Introduce Inductive ML and link our public research and MONARCH release

README.md

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-title: README
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+title: Inductive ML
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-Edit this `README.md` markdown file to author your organization card.
+# Inductive ML
+
+**An independent lab studying how machines learn to learn.**
+
+We study how learned systems represent, adapt, and generalize, with experiments in representation geometry, efficient adaptation, and the systems that make models practical to run. Founded and run by **Aditya Veer Parmar** in **Bangalore, India**.
+
+[Website](https://inductive.ml) · [Experiments](https://inductive.ml/experiments) · [GitHub](https://github.com/inductiveML) · [About the lab](https://inductive.ml/about)
+
+## Explore our work
+
+### MONARCH · Language models on your GPU
+
+How far can careful GPU programming take a small language model in a browser?
+
+MONARCH explores WebGPU and WGSL kernel optimization for **Liquid AI's LFM2.5-230M**. The public demo runs inference on your device, with editable prompts, Markdown responses, live tokens per second, and a repeatable benchmark. The release includes kernel source, build instructions, model checksums, and documented measurement conditions.
+
+**[Try the demo](https://huggingface.co/spaces/inductiveML/monarch-webgpu)** · [Read the experiment](https://inductive.ml/experiments/monarch) · [Browse the code](https://huggingface.co/spaces/inductiveML/monarch-webgpu/tree/main) · [Model files](https://huggingface.co/inductiveML/LFM2.5-230M-MONARCH)
+
+### Staged Learned Coordinates · Teaching trees to understand geometry
+
+Can a small neural network learn a coordinate system that makes a decision tree's job easier? This experiment studies learned representations in front of XGBoost to simplify curved and rotated decision boundaries.
+
+[Read the experiment](https://inductive.ml/staged-learned-coordinates)
+
+## How we work
+
+We publish experiments with their methods, measurements, and limitations. Correctness checks come before performance claims; recorded lab results are distinguished from measurements on your own hardware.
+
+MONARCH's code is **Apache-2.0**. Its model and tokenizer data retain **Liquid AI's LFM Open License v1.0**; see each repository for its license and provenance.
+
+Questions, replications, and technical feedback are welcome in our [Space discussions](https://huggingface.co/spaces/inductiveML/monarch-webgpu/discussions).