A wearable ring, conversational AI and US$23M in funding. Sandbar wants to rethink how we interact with technology
Updated
March 12, 2026 5:59 PM
Sandbar's Stream ring. PHOTO: SANDBAR
Sandbar, a New York–based interface startup, has raised US$23 million in Series A funding to develop a wearable device that lets people interact with artificial intelligence via voice rather than screens.
Adjacent and Kindred Ventures led the round; both venture firms focused on early-stage technology startups. The investment brings Sandbar’s total funding to us$36 million. Earlier backing included a US$10 million seed round led by True Ventures, a venture capital firm, as well as a US$3 million pre-seed round supported by Upfront Ventures, a venture firm and Betaworks, a startup studio and investment firm.
Sandbar was founded by Mina Fahmi and Kirak Hong, who previously worked together at CTRL-labs, a neural interface startup acquired by Meta in 2019. Their earlier work explored how computers could respond more directly to human intent — an idea that continues to shape Sandbar’s approach to AI interfaces.
The new funding will help the company expand its team across machine learning, interaction design and software engineering as it prepares to launch its first product. That product, called Stream, combines a wearable ring with a conversational AI interface. The system allows users to speak to an AI assistant without unlocking a phone or opening an app.
The concept is simple. Instead of typing into a screen, users press a button on the ring and talk. The system can capture notes, organize ideas, retrieve information from the web or trigger actions through connected applications.
The ring includes a microphone, a touchpad and subtle haptic feedback. These elements allow the device to respond through gentle vibrations rather than visual alerts. According to the company, the ring only listens when the user presses the button — a design meant to address common concerns around always-on microphones.
That design reflects a larger shift Sandbar believes is underway. As AI assistants become more capable, many startups are experimenting with new ways to interact with them. The focus is moving away from screens and keyboards toward interfaces that feel more natural and immediate.
Stream uses multiple AI models working together to process requests, search the web and structure information in real time. The company says users remain in control of their data and can choose whether to share information with other apps.
Sandbar is also developing a feature called Inner Voice, which responds using a voice customized to the user. The feature will debut during a closed beta planned for this spring, giving the company time to refine how the software behaves in everyday use.
The startup currently employs a team of 15 people. Many have worked on well-known consumer devices including the iPhone, Fitbit, Kindle and Vision Pro. Recent hires include Sam Bowen, formerly of Amazon and Fitbit, who joined as vice president of hardware and Brooke Travis, previously at Equinox, Dior and Gap, who now leads marketing.
Sandbar plans to begin shipping Stream in summer 2026 after completing early testing. As artificial intelligence tools become more integrated into daily life, the company is betting that the next shift in computing will not come from another app — but from new ways for people to interact with AI itself.
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A new safety layer aims to help robots sense people in real time without slowing production
Updated
February 13, 2026 10:44 AM
An industrial robot in a factory. PHOTO: UNSPLASH
Algorized has raised US$13 million in a Series A round to advance its AI-powered safety and sensing technology for factories and warehouses. The California- and Switzerland-based robotics startup says the funding will help expand a system designed to transform how robots interact with people. The round was led by Run Ventures, with participation from the Amazon Industrial Innovation Fund and Acrobator Ventures, alongside continued backing from existing investors.
At its core, Algorized is building what it calls an intelligence layer for “physical AI” — industrial robots and autonomous machines that function in real-world settings such as factories and warehouses. While generative AI has transformed software and digital workflows, bringing AI into physical environments presents a different challenge. In these settings, machines must not only complete tasks efficiently but also move safely around human workers.
This is where a clear gap exists. Today, most industrial robots rely on camera-based monitoring systems or predefined safety zones. For instance, when a worker steps into a marked area near a robotic arm, the system is programmed to slow down or stop the machine completely. This approach reduces the risk of accidents. However, it also means production lines can pause frequently, even when there is no immediate danger. In high-speed manufacturing environments, those repeated slowdowns can add up to significant productivity losses.
Algorized’s technology is designed to reduce that trade-off between safety and efficiency. Instead of relying solely on cameras, the company utilizes wireless signals — including Ultra-Wideband (UWB), mmWave, and Wi-Fi — to detect movement and human presence. By analysing small changes in these radio signals, the system can detect motion and breathing patterns in a space. This helps machines determine where people are and how they are moving, even in conditions where cameras may struggle, such as poor lighting, dust or visual obstruction.
Importantly, this data is processed locally at the facility itself — not sent to a remote cloud server for analysis. In practical terms, this means decisions are made on-site, within milliseconds. Reducing this delay, or latency, allows robots to adjust their movements immediately instead of defaulting to a full stop. The aim is to create machines that can respond smoothly and continuously, rather than reacting in a binary stop-or-go manner.
With the new funding, Algorized plans to scale commercial deployments of its platform, known as the Predictive Safety Engine. The company will also invest in refining its intent-recognition models, which are designed to anticipate how humans are likely to move within a workspace. In parallel, it intends to expand its engineering and support teams across Europe and the United States. These efforts build on earlier public demonstrations and ongoing collaborations with manufacturing partners, particularly in the automotive and industrial sectors.
For investors, the appeal goes beyond safety compliance. As factories become more automated, even small improvements in uptime and workflow continuity can translate into meaningful financial gains. Because Algorized’s system works with existing wireless infrastructure, manufacturers may be able to upgrade machine awareness without overhauling their entire hardware setup.
More broadly, the company is addressing a structural limitation in industrial automation. Robotics has advanced rapidly in precision and power, yet human-robot collaboration is still governed by rigid safety systems that prioritise stopping over adapting. By combining wireless sensing with edge-based AI models, Algorized is attempting to give machines a more continuous awareness of their surroundings from the start.