Neurovia AI

Neurovia AI (a Robo ai Company NASDAQ: AIIO) builds NeuroStream™, an AI-native visual data infrastructure empowering Physical AI scenarios

[Neurovia AI @NeuroviaAI_]

Less data. Greater potential for AI.

As LEAP 2026 concludes in Riyadh, @roboaiio ’s subsidiary Neurovia AI looks ahead to helping businesses address a growing challenge: managing the visual data that modern AI infrastructure depends on.

In this closing video, Neurovia AI CEO Khalifa Alshehhi shares his vision for the company and how NeuroStream™ can reduce storage and bandwidth demands through visual data compression—supporting more efficient, scalable AI infrastructure.

Thank you to everyone who visited the stand, explored our technology and shared their perspectives. We look forward to building on these conversations.

#RoboAI #NeuroviaAI #NeuroStream #LEAP2026 #AIInfrastructure

[Neurovia AI @NeuroviaAI_]

From 1.54 GB to 57.4 MB.

This live demo shows how NeuroStream™ achieved a 96.27% reduction in file size while preserving visual quality.

More efficient visual data. Less infrastructure burden.

#NeuroviaAI #NeuroStream #LEAP2026 #VisualData #AIInfrastructure @roboaiio @LEAPandInnovate

[Neurovia AI @NeuroviaAI_]

https://t.co/NqZ1PdW58q

[Neurovia AI @NeuroviaAI_]

https://t.co/JHDhOctfpo

[Neurovia AI @NeuroviaAI_]

Day 1 at @LEAPandInnovate 2026 marked a strong start for Neurovia AI, a subsidiary of @roboaiio

Throughout the day, the Neurovia AI team connected with industry leaders, partners and technology professionals through live demonstrations and in-depth discussions on the future of visual data infrastructure.

The demonstrations highlighted how NeuroStream™ is designed to optimize visual data workflows, reduce demands on storage, bandwidth and computing resources, and support more efficient and scalable AI infrastructure.

These conversations reflect https://t.co/PQYEkdqB04’s broader commitment to advancing practical AI technologies that address real-world infrastructure challenges.

Thank you to everyone who visited the Neurovia AI booth and engaged with our team. We look forward to continuing the conversations, demonstrations and exchange of ideas tomorrow.

📍 Neurovia AI | H3.D61
#RoboAI #NeuroviaAI #NeuroStream #LEAP2026 #AI #SaudiArabia

[Neurovia AI @NeuroviaAI_]

AI is advancing fast, but keeping up requires more than better models. What will shape AI's nextphase?

Read more: https://t.co/aYdnuEKSo2

يتطور الذكاء الاصطناعي بسرعة، لكن مواكبة تطوره تتطلب أكثر من نماذج أفضل. ما الذي سيشكل مرحلته المقبلة
اقرأوا المزيد
https://t.co/aYdnuEKSo2

[Neurovia AI @NeuroviaAI_]

The next era of AI starts with infrastructure. Read the full article:

https://t.co/S5rSYRca2c

مستقبل الذكاء الاصطناعي يبدأ من البنية التحتية التي ستشكل ملامح المرحلة المقبلة.

[Neurovia AI @NeuroviaAI_]

The next AI winner won't be the one with the biggest model. It'll be the one with the smartest data infrastructure. Read more: https://t.co/H0mgo8J4In

الفائز القادم في سباق الذكاء الاصطناعي لن يكون صاحب أكبر نموذج، بل صاحب البنية التحتية الأذكى للبيانات.

[Neurovia AI @NeuroviaAI_]

Trusted data powers AI. Discover how  NeuroStream supports the UAE's AI vision.

https://t.co/PHv0cxZEb0

البيانات الموثوقة أساس الذكاء الاصطناعي. تعرّفوا على دور  NeuroStream في دعم رؤية الإمارات للذكاء الاصطناعي.

[Neurovia AI @NeuroviaAI_]

Neurovia helps organizations turn growing visual data into AI-readyinfrastructure with greater efficiency, security and scale.
Read more: https://t.co/aYdnuEKSo2

تساعد نيوروفيا المؤسسات على تحويل تحديات البيانات المرئية إلى بنية تحتية جاهزة للذكاء الاصطناعي بكفاءة وأمان وقابلية للتوسع. اقرأ المزيد:
https://t.co/aYdnuEKSo2

[Neurovia AI @NeuroviaAI_]

The AI conversation is shifting from models to infrastructure. Discover what's driving the next generation of AI. Read more: https://t.co/aYdnuEKSo2

يتجه الحوار حول الذكاء الاصطناعي من النماذج إلى البنية التحتية. اكتشفوا ما يقود الجيل القادم من الذكاء الاصطناعي. اقرأ المزيد: https://t.co/aYdnuEKSo2

