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VALIDATED & FUNDED•AWS Deep Tech
Your data is talking.We are listening.
Working with polymers – from material production to final high-quality products? Is your lab and production data scattered across different instruments, vendors and file formats?
Our PolyCore solution brings it together – no manual copying from spreadsheets, just one-click pipelines that preserve full curves and metadata. But unlike a Laboratory Information Management System (LIMS), we do not just store it – we analyze curves from multiple methods (Fourier-Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), tensile test, viscosity, etc.) and forecast properties using our polymer-physics informed DataFusion™ concept. Qualify faster, reduce testing costs and save CO₂.
Apply for the Free Pilot
Limited spots for selected partners
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VALIDATED & FUNDED•AWS Deep Tech
Your data is talking.We are listening.
Working with polymers – from material production to final high-quality products? Is your lab and production data scattered across different instruments, vendors and file formats?
Our PolyCore solution brings it together – no manual copying from spreadsheets, just one-click pipelines that preserve full curves and metadata. But unlike a Laboratory Information Management System (LIMS), we do not just store it – we analyze curves from multiple methods (Fourier-Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), tensile test, viscosity, etc.) and forecast properties using our polymer-physics informed DataFusion™ concept. Qualify faster, reduce testing costs and save CO₂.
Apply for the Free Pilot
Limited spots for selected partners
Backed by
Choose your path
We offer a platform for unified material and product records, consulting for custom needs, mindmap of collaboration possibilities, Return on Investment estimation, as well as transparent pricing.
PolyCore Platform
Upload test data from any vendor. Get one material record per sample (curves + metadata). Compare results across batches, spot outliers, and predict performance from QC tests.
PolyDecypher Consulting
Not ready for a PolyCore subscription? Our polymer scientists analyze your data and deliver custom models for your specific use case.
Real Challenges, Real Solutions
Explore the core challenges facing the plastics industry – and discover how PolyDecypher addresses them at their root.
Digital Data
Digital Data ChallengesVSPolyCore:Basic
Most laboratories and production environments still operate without proper digital systems. Manual workflows, scattered data, and lost institutional knowledge slow down innovation.
Product-Level
Product-Level ChallengesVSPolyCore:Premium
Physical testing of polymers and plastic products is demanding, slow, and resource intensive. Long testing times and heavy loads create costly delays and bottlenecks.
Our Journey
Funding, milestones, and what is next. Upcoming items are targets, not commitments.
Jan2025
ZAT Leoben Funding Secured
Awarded ZAT Leoben Startup Combo and Green pre-seed funding to support conceptualization and development of our businessplan.
May2025
AWS Deep Tech Pre-Seed Support
Received Austria Wirtschaftsservice Gesellschaft (AWS) Deep Tech Pre-Seed funding approval to develop the cloud infrastructure and scalable platform foundations.
Aug2025
PolyDecypher Launched
Official launch of PolyDecypher as a company focused on multi-method materials data integration and physics-informed prediction.
Apr2026• Upcoming
PolyCore Platform Beta Release
Target: PolyCore Basic Features (Q1 2026) and Premium Features (Q3 2026) for centralized data consolidation, analytics, and physics-informed predictions.
Jul2026• Upcoming
Industry Validation Partnerships
Target: Partnerships with polymer manufacturers and testing labs to validate models in production QC workflows.
Jan2027• Upcoming
DACH Region Expansion
2027: Sales expansion in DACH region (Germany, Austria, Switzerland). 2028: European market expansion.
Jan
2025
ZAT Leoben Funding Secured
Awarded ZAT Leoben Startup Combo and Green pre-seed funding to support conceptualization and development of our businessplan.
AWS Deep Tech Pre-Seed Support
Received Austria Wirtschaftsservice Gesellschaft (AWS) Deep Tech Pre-Seed funding approval to develop the cloud infrastructure and scalable platform foundations.
May
2025
Aug
2025
PolyDecypher Launched
Official launch of PolyDecypher as a company focused on multi-method materials data integration and physics-informed prediction.
PolyCore Platform Beta Release
Target: PolyCore Basic Features (Q1 2026) and Premium Features (Q3 2026) for centralized data consolidation, analytics, and physics-informed predictions.
Apr
2026
Upcoming
Jul
2026
Upcoming
Industry Validation Partnerships
Target: Partnerships with polymer manufacturers and testing labs to validate models in production QC workflows.
DACH Region Expansion
2027: Sales expansion in DACH region (Germany, Austria, Switzerland). 2028: European market expansion.
Jan
2027
Upcoming
Why We Built PolyCore
These are the real challenges and expectations we hear from polymer and materials teams – motivating every feature we build.
Industry perspective
“If we had one material record across instruments, we would cut reporting time dramatically.”
Expectation from pilot partners
“Traceability from raw files to decisions is what auditors keep asking for.”
Partner perspective (pilot / consulting)
“We need a way to reuse historical test data instead of repeating year-long experiments.”
Industry perspective
“If we had one material record across instruments, we would cut reporting time dramatically.”
Expectation from pilot partners
“Traceability from raw files to decisions is what auditors keep asking for.”
Partner perspective (pilot / consulting)
“We need a way to reuse historical test data instead of repeating year-long experiments.”
Industry perspective
“If we had one material record across instruments, we would cut reporting time dramatically.”
Expectation from pilot partners
“Traceability from raw files to decisions is what auditors keep asking for.”
Partner perspective (pilot / consulting)
“We need a way to reuse historical test data instead of repeating year-long experiments.”
Industry perspective
“If we had one material record across instruments, we would cut reporting time dramatically.”
Expectation from pilot partners
“Traceability from raw files to decisions is what auditors keep asking for.”
Partner perspective (pilot / consulting)
“We need a way to reuse historical test data instead of repeating year-long experiments.”
Industry perspective
“Our biggest risk is making decisions without knowing how comparable our old data really is.”
Partner perspective (pilot / consulting)
“Every time we switch instruments or vendors, we lose months of context buried in scattered files.”
Expectation from pilot partners
“We would want to predict long-term behavior from short-term tests – if we could trust the model.”
Industry perspective
“Our biggest risk is making decisions without knowing how comparable our old data really is.”
Partner perspective (pilot / consulting)
“Every time we switch instruments or vendors, we lose months of context buried in scattered files.”
Expectation from pilot partners
“We would want to predict long-term behavior from short-term tests – if we could trust the model.”
Industry perspective
“Our biggest risk is making decisions without knowing how comparable our old data really is.”
Partner perspective (pilot / consulting)
“Every time we switch instruments or vendors, we lose months of context buried in scattered files.”
Expectation from pilot partners
“We would want to predict long-term behavior from short-term tests – if we could trust the model.”
Industry perspective
“Our biggest risk is making decisions without knowing how comparable our old data really is.”
Partner perspective (pilot / consulting)
“Every time we switch instruments or vendors, we lose months of context buried in scattered files.”
Expectation from pilot partners
“We would want to predict long-term behavior from short-term tests – if we could trust the model.”
Industry perspective
“Compliance checks currently happen at the end. Catching issues earlier would prevent costly rework.”
Partner perspective (pilot / consulting)
“Recyclate qualification is mostly guesswork right now. A data-driven approach could change that.”
Expectation from pilot partners
“Generic Machine Learning tools assume clean, large datasets. We rarely have either in polymer labs.”
Industry perspective
“Compliance checks currently happen at the end. Catching issues earlier would prevent costly rework.”
Partner perspective (pilot / consulting)
“Recyclate qualification is mostly guesswork right now. A data-driven approach could change that.”
Expectation from pilot partners
“Generic Machine Learning tools assume clean, large datasets. We rarely have either in polymer labs.”
Industry perspective
“Compliance checks currently happen at the end. Catching issues earlier would prevent costly rework.”
Partner perspective (pilot / consulting)
“Recyclate qualification is mostly guesswork right now. A data-driven approach could change that.”
Expectation from pilot partners
“Generic Machine Learning tools assume clean, large datasets. We rarely have either in polymer labs.”
Industry perspective
“Compliance checks currently happen at the end. Catching issues earlier would prevent costly rework.”
Partner perspective (pilot / consulting)
“Recyclate qualification is mostly guesswork right now. A data-driven approach could change that.”
Expectation from pilot partners
“Generic Machine Learning tools assume clean, large datasets. We rarely have either in polymer labs.”
The plastics industry is conservative by nature – what works, you don't touch. So, our approach is not to disrupt established systems, but to provide an intelligent overlay on top of what already exists – one that delivers both digitalization and green impact without requiring companies to change how they work.
Frequently Asked Questions
Short, practical answers for teams evaluating PolyCore.