Deep Tech

What the Hesai–Keeta Drone Partnership Reveals About Scaling Urban Drone Delivery

Sensing technology is facilitating the transition of drone delivery services from trial phases to regular daily operations.

Updated

January 23, 2026 10:41 AM

A quadcopter drone with package attached. PHOTO: FREEPIK

A new partnership between Hesai Technology, a LiDAR solutions company and Keeta Drone, an urban delivery platform under Meituan, offers a glimpse into how drone delivery is moving from experimentation to real-world scale.

Under the collaboration, Hesai will supply solid-state LiDAR sensors for Keeta’s next-generation delivery drones. The goal is to make everyday drone deliveries more reliable as they move from trials to routine operations. Keeta Drone operates in a challenging space—low-altitude urban airspace. Its drones deliver food, medicine and emergency supplies across cities such as Beijing, Shanghai, Hong Kong and Dubai. With more than 740,000 deliveries completed across 65 routes, the company has discontinued testing the concept. It is scaling it. For that scale to work, drones must be able to navigate crowded environments filled with buildings, trees, power lines and unpredictable conditions. This is where Hesai’s technology comes in.

Hesai’s solid-state LiDAR is integrated into Keeta's latest long-range delivery drones. LiDAR stands for Light Detection and Ranging. In simple terms, it is a sensing technology that helps machines understand their surroundings by sending out laser pulses and measuring how they bounce back. Unlike GPS, LiDAR does not rely solely on satellites to determine position. Instead, it gives drones a direct sense of their surroundings, helping them spot small but critical obstacles like wires or tree branches.

In a recent demonstration, Keeta Drone completed a nighttime flight using LiDAR-based navigation alone without relying on cameras or satellite positioning. This shows how the technology can support stable operations even when visibility is poor or GPS signals are limited.

The LiDAR system used in these drones is Hesai’s second-generation solid-state model known as FTX. Compared with earlier versions, the sensor offers higher resolution while being smaller and lighter—important considerations for airborne systems where weight and space are limited. The updated design also reduces integration complexity, making it easier to incorporate into commercial drone platforms. Large-scale production of the sensor is expected to begin in 2026.

From Hesai’s perspective, delivery drones are one of several forms robots are expected to take in the coming decades. Industry forecasts suggest robots will increasingly appear in many roles from industrial systems to service applications, with drones becoming a familiar part of urban infrastructure rather than a novelty.

For Keeta Drone, this improves safety and reliability. And for the broader industry, it signals that drone logistics is entering a more mature phase—one defined less by experimentation and more by dependable execution. Taken together, the partnership highlights a practical evolution in drone delivery.

As cities grow more complex, the question is no longer whether drones can fly but whether they can do so reliably, safely and at scale. At its core, this partnership is not about drones or sensors as products. It is about what it takes to make a complex system work quietly in real cities. As drone delivery moves out of pilot zones and into everyday use, reliability matters more than novelty.

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Artificial Intelligence

As AI Music Copyright Battles Grow, Companies Are Turning to Licensed Training Data

Sonilo and Shutterstock are betting that licensed training data could define the future of AI music.

Updated

May 13, 2026 3:39 PM

A human operating a digital turntable. PHOTO: UNSPLASH

As copyright disputes continue to grow around AI-generated music, Sonilo, the world’s first professionally licensed video-to-music AI platform, has partnered with Shutterstock to train its models on licensed music catalogs.

The agreement gives Sonilo access to Shutterstock’s music library for AI model training. According to the companies, it is Shutterstock’s first partnership with a video-to-music AI platform and the timing is significant. AI music companies are facing growing pressure over how their systems are trained. Artists and record labels have increasingly challenged the use of copyrighted music in AI datasets, especially when licensing agreements or compensation structures are unclear.

That tension has created a divide across the industry. Some companies have continued building models around scraped or disputed data. Others are trying to position licensing as part of the product itself.

Sonilo falls into the second group. The company says its models are trained only on licensed material where artists and rights holders have agreed to participate and receive compensation. The Shutterstock partnership strengthens that position while giving Sonilo access to a larger pool of commercially cleared music.

The collaboration also points to a broader change happening inside generative AI. As AI tools move into commercial production, companies are being pushed to show not just what their models can generate, but also where their training data comes from.

Sonilo’s platform is built around video rather than text prompts. The system analyses footage directly, studies pacing and emotional tone, then generates an original soundtrack to match the content. The company says this removes the need for manual music searches, syncing or editing workflows. The generated tracks are cleared for commercial use across social media, branded content and broadcast production.

Shawn Song, CEO of Sonilo, said: "Music has always been the last unsolved layer of video creation, and video has always carried its own soundtrack. We built Sonilo to hear it and compose from it, without a single text prompt. But how we build matters as much as what we build. While others have chosen to take artists' work without permission and charge creators for the privilege, we've chosen a different path—one where artists are compensated from day one. Partnering with Shutterstock reflects that standard. Every model we train meets a bar the music industry can stand behind, because the most innovative AI platforms don't have to come at the expense of the artists who make all of these possible."

For Shutterstock, the deal expands the company’s growing role in generative AI infrastructure. The company has increasingly focused on licensing content for AI systems across images, video and music.

Jessica April, Vice President of Data Licensing & AI Services at Shutterstock, said: "AI innovation depends on access to high-quality, rights-cleared content and trusted licensing partnerships. Sonilo's approach reflects the growing demand for responsibly sourced training data and commercially safe AI workflows. We're pleased to support companies building generative AI products with licensed content and scalable data solutions that help accelerate innovation while respecting creators and rights holders."

The partnership also comes as Sonilo expands into creator and developer ecosystems. Earlier this month, the company launched as a native node inside ComfyUI, an open-source AI workflow platform used by millions of creators. Sonilo also offers API access for integration into creator tools, video platforms, game engines and other AI systems.

As AI-generated music becomes more common across advertising, creator platforms and digital media, the industry’s focus is shifting beyond generation alone. Questions around licensing, ownership and compensation are increasingly shaping how AI music companies position themselves and build trust with creators.