We built InVariants to address a gap we kept encountering in data analysis workflows: standard statistical methods and classical ML describe what is in the data, but struggle to capture the underlying structural patterns — especially in high-dimensional, noisy, or topologically complex datasets. Topological Data Analysis (TDA) was the answer, but it has historically required significant mathematical background and custom engineering to apply in practice. InVariants brings TDA into a no-code workspace alongside ML training, dimensionality reduction, anomaly detection, and time series analysis — so practitioners can explore data structure without writing a single line of code. Key capabilities in the current beta: • Persistent homology and Mapper graphs with AI-powered interpretation • ML training with SHAP explainability, PDP/ICE plots, and model export as a self-contained Python bundle • Anomaly detection, ARIMA forecasting, and rolling TDA for time series • Full data preparation pipeline with undo history We are actively looking for beta participants — particularly data scientists, analysts, and ML engineers working with complex or high-dimensional data. Access is free during the beta period. Apply at invariants.tech — happy to answer questions here.

InVariants (Beta)

Explore your data's shape with TDA, ML & AI — no code

Explore your data's shape with TDA, ML & AI — no code