leadership roles at Cisco, HPE Aruba, and Vectra AI, Think is developing an integrated hardware and software platform designed specifically for enterprise AI deployments.
Its solution combines high-density, liquid-cooled multi-GPU compute nodes with proprietary bare-metal orchestration software called ILM, enabling organizations to maximize the performance of existing GPU infrastructure while reducing operational costs and improving deployment efficiency.
“As the industry moves beyond the race for bigger models and larger data centres, a new age of efficiency is beginning,” said CEO Ahmed AlSharif. “AI infrastructure today is expensive, inefficient, and increasingly difficult to scale. Think exists to help organisations do more with the compute they already have.”
Making AI More Efficient
One of the biggest challenges facing AI adoption today is GPU utilization. Industry-wide, expensive AI hardware often operates at only 30–50% utilization, leaving significant computing capacity unused.
Think says its platform has achieved more than 90% sustained GPU utilization during production benchmark testing, dramatically improving infrastructure efficiency while reducing inference costs. According to the company, its platform can deliver a per-million-token cost nearly ten times lower than the average cost of using frontier AI models from providers such as OpenAI, Google, and Anthropic.
Importantly, these gains do not require proprietary chips or specialized inference hardware. Instead, Think’s software optimizes widely available commercial GPUs, allowing enterprises to improve performance without replacing existing infrastructure. The company also plans to support mixed-vendor GPU environments and emerging AI accelerators in future releases.
Powering Sovereign AI
Beyond efficiency, Think is positioning itself at the center of one of the AI industry’s fastest-growing trends: AI sovereignty.
Governments and regulated industries increasingly want AI systems that they fully own and control rather than relying entirely on hyperscale cloud providers. Think’s infrastructure is designed to enable enterprises, governments, and public institutions to deploy AI securely while maintaining complete ownership of their data, infrastructure, and models.
“Our customers want the benefits of AI without the spiralling costs, security concerns, and dependence associated with hyperscale cloud providers,” said co-founder Ammar Enaya. “We’re seeing strong demand from enterprises, start-ups and government organisations looking for infrastructure that delivers the performance they need, with an approach that gives them total control and ownership.”
The platform integrates hardware, orchestration software, and advanced liquid cooling into a unified infrastructure layer capable of operating across data centres, enterprise environments, laboratories, and edge deployments.
Supporting Saudi Arabia’s AI Vision
The funding comes as Saudi Arabia accelerates investments aimed at becoming one of the world’s leading AI economies. Rather than focusing solely on AI applications, the Kingdom is increasingly investing in foundational infrastructure that enables secure, scalable, and sovereign artificial intelligence.
Think is already engaged in multiple proof-of-concept projects, production deployments, and strategic partnerships across Saudi Arabia, positioning itself alongside national AI initiatives as demand for sovereign infrastructure continues to grow.
According to Wael Nafee, General Partner at RAED Ventures, “The next generation of AI leaders will be defined not only by the models they build, but by the infrastructure that makes AI practical, affordable and sovereign.”
Similarly, Anas Algahtani, CEO of Wa’ed Ventures, believes Saudi Arabia has a unique opportunity not only to adopt AI but also to build the infrastructure powering the next generation of intelligent systems.
Building a Global AI Infrastructure Company
With fresh funding secured, Think plans to accelerate commercial deployments throughout Saudi Arabia while expanding across the GCC over the next 18 months. The company also intends to continue developing ILM as a standalone orchestration platform capable of serving organizations worldwide.
As AI adoption moves beyond experimentation into enterprise-scale deployment, infrastructure is becoming one of the industry’s most important competitive battlegrounds. Rising GPU costs, increasing concerns around data sovereignty, and mounting pressure to improve both efficiency and sustainability are reshaping how organizations build AI systems.
Rather than competing to create larger models, Think is building the infrastructure that allows those models to run more efficiently. By combining advanced hardware, intelligent orchestration software, and sovereign deployment capabilities, the Saudi startup is positioning itself as a foundational layer for the next generation of enterprise AI—demonstrating that the future of artificial intelligence may depend as much on infrastructure innovation as on the models themselves.
