Predictive maintenance for wastewater treatment plant
Predictive maintenance for wastewater treatment plant
Predictive maintenance for wastewater treatment plant
Timeline
Nov 2022 – May 2024
Timeline
Nov 2022 – May 2024
Stage
Startup within corporation
Stage
Startup within corporation
Industry
Utilities, Water management
Industry
Utilities, Water management

My impact
Led end-to-end research & design activities for two product versions. Turning discovery phase into functional Minimum Viable Product.
Facilitated x2 live discovery workshops and started Product Trio meetings, that allowed us to align vision and define scope (Continuous Discovery Habits)
Ran 4+ rounds of usability tests with pilot customers and subject-matter experts
Enabled onboarding for 4 pilot companies, that allowed us to secure full funding
My impact
Led end-to-end research & design activities for two product versions. Turning discovery phase into functional Minimum Viable Product.
Facilitated x2 live discovery workshops and started Product Trio meetings, that allowed us to align vision and define scope (Continuous Discovery Habits)
Ran 4+ rounds of usability tests with pilot customers and subject-matter experts
Enabled onboarding for 4 pilot companies, that allowed us to secure full funding
context
It’s XXI centuary and yet everyday water & sewage maintenance means running from one alarm to another.
Not knowing what tools to bring, how big the problem is or when missing spare parts will be available.
How about no more?
context
It’s XXI centuary and yet everyday water & sewage maintenance means running from one alarm to another.
Not knowing what tools to bring, how big the problem is or when missing spare parts will be available.
How about no more?
Challenges
For our clients
Water infrastructure teams are often overwhelmed by alarms: scattered systems, missing spare parts, and no clue what they’re walking into.
→ Service Engineers
For field engineers, every day is a puzzle. They juggle unpredictable routes, respond to emergency calls on the fly, and often arrive without the right tools or parts. Often without proper training or often visibility into asset history.
→ Utility Managers
When repair cycles are long and spare parts are delayed, planning is a guesswork. Aging infrastructure only adds to the challenge. Teams are shrinking, experienced staff are rare, and most junior engineers need active supervision.
For the Posydon team
The initial scope was massive. The feature tree alone had to be adjusted to vague job titles of our target group, that differed per company.
Building clarity took time especially that we started with:
Huge scope with unclear priorities (welcome to Minimal Viable Product 🙃)
Corporate constraints like branding guidelines or regulatory limitations
Supporting two product versions at once: algorithm & expert-led
Challenges
For our clients
Water infrastructure teams are often overwhelmed by alarms: scattered systems, missing spare parts, and no clue what they’re walking into.
→ Service Engineers
For field engineers, every day is a puzzle. They juggle unpredictable routes, respond to emergency calls on the fly, and often arrive without the right tools or parts. Often without proper training or often visibility into asset history.
→ Utility Managers
When repair cycles are long and spare parts are delayed, planning is a guesswork. Aging infrastructure only adds to the challenge. Teams are shrinking, experienced staff are rare, and most junior engineers need active supervision.
For the Posydon team
The initial scope was massive. The feature tree alone had to be adjusted to vague job titles of our target group, that differed per company.
Building clarity took time especially that we started with:
Huge scope with unclear priorities (welcome to Minimal Viable Product 🙃)
Corporate constraints like branding guidelines or regulatory limitations
Supporting two product versions at once: algorithm & expert-led






goal
Helping water managers spot issues before they happen
The project focused on predictive maintenance, combining sensor data (SCADA), AI algorithms, and expert knowledge to:
Reduce unplanned failures
Extend asset life
Cut maintenance costs
Lower equipment and labor costs
We ran two product versions:
Algorithm-based: recommendations driven by sensor data
Expert-led: engineers (Subject Matter Experts) working behind the scenes
Did I mention we wanted to squeeze top engineers knowledge into AI-based solution? 🤖
goal
Helping water managers spot issues before they happen
The project focused on predictive maintenance, combining sensor data (SCADA), AI algorithms, and expert knowledge to:
Reduce unplanned failures
Extend asset life
Cut maintenance costs
Lower equipment and labor costs
We ran two product versions:
Algorithm-based: recommendations driven by sensor data
Expert-led: engineers (Subject Matter Experts) working behind the scenes
Did I mention we wanted to squeeze top engineers knowledge into AI-based solution? 🤖


learnings
It’s always worth to meet your team in person, especially at early project stage
Deliver small finished pieces (Minimum Viable Functionalities). Fast feedback is everything
Do the best job explaining that prototype is not a product, but fill it with ‘real’ data
Record all the necessary meetings,
even if they intent to last 10 min max
learnings
It’s always worth to meet your team in person, especially at early project stage
Deliver small finished pieces (Minimum Viable Functionalities). Fast feedback is everything
Do the best job explaining that prototype is not a product, but fill it with ‘real’ data
Record all the necessary meetings,
even if they intent to last 10 min max
learnings
It’s always worth to meet your team in person, especially at early project stage
Deliver small finished pieces (Minimum Viable Functionalities). Fast feedback is everything
Do the best job explaining that prototype is not a product, but fill it with ‘real’ data
Record all the necessary meetings,
even if they intent to last 10 min max
I can honestly say that without Zuzanna’s contributions to our product, we would not be where we are today:
ready for commercialization less than a year into development
Andreas Betz
Global Product Manager Services
Xylem Inc.
Get in touch
Local time in Gdańsk, Poland
🇵🇱 🇪🇺
© 2025 Fembot Studio 👾 by Zuzanna Adamczyk. All rights reserved.
Get in touch
Local time in Gdańsk, Poland
🇵🇱 🇪🇺
© 2025 Fembot Studio 👾 by Zuzanna Adamczyk. All rights reserved.
Get in touch
Local time in Gdańsk, Poland
🇵🇱 🇪🇺
© 2025 Fembot Studio 👾 by Zuzanna Adamczyk. All rights reserved.