How ECOPEACE uses autonomous robots and data to monitor and maintain urban water bodies.
Updated
January 8, 2026 6:27 PM
A school of fish swimming among debris and waste. PHOTO: UNSPLASH
South Korea–based water technology company ECOPEACE is working on a practical challenge many cities face today: keeping urban water bodies clean as pollution and algae growth become more frequent. Rather than relying on periodic cleanup drives, the company focuses on systems that can monitor and manage water conditions on an ongoing basis.
At the core of ECOPEACE’s work are autonomous water-cleanup robots known as ECOBOT. These machines operate directly on lakes, reservoirs and rivers, removing algae and surface waste while also collecting information about water quality. The idea is to combine cleaning with constant observation so changes in water conditions do not go unnoticed.
Alongside the robots, ECOPEACE uses a filtration and treatment system designed to process polluted water continuously. This system filters out contaminants using fine metal filters and treats the water using electrical processes. It also cleans itself automatically, which allows it to run for long periods without frequent manual maintenance.
The role of AI in this setup is largely about decision-making rather than direct control. Sensors placed across the water body collect data such as pollution levels and water quality indicators. The software then analyses this data to spot early signs of issues like algae growth. Based on these patterns, the system adjusts how the robots and filtration units operate, such as changing treatment intensity or water flow. In simple terms, the technology helps the system respond sooner instead of waiting for visible problems to appear.
ECOPEACE has already deployed these systems across several reservoirs, rivers and urban waterways in South Korea. Those projects have helped refine how the robots, sensors and software work together in real environments rather than controlled test sites.
Building on that experience, the company has begun expanding beyond Korea. It is currently running pilot and proof-of-concept projects in Singapore and the United Arab Emirates. These deployments are testing how the technology performs in dense urban settings where waterways are closely linked to public health, infrastructure and daily city life.
Both regions have invested heavily in smart city initiatives and water management, making them suitable test beds for automated monitoring and cleanup systems. The pilots focus on algae control, surface cleaning and real-time tracking of water quality rather than large-scale rollout.
As cities continue to grow and climate-related pressures on water systems increase, managing waterways is becoming less about occasional intervention and more about continuous oversight. ECOPEACE’s approach reflects that shift by using automation and data to address problems early and reduce the need for reactive cleanup later.
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Why investors are backing Applied Brain Research’s on-device voice AI approach.
Updated
January 14, 2026 1:38 PM
Plastic model of a human's brain. PHOTO: UNSPLASH
Applied Brain Research (ABR), a Canada-based startup, has closed its seed funding round to advance its work in “on-device voice AI”. The round was led by Two Small Fish Ventures, with its general partner Eva Lau joining ABR’s board, reflecting investor confidence in the company’s technical direction and market focus.
The round was oversubscribed, meaning more investors wanted to participate than the company had planned for. That response reflects growing interest in technologies that reduce reliance on cloud-based AI systems.
ABR is focused on a clear problem in voice-enabled products today. Most voice features depend on cloud servers to process speech, which can cause delays, increase costs, raise privacy concerns and limit performance on devices with small batteries or limited computing power.
ABR’s approach is built around keeping voice AI fully on-device. Instead of relying on cloud connectivity, its technology allows devices to process speech locally, enabling faster responses and more predictable performance while reducing data exposure.
Central to this approach is the company’s TSP1 chip, a processor designed specifically for handling time-based data such as speech. Built for real-time voice processing at the edge, TSP1 allows tasks like speech recognition and text-to-speech to run on smaller, power-constrained devices.
This specialization is particularly relevant as voice interfaces become more common across emerging products. Many edge devices such as wearables or mobile robotics cannot support traditional voice AI systems without compromising battery life or responsiveness. The TSP1 addresses this limitation by enabling these capabilities at significantly lower power levels than conventional alternatives. According to the company, full speech-to-text and text-to-speech can run at under 30 milliwatts of power, which is roughly 10 to 100 times lower than many existing alternatives. This level of efficiency makes advanced voice interaction feasible on devices where power consumption has long been a limiting factor.
That efficiency makes the technology applicable across a wide range of use cases. In augmented reality glasses, it supports responsive, hands-free voice control. In robotics, it enables real-time voice interaction without cloud latency or ongoing service costs. For wearables, it expands voice functionality without severely impacting battery life. In medical devices, it allows on-device inference while keeping sensitive data local. And in automotive systems, it enables consistent voice experiences regardless of network availability.
For investors, this combination of timing and technology is what stands out. Voice interfaces are becoming more common, while reliance on cloud infrastructure is increasingly seen as a limitation rather than a strength. ABR sits at the intersection of those two shifts.
With fresh funding in place, ABR is now working with partners across AR, robotics, healthcare, automotive and wearables to bring that future closer. For startup watchers, it’s a reminder that some of the most meaningful AI advances aren’t about bigger models but about making intelligence fit where it actually needs to live.