Manual Sampling
Provides trusted data, but it isn't continuous — conditions can shift entirely between visits.
AquaMesh helps coastal monitoring teams move from fragmented sensor data and manual review to one operating layer for live water quality visibility, alerts, AI summaries, and historical trend analysis.
Coastal monitoring teams often rely on periodic field sampling, disconnected dashboards, spreadsheets, and manual data review, creating a delay between when water quality begins to change and when operators can respond. For public agencies, those delays can affect public safety, regulatory reporting, field response, and stakeholder communication.
Provides trusted data, but it isn't continuous — conditions can shift entirely between visits.
Show readings in real time, but rarely explain what matters or which site needs attention first.
Can visualize information, but require heavy configuration and still leave interpretation to the operator.
AquaMesh sits above existing sensors and data sources, turning coastal water quality data into operational intelligence: centralized, alert-driven, and explained in plain language.
A cloud-based monitoring platform built to centralize sensor data, surface real-time alerts, and deliver AI-assisted operational insight across every coastal deployment.
Sensor readings from coastal monitoring stations are ingested into the AquaMesh backend and organized by tenant, deployment, hub, and sensor — with secure multi-tenant access, serverless alert evaluation, and cloud-based storage for historical readings.
Operators view coastal monitoring stations on an interactive map, so it's immediately clear where sensors are deployed and which sites require attention — plus a fleet-wide read on healthy sites, active alerts, and highest risk reading.
Users monitor readings such as chlorophyll, turbidity, nitrate, temperature, and dissolved oxygen across selected deployments, with short-range forecasts layered directly onto measured data.
AquaMesh lets operators configure threshold-based alert rules for specific sensor types. When a reading crosses a threshold, the system opens an alert that can be reviewed, acknowledged, and resolved — with full metric, observed value, and threshold context.
The AI Insights module summarizes current water quality conditions, identifies notable changes, and helps operators understand which sites or parameters may require attention — without reading every chart by hand.
Historical charts let users compare sensor values over time and identify trends such as increasing turbidity, nutrient changes, or possible algae bloom indicators — summarized automatically into a shareable report.
The goal is to prove that AquaMesh can reduce manual review time, improve situational awareness, and help teams identify abnormal conditions earlier.
Identify threshold violations in minutes instead of hours or days
Reduced manual review time across all monitored sites
Centralized visibility across every pilot location
More consistent alert handling, acknowledge to resolve
Clearer prioritization of follow-up sampling for field teams
Plainer summaries of current water quality conditions for managers
These are target pilot outcomes, not verified production claims.
Centralize live sensor data, detect abnormal conditions earlier, and communicate site status more clearly.