[Neurovia AI @NeuroviaAI_]

For years, AI innovation has been measured by the models we build. But as AI moves from experimentation to enterprise-scale deployment, the bottleneck is shifting.
Today, the real challenge isn't access to capable models. It's the ability to efficiently move, store, secure, and process the data those models depend on, especially as visual and multimodal workloads continue to grow.
What do you see as the biggest barrier to enterprise AI adoption today?
Read the full article: https://t.co/UFNJKUP8WT

على مدار السنوات الماضية، انصبّ التركيز في عالم الذكاء الاصطناعي على تطوير نماذج أكثر تقدمًا. لكن مع انتقاله من مرحلة التجارب إلى التطبيق على نطاق المؤسسات، برزتحدٍ جديد.
فاليوم، لم يعد التحدي الحقيقي يتمثل في الوصول إلى نماذج أكثر كفاءة، بل في القدرة على إدارة البيانات التي تعتمد عليها هذه النماذج، ونقلها، وتخزينها، وتأمينها، ومعالجتهابكفاءة، خاصة مع التوسع المتسارع في البيانات المرئية ومتعددة الوسائط.
برأيكم، ما التحدي الأكبر أمام توسيع نطاق تطبيقات الذكاء الاصطناعي في المؤسسات؟
اقرأ المقال كاملًا عبر الرابط:
https://t.co/UFNJKUP8WT

#نيوروفيا #البنية_التحتية_للذكاء_الاصطناعي #البنية_التحتية_للبيانات #البيانات_المرئية #الذكاء_الاصطناعي_المؤسسي

#NeuroviaAI #AIInfrastructure #DataInfrastructure #VisualData#EnterpriseAI

[Neurovia AI @NeuroviaAI_]

Why Data Infrastructure Will Become the Next AI Battleground

The defining story of the past few years has been the model. Each new release is larger, more capable, more multimodal than the last, and each one captures the headlines. But while the industry watches the models, the real constraint is quietly shifting somewhere else — to the infrastructure that has to feed them.
The next phase of AI competition will not be decided by who has the largest model. It will be decided by who can move, store, secure and process data efficiently enough to put those models to work at scale. That is a different problem, and it is one most of the market is only beginning to take seriously.
The economics are turning
Training and running advanced AI is becoming structurally more expensive. Compute attracts most of the attention, but compute is only part of the equation. As models grow and shift toward multimodal workloads, the cost of handling the underlying data — capturing it, transmitting it, storing it, and processing it repeatedly — is rising faster than many organisations anticipated.
For a research demonstration, this is manageable. For commercial deployment at national or enterprise scale, it becomes the deciding factor. The question is no longer whether a model can perform a task. It is whether the surrounding infrastructure can support that task continuously, securely and at a cost that makes sense.
Visual data is the heaviest load in the system
Nowhere is this pressure more visible than in visual and multimodal data. High-resolution video, large-scale camera networks, industrial sensors and autonomous systems generate continuous, high-volume data streams — and visual data is structurally the heaviest data type AI has to contend with.
The volume is not growing in a straight line. As smart cities, public safety networks, mobility platforms and industrial monitoring expand, the data they produce compounds. Most existing data centre architectures were never designed for this kind of sustained visual workload. Traditional pipelines require data to be transmitted, stored and decompressed at full size before it can be used — adding latency, complexity and cost at every step.
The real shortage is not models. It is data infrastructure.
This is the point the market is missing. The AI era does not have a model shortage. Capable models are increasingly accessible, and that accessibility is only widening. What is genuinely scarce is the infrastructure required to make AI work in the real world — the ability to capture, compress, move, protect and serve data at scale without unsustainable cost, energy or security overhead.
In other words, the bottleneck has moved down the stack. The organisations that lead the next wave of AI will be the ones that solved the data problem, not just the model problem.
This is where Neurovia operates
Neurovia AI is built for exactly this layer of the problem. Our focus is the visual data infrastructure that the AI era depends on — reducing the data burden at the source so that AI systems can run efficiently, securely and at scale.
NeuroStream, our core compression technology, has achieved up to a 96.37% reduction in high-resolution video data volume in testing, while maintaining virtually lossless visual quality. The implication of that figure is significant. A reduction of this magnitude directly lowers storage requirements, bandwidth consumption and energy use, and it reduces the repeated decompression and data movement that drive cost and latency in conventional workflows.
Equally important, NeuroStream is designed for AI, not only for storage. It preserves the visual integrity, timestamp continuity and structural usability that downstream AI recognition, search and analytics depend on. The data does not just get smaller — it stays useful. And by reducing the volume of data that has to be transmitted and stored, it also reduces data exposure, strengthening security and confidentiality.
The battleground is set
The companies that define the next era of AI will not necessarily be those with the most parameters. They will be those who built the infrastructure to make AI viable in production — efficient, secure and sustainable at scale.
That is the battleground. And it is the one Neurovia AI is building for.

Neurovia AI develops visual data infrastructure for the AI era, enabling government and enterprise organisations to manage, process and scale visual and multimodal AI data more efficiently and securely.

[Neurovia AI @NeuroviaAI_]

Why Data Infrastructure Will Become the Next AI Battleground

The defining story of the past few years has been the model. Each new release is larger, more capable, more multimodal than the last, and each one captures the headlines. But while the industry watches the models, the real constraint is quietly shifting somewhere else — to the infrastructure that has to feed them.
The next phase of AI competition will not be decided by who has the largest model. It will be decided by who can move, store, secure and process data efficiently enough to put those models to work at scale. That is a different problem, and it is one most of the market is only beginning to take seriously.
The economics are turning
Training and running advanced AI is becoming structurally more expensive. Compute attracts most of the attention, but compute is only part of the equation. As models grow and shift toward multimodal workloads, the cost of handling the underlying data — capturing it, transmitting it, storing it, and processing it repeatedly — is rising faster than many organisations anticipated.
For a research demonstration, this is manageable. For commercial deployment at national or enterprise scale, it becomes the deciding factor. The question is no longer whether a model can perform a task. It is whether the surrounding infrastructure can support that task continuously, securely and at a cost that makes sense.
Visual data is the heaviest load in the system
Nowhere is this pressure more visible than in visual and multimodal data. High-resolution video, large-scale camera networks, industrial sensors and autonomous systems generate continuous, high-volume data streams — and visual data is structurally the heaviest data type AI has to contend with.
The volume is not growing in a straight line. As smart cities, public safety networks, mobility platforms and industrial monitoring expand, the data they produce compounds. Most existing data centre architectures were never designed for this kind of sustained visual workload. Traditional pipelines require data to be transmitted, stored and decompressed at full size before it can be used — adding latency, complexity and cost at every step.
The real shortage is not models. It is data infrastructure.
This is the point the market is missing. The AI era does not have a model shortage. Capable models are increasingly accessible, and that accessibility is only widening. What is genuinely scarce is the infrastructure required to make AI work in the real world — the ability to capture, compress, move, protect and serve data at scale without unsustainable cost, energy or security overhead.
In other words, the bottleneck has moved down the stack. The organisations that lead the next wave of AI will be the ones that solved the data problem, not just the model problem.
This is where Neurovia operates
Neurovia AI is built for exactly this layer of the problem. Our focus is the visual data infrastructure that the AI era depends on — reducing the data burden at the source so that AI systems can run efficiently, securely and at scale.
NeuroStream, our core compression technology, has achieved up to a 96.37% reduction in high-resolution video data volume in testing, while maintaining virtually lossless visual quality. The implication of that figure is significant. A reduction of this magnitude directly lowers storage requirements, bandwidth consumption and energy use, and it reduces the repeated decompression and data movement that drive cost and latency in conventional workflows.
Equally important, NeuroStream is designed for AI, not only for storage. It preserves the visual integrity, timestamp continuity and structural usability that downstream AI recognition, search and analytics depend on. The data does not just get smaller — it stays useful. And by reducing the volume of data that has to be transmitted and stored, it also reduces data exposure, strengthening security and confidentiality.
The battleground is set
The companies that define the next era of AI will not necessarily be those with the most parameters. They will be those who built the infrastructure to make AI viable in production — efficient, secure and sustainable at scale.
That is the battleground. And it is the one Neurovia AI is building for.

Neurovia AI develops visual data infrastructure for the AI era, enabling government and enterprise organisations to manage, process and scale visual and multimodal AI data more efficiently and securely.

[Robo.ai Inc. @roboaiio]

https://t.co/2xba2TMKU4

[Neurovia AI @NeuroviaAI_]

Neurovia AI showcased NeuroStream™️ and its vision for Physical AI infrastructure at the UAE Data Center Infrastructure & Cloud Summit 2026.

استعرضت Neurovia AI منصة NeuroStream™️ ورؤيتها للبنية التحتية للذكاء الاصطناعي الفيزيائي.

#NeuroviaAI #AIInfrastructure #PhysicalAI

[Neurovia AI @NeuroviaAI_]

نفخر بالترحيب براشد الغفلي رئيساً للعمليات لدى Neurovia AI، حيث ستسهم خبرته في تعزيز البنية التحتية لمستقبل الذكاء الاصطناعي الفيزيائي.

Proud to welcome Rashed Aleghfeli as COO of Neurovia AI, helping strengthen the infrastructure powering the future of Physical AI.