600–1,200 h
collecting and sorting data by hand
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VALIDATED & FUNDED•AWS Deep Tech
The material intelligence platform for polymer labs
Your data is talking.We are listening.
One neutral platform for every testing machine in your lab: PolyCore reads the raw files — whatever the vendor — and turns them automatically into -aligned material records. Ready for Quality Control, Machine Learning, and Digital Product Passport preparation.
Bring your file types + methods – we will tell you what is feasible.
For a lab with 12,000 test files a year:
~2 h
for the same files in PolyCore
EUR 39k–78k
saved every year, at EUR 65/h
20+
test methods read automatically
At the upper end, that is nearly three-quarters of a full-time position — spent only on collecting and sorting data, before any analysis.
Adapt to your use caseBuilt for
From polymer engineers. For the polymer industry.
Whether you manufacture, test, recycle, or research – we speak your language. Starting with polymers, extensible to any material.
- Product manufacturing
- Project management
- Testing labs
- Universities
- Research centers
Industries
- Industrial processing & manufacturing
- Infrastructure & environment
- Automotive & aerospace
- Pharma, medical & hygiene
- Recycling & recyclates
Backed by
SEE POLYCORE IN ACTION
Watch raw instrument files become a living material record.
From upload to parsed curves, material records, and analysis. Every curve in the film is real measured data from polymer instruments — no stock footage, no invented numbers.
20+ test methods read automatically
- DSC
- TGA
- OIT/OOT
- Cp
- Tensile Testing
- Flexural
- Charpy
- Compression
- DMA
- Nanoindentation
- MFR
- Capillary rheometry
- Rotational rheology
- FTIR
- NIR
- UV-Vis
- Raman
- Fluorescence
- GPC/SEC
- GC-MS
- Density
Real Challenges, Real Solutions
Two entry points explain the homepage claim: first make fragmented test data usable, then use full-signal records to predict product-level performance earlier.
Data Foundation
Fragmented Test DataVSPolyCore
Vendor exports, PDFs, CSVs, spreadsheets, instrument files, curves, spectra, scalar values, and lab knowledge all start in different places. PolyCore turns that scattered layer into one searchable, FAIR-aligned material data foundation.
Product-Level
Product-Level ChallengesVSPolyCore
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
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
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
Jul
2026
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
- Presented at Plastic Pipes Conference XXII
- AWS Deep Tech · validated & funded program
- Peer-reviewed publication in progress
Why We Built PolyCore
These are paraphrased, composite industry sentiments – not customer testimonials. They reflect the recurring challenges and expectations we hear from polymer and materials teams, and they motivate 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.”
Most industries are 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
Compact answers for teams comparing manual work, LIMS, and PolyCore.
Where to next.
Platform deep-dive, consulting, 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
Need help getting started? Add onboarding, team training, or focused materials and ML expertise around PolyCore.
Turn any instrument file into a record that lasts.
One record, ready for audit, Digital Product Passport, and machine learning. Proven at the curve level, not at the sample level.
See PolyCore →