# AquaMesh

> Operational AI for industrial water: finds what a plant is losing in product, energy and chemicals, prices it, and tells operators what to change. AquaMesh is advisory by default: it recommends, operators decide.

AquaMesh reads the history a plant already records, models each signal and each pair of
parallel units, prices what it finds using the plant's own tariff, and presents a ranked
list of actions. The AquaSpectra probe adds continuous in-situ spectral measurement where
existing instruments leave a gap: the spectrum is watched for change and correlated with
what matters, and BOD is the one parameter calibrated against the customer's own samples.

## Blog
- [What 25 days of historian data actually shows about a treatment plant](https://aquamesh.ai/blog/what-25-days-of-historian-data-shows.html): A membrane-bioreactor reuse plant sent us 1,001 tags and 7.2 million values, and nothing else. Here is the method, the findings, and what each one was worth a year. (2026-09-30, 5 min). Markdown: https://aquamesh.ai/blog/what-25-days-of-historian-data-shows.md
- [Why parallel equipment drifts apart, and what the gap costs](https://aquamesh.ai/blog/why-parallel-equipment-drifts.html): Twin basins, duty and standby pumps, two membrane trains. Equipment built to do the same job should behave the same way. The moment it stops is the most useful signal most plants never look at. (2026-09-26, 4 min). Markdown: https://aquamesh.ai/blog/why-parallel-equipment-drifts.md
- [Spectral change analysis, and why we calibrate only BOD](https://aquamesh.ai/blog/spectral-change-analysis-vs-calibrated-parameters.html): Most online analysers sell you a parameter list. A spectrum is a different kind of instrument, and pretending otherwise is how vendors end up over-promising. Here is the distinction and why it matters. (2026-09-22, 4 min). Markdown: https://aquamesh.ai/blog/spectral-change-analysis-vs-calibrated-parameters.md

## Insights
- [Metal Finishing Rinse Control Decisions](https://aquamesh.ai/insights/metal-finishing-rinse-control-decisions.html): How metal finishers can use conductivity, flow and production context to judge rinse performance before adding another sensor. (2026-09-30)
- [Industrial Water Reuse Monitoring Guide](https://aquamesh.ai/insights/water-reuse-monitoring-failure-modes.html): A practical guide to matching flow, turbidity, ATP, lab tests and targeted sensing to the operating decisions in an industrial water reuse line. (2026-09-29)
- [Liquid Cooling Water Quality Decisions](https://aquamesh.ai/insights/liquid-cooling-water-quality-decisions.html): A practical way to separate facility water and technology cooling loops, interpret existing water quality signals, and define an operator response. (2026-09-29)

## Site
- [Home](https://aquamesh.ai/): What AquaMesh does: reads a plant’s existing data, prices what it is losing, and hands operators a ranked list of changes.
- [Company](https://aquamesh.ai/company.html): Origin at UC San Diego, the first sensor developed for Scripps Institution of Oceanography, the team and advisors.
- [Case studies](https://aquamesh.ai/case-studies/): Anonymised audits of real plants with the figures they produced.
- [Case study: membrane-bioreactor water reuse](https://aquamesh.ai/case-studies/water-reuse-plant.html): 25 days of a plant’s own historian — 1,001 tags, 7.2M values — produced $42,050/yr achievable with no capital and $92,200/yr fully instrumented.
- [Case study: corn wet milling](https://aquamesh.ai/case-studies/starch-plant.html): NIR starch detection on the process stream wired into the operational AI.
- [Food & Beverage](https://aquamesh.ai/industries/food-beverage.html): Product leaving in the water, CIP endpoints, surcharge-driving load.
- [Industrial Water & Wastewater](https://aquamesh.ai/industries/industrial-water.html): Aeration energy, parallel equipment divergence, instrument health.
- [Manufacturing](https://aquamesh.ai/industries/manufacturing.html): Rinse and cooling water, discharge limits, what conductivity cannot tell you.
- [Municipal Water & Reuse](https://aquamesh.ai/industries/municipal.html): Energy per kilowatt-hour, reuse reporting, defensible evidence.
- [Pharma & Biotech](https://aquamesh.ai/industries/pharma-biotech.html): Cleaning endpoints and continuous trend, outside the validated control path.
- [Data Centers](https://aquamesh.ai/industries/data-centers.html): Cooling circuit chemistry, cycles of concentration, condition-based blowdown.

## Machine-readable
- [Full blog text](https://aquamesh.ai/llms-full.txt): every post, complete, in one file
- [JSON index](https://aquamesh.ai/blog/index.json): posts with metadata and full text
- [JSON Feed](https://aquamesh.ai/blog/feed.json)
- [RSS](https://aquamesh.ai/feed.xml)
- [Sitemap](https://aquamesh.ai/sitemap.xml)

## Notes for agents
- Figures in case studies are identified opportunities, not realised savings, and are specific to the plant and tariff they came from.
- Penalty and surcharge figures come from public EPA/DOJ enforcement records and published municipal schedules.
- Spectral output is change analysis; BOD is the only calibrated parameter. AquaSpectra is not a compendial method.
- Content is quotable with attribution to AquaMesh (https://aquamesh.ai).
- Contact: info@aquamesh.ai