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Orca AI To Save Lives With Its Collision Avoidance System For Ships
Orca AI combines data from sensors with artificial intelligence to provide real-time insights that reflect the changing conditions at sea.
The recent Suez Canal blockage, which lasted for six days after a 400-metre-long (1,300 ft) container ship ended up wedged across the waterway due to the combination of strong winds and human errors, showed the whole world just how easily can major shipping accidents happen and how severe and long-lasting their impact can be. Unfortunately, the Suez Canal blockage was just one of several thousand marine incidents that occur annually. Now, one Tel Aviv-based company has successfully raised €10.8 million in a Series A round to work on its ship navigation and collision avoidance system, Orca AI.
“The maritime industry has come leaps and bounds in recent years, but is still far behind aviation with technological innovations. Ships deal with increasingly congested waterways, severe weather and low-visibility conditions creating difficult navigation experiences with often expensive cargo,” says Orca AI CEO and co-founder Yarden Gross.
Orca AI combines data from sensors with artificial intelligence to provide real-time insights that reflect the changing conditions at sea. The system can be used for individual ships to deliver predictions and alerts on hazards, but it can also supply fleet managers with insights on the risk behavior and patterns of the entire fleet.
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“Utilizing onboard navigation sensors and high-resolution cameras with proprietary AI algorithms, the technology is able to provide valuable insight such as alerting the crew on dangerous targets, prioritize risk in real-time and sort out complex navigation situations,” explains Dor Raviv, Co-Founder & CTO of Orca AI.
Other tech companies are also trying to make the shipping industry safer and less prone to costly accidents. For example, the Saudi Arabian Oil Company is using AI technology to monitor its maritime fleet, while Stockholm-based X Shore is exploring an auto-docking solution that could greatly streamline what’s arguably the most dangerous part of getting a massive cargo ship from point A to point B.
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NVIDIA Puts GPT-5.5 Codex In Hands Of 10,000 Staff
The chipmaker has significantly expanded OpenAI’s latest model across teams from engineering to HR under tight internal controls.
NVIDIA has started rolling out OpenAI’s GPT-5.5 model through the Codex coding agent to more than 10,000 employees, extending the tool well beyond software teams and into core business functions.
The deployment covers engineering, product, legal, marketing, finance, sales, HR, operations and developer programs. Staff are using Codex for coding, internal research and routine knowledge work as companies test whether AI agents can move from demos to daily use.
GPT-5.5 is running on NVIDIA’s GB200 NVL72 rack-scale systems, linking OpenAI’s newest model directly to the chipmaker’s latest infrastructure push. NVIDIA said the systems cut cost per million tokens by 35 times and raise token output per second per megawatt by 50 times versus earlier generations.
Inside the company, it says the effects are immediate. Debugging work that once took days is being finished in hours and experiments across large codebases that used to stretch over weeks are now handled overnight. Teams are also building features from natural-language prompts with fewer failed runs.
In a company-wide note urging staff to adopt the tool, CEO Jensen Huang wrote: “Let’s jump to lightspeed. Welcome to the age of AI.”
Security remains central to the rollout. Codex can connect through Secure Shell to approved cloud virtual machines, allowing agents to work with company data without moving it outside approved environments. NVIDIA said it assigned cloud VMs to employees so agents run in isolated sandboxes with full audit trails.
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The company added that the setup uses a zero-data-retention policy. Access to production systems is read-only through command-line tools and internal automation layers.
The move also highlights NVIDIA’s long relationship with OpenAI. NVIDIA said the partnership began in 2016, when Huang personally delivered the first DGX-1 AI supercomputer to OpenAI’s San Francisco office.
The two companies have since worked across hardware and model deployment. NVIDIA also said OpenAI plans to deploy more than 10 gigawatts of NVIDIA systems for future AI infrastructure.
For Gulf markets pouring money into sovereign AI and enterprise automation, the signal is clear: internal AI agents are moving from pilot phase to standard tooling.
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