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About Us
A polymer scientist and Machine Learning Operations (MLOps) engineer, a senior software developer and an entrepreneur – united by one goal: turning fragmented test data into actionable intelligence.
[ BRAND ]
Why the Glasses?
A New Perspective on Your Data
What if companies continue producing exactly the same data – but through us gain a new perspective? That is the meaning behind our glasses logo.
You do not need different data. You need to see your existing data differently. PolyDecypher provides the lens that reveals connections hidden in plain sight – uncovering hidden interactions through Data Fusion and Multivariate Data Analysis (MVDA) that single tests alone cannot reveal.
Same data. New perspective. Better decisions.
Our Story
PolyDecypher was born from a simple realization: the plastics industry generates mountains of valuable data – but the insights stay trapped in isolated systems.
Our founder spent over a decade in polymer research before adding Data Science and MLOps expertise at the University of Paris (Sorbonne). The combination revealed what was missing: domain knowledge meeting modern data science.
Together with entrepreneurial co-founders and support from ZAT Leoben's startup ecosystem and funding by AWS Deep Tech PreSeed, PolyDecypher emerged – built by people who understand both the lab bench and the data pipeline.
Why This Combination is Rare
In theory, combining material science with machine learning fits perfectly. In practice, hardly anyone does it – because it is too costly without the right expertise.
Material data is not like business data. Without domain knowledge, you may build models that look statistically pretty but are technically not generalizable – like building a foundation on sandy ground.
[ APPROACH ]
Data Fusion, Not Data Separation
See What Others Miss
Today, material data lives in separate, isolated systems: IR spectrum is labeled 'chemical', tensile test is 'mechanical', thermal analysis is 'process'. But they are all measuring the same material.
PolyDecypher does not place data beside each other – we layer and dissect it. Each test contributes differently to the full picture: one method might reveal 10 % of a correlation, another 30 %, another 60 %. Together, they predict product properties like lifetime far better than any single test alone.
From these CONNECTIONS come insights you would never see with the naked eye.
Quality Data
Process Data
Thermal Analysis
Mechanical Tests
Spectral Data (FTIR)
Data Fusion
[ PURPOSE ]
Our Mission & Our Vision
Mission
Turning fragmented test and production data into actionable material intelligence.
Vision
Leading polymer R&D and QC innovation through digitalization and AI.
Our Values
Our values guide how we UNIFY data, ANALYZE it comprehensively, and FORECAST performance.
Responsibility and sustainability
Accurate predictions mean fewer wasted batches, less energy on dead-end tests, and smarter material choices – without compromising quality.
Products designed for decades showing failure after just 5 years – often due to recycled material use – is not just an expensive claim. It is image damage for the entire plastics industry.
Trust is good. Control is PolyDecypher.
Leadership
Domain expertise meets modern data science. Our team combines polymer engineering credentials with software development experience to build tools that actually understand your data.
Maria Messiha-Awad, MEd.
Chief Operating Officer
Entrepreneur managing design, social media, public appearances, finances, and all internal operations.
