Transform Your Chemical Data with
Machine Learning
Faster Insights. Better Decisions.
We build machine learning solutions for your chemistry data analysis.
Analytical Techniques We Empower
We specialize in extracting valuable insights from complex chemical data across multiple analytical techniques
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Separation Methods
Separate and quantify components in a mixture based on their physical or chemical properties (e.g., HPLC, GC).
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Spectroscopy
Identify molecular structure and functional groups through interactions between matter and electromagnetic radiation (e.g., NMR, FTIR, NIR).
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Thermal Techniques
Analyse material transitions, stability, and composition as a function of temperature (e.g., DSC, TGA).
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Other
Electrochemical, XRD, EDX, etc…
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PatternVerse
Our Machine Learning Toolkit
Combining classical Chemometrics with modern Machine Learning / AI for superior results
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Exploration & Preparation
- • Principal Component Analysis (PCA)
- • Clustering (k-Means, Hierarchical)
- • Feature Selection / Dimension Reduction
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Classical Machine Learning
- • Linear & Logistic Regression
- • Decision Trees
- • Support Vector Machines
- • k-Nearest Neighbour
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Ensemble & Hybrid Methods
- • Random Forest
- • Gradient Boosting / XGBoost
- • Bagging
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Deep Learning
- • Forward Neural Networks
- • Convolutional Neural Networks
- • Transfer Learning
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From Raw Data to Actionable Insights
Our unique data processing pipelines ensure high-quality inputs for your machine learning models, regardless of data source.
Data Pre-processing and Feature Selection
Advanced spectral pre-processing including SNV, derivatives, MSC and custom baseline correction. Each analytical technique requires a different approach.
Model Deployment
Easy integration with laboratory instruments and enterprise systems via REST APIs.
Feature Engineering
Domain-specific feature extraction for XRD patterns, rheograms and NMR spectra.
Ready to Transform Your Chemical Data Analysis?
Schedule a free consultation with our ML experts to discuss your specific analytical challenges.