Aquawise to Demo AI Water Quality Monitoring at TechCrunch Disrupt 2025
At TechCrunch Disrupt 2025, Aquawise - a Bangkok-based startup - will pull up its AI-driven water-quality monitor. The pitch? Let fish and shrimp farmers in Southeast Asia keep tabs on pond conditions without buying new gear. Instead, the system leans on satellite images of the farms and runs them through a physics-based AI model.
The timing is interesting because governments and industry groups in the region are still figuring out how to police AI in agriculture and environmental work. AI tools seem to promise speed, but regulators are only now sketching rules to make sure they’re accurate, reliable and fair. Because Aquawise uses publicly available satellite data, its approach might shape the conversation around data sourcing and validation for remote-sensing AI.
Showing the model at the conference puts it straight in front of investors, tech heads and policymakers. Disrupt often doubles as a first stop for regulatory eyes on new tech. For aquaculture - a huge economic driver in Southeast Asia - any fresh monitoring method could affect both yields and compliance. How the demo lands may hint at how ready the market and its overseers are for AI-based resource management.
Aquawise wants to offer aquaculture farmers in Southeast Asia a better way to monitor their water quality by using AI and existing satellites — no hardware purchases required. Bangkok-based Aquawise takes satellite images of fish and shrimp farms and feeds them into its physics-based AI model that monitors the water for things like temperature, level of chlorophyl, and oxygen levels. Aquawise’s platform can monitor water quality continuously; traditional methods monitor daily or weekly.
Aquawise also offers tracking and predictions. “Water quality is one of the most important things in aquaculture,” Patipond Tiyapunjanit, co-founder and CEO of Aquawise, told TechCrunch.
For regulators overseeing aquaculture in Southeast Asia, Aquawise’s satellite-based system feels like both a chance and a headache. On the plus side, cheap, wide-area monitoring could let officials spot problems before they blow up and bring some uniformity to compliance checks along sprawling coastlines. On the flip side, plugging this new data into the old paperwork will probably need a lot of testing and maybe brand-new rules about what counts as acceptable evidence.
Small-scale farmers stand to win the most - a low-cost alternative to the pricey buoys and probes they’ve been using. The whole idea, though, rides on whether the satellites can actually deliver accurate, on-time alerts about oxygen drops or possible contaminants - something most growers can’t afford right now. If Aquawise pulls off a solid demo at TechCrunch Disrupt, investors might start lining up, and policymakers could finally start talking seriously about updating aquaculture management with affordable remote-sensing tools, all while keeping an eye on reliability and trust.
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Common Questions Answered
How does Aquawise's AI system monitor water quality without requiring farmers to buy hardware?
Aquawise uses satellite imagery of fish and shrimp farms, which it analyzes with its proprietary physics-based AI model. This approach eliminates the need for farmers to purchase any additional monitoring equipment on-site.
What specific water quality parameters does Aquawise's physics-based AI model track?
The model monitors key parameters including water temperature, chlorophyll levels, and oxygen levels. These metrics are crucial for maintaining healthy conditions in aquaculture farms.
What advantage does Aquawise's continuous monitoring offer over traditional methods?
Aquawise's platform provides continuous monitoring of water quality, whereas traditional methods typically only check conditions on a daily basis. This allows for more timely interventions and better overall farm management.
What are the potential benefits and challenges of Aquawise's technology for aquaculture regulators in Southeast Asia?
The satellite-based approach offers the opportunity for widespread, affordable monitoring, enabling more proactive environmental oversight. However, integrating this novel data into existing regulatory frameworks will require careful validation and potentially new protocols.