Nyad offers an AI-powered software solution for activated sludge wastewater treatment plants. It provides real-time microbial analysis using computer vision and machine learning to give operators insights into biological activity, enabling optimized performance and reduced chemical costs.
Funding
$320K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Industrial wastewater plants utilizing the activated sludge process often lack real-time visibility into the microbial communities driving treatment. This opacity makes it challenging for operators to proactively manage biological processes, leading to suboptimal performance, increased chemical expenditure, and potential process disruptions.
Solution
Nyad provides an AI-powered microbiology co-pilot designed for activated sludge wastewater treatment plants. The platform leverages computer vision and machine learning to deliver real-time microbial analysis, offering operators critical insights into the biological activity within the system. This enables data-driven decision-making to optimize plant efficiency, reduce operational costs associated with chemical inputs, and mitigate the risk of process upsets. Nyad operates as a pure software solution, facilitating rapid deployment without requiring extensive system integration or causing operational downtime.
Target Audience
The primary customers are operators and engineers at industrial wastewater treatment facilities that employ the activated sludge process.
Features
- AI-powered computer vision engine for real-time microbial identification and analysis
- Machine learning models trained on activated sludge process biology to interpret microbial data
- Software-only deployment for rapid integration and minimal operational impact
- Real-time data visualization of microbial populations and their correlation to treatment performance
- Predictive analytics for early detection of potential process upsets
- Optimization recommendations for chemical dosing and operational parameters based on microbial insights