Normalized claim
Time: 65% increase
Client onboarding is 65% faster than the initial version.
Ainwater transforms water management with predictive AI on AWS
Use case typeAI platformUpdated Jun 13, 2026
Ainwater builds Poseidón, an AI-based predictive management solution for drinking water, wastewater, and desalination. The company moved the pilot into a scalable multi-tenant SaaS architecture on AWS, embedded machine learning and generative AI into the product, and expanded the service across plants in Chile, Mexico, and Brazil. The solution provides operational recommendations and predictive insights to help water treatment operators shift from reactive to predictive management.
- Organization
- Ainwater
- Industry
- Energy & Utilities
- Location
- Chile
- Published
- May 2026
Reported outcomes
+80%
timeTime & speed
+65%time30-40%quantified impact−9%cost−35%cost−30%cost
Strategic outcomes
New business modelTurned pilot into multi-tenant SaaSNew product / capabilityEmbedded predictive and generative AIMarket & geographic expansionExpanded across three countriesBetter decisions & insightShifted operators to predictive management
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 80% increase
Model development is 80% faster using Amazon SageMaker AI and Amazon Bedrock.
Normalized claim
Quantified impact: 30-40% increase
Delivery speed increased by 30-40%.
Normalized claim
Cost: 9% decrease
One customer achieved 9% energy savings; the broader solution delivered 35% less grey water, 30% energy reduction, 10% less chemical usage, and 82% more water produced by desalination membranes.
Normalized claim
Cost: 35% decrease
One customer achieved 9% energy savings; the broader solution delivered 35% less grey water, 30% energy reduction, 10% less chemical usage, and 82% more water produced by desalination membranes.
Normalized claim
Cost: 30% decrease
One customer achieved 9% energy savings; the broader solution delivered 35% less grey water, 30% energy reduction, 10% less chemical usage, and 82% more water produced by desalination membranes.
Normalized claim
Cost: 10% decrease
One customer achieved 9% energy savings; the broader solution delivered 35% less grey water, 30% energy reduction, 10% less chemical usage, and 82% more water produced by desalination membranes.
Normalized claim
Cost: 82% decrease
One customer achieved 9% energy savings; the broader solution delivered 35% less grey water, 30% energy reduction, 10% less chemical usage, and 82% more water produced by desalination membranes.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Ainwater
- Provider
- AWS
- Maturity
- Scaled Production
- Linked source
- AWS
Scaled to over 100 plants in 3 countries
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 4
- 1Predictive Analytics
- 2Operational Optimization
- 3SaaS Platform
- Ainwater needed to turn a pilot into a SaaS solution that could support multinational clients.
- The company needed a secure, compliant environment to scale predictive water-treatment operations across regions.
- The solution had to handle heterogeneous industrial data and support operational recommendations for plant operators.
- Ainwater adopted a multi-tenant SaaS architecture on AWS for Poseidón.
- Amazon SageMaker AI is used to embed predictive machine learning models directly into the software.
- Amazon Bedrock is used to experiment with and incorporate LLMs for generative AI applications and agents.
- AWS Fargate serves as core serverless compute for containers.
- Terraform is used to implement the architecture and manage infrastructure.
- Scaled to over 100 plants in 3 countries.
- Client onboarding is 65% faster than the initial version.
- Model development is 80% faster using Amazon SageMaker AI and Amazon Bedrock.
- Delivery speed increased by 30-40%.
- One customer achieved 9% energy savings; the broader solution delivered 35% less grey water, 30% energy reduction, 10% less chemical usage, and 82% more water produced by desalination membranes.
Architecture
Poseidón is a modular multi-tenant SaaS architecture on AWS that ingests heterogeneous industrial data, applies time-series forecasting and chemical-dosing optimization models embedded with Amazon SageMaker AI, uses Amazon Bedrock for LLM experimentation, and runs core container workloads on AWS Fargate with infrastructure managed by Terraform.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 27, 2026Publisher: AWSEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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