Physical AI in Rust. Explainable AI in your data pipeline. Transparent models across every modality.
Projects
Transparent AI from sensor to decision — open source, production grade.
The foundation: a zero-copy bridge between Apache Arrow and ndarray. O(1) conversions — views and ownership moves, never a data copy.
nabled-powered linear algebra and ML as DataFusion SQL — 147 scalar UDFs for SVD, QR, eigen, PCA, and matrix functions, callable straight from a query.
11 crates covering linear algebra, ML, robot kinematics & dynamics, control theory, sensor fusion, and full simulation pipelines.
The Barn Effect algorithm in Rust. Appends {col}_effect interpretability columns to any Arrow RecordBatch — zero-copy, no GIL.
The original MontOps explainability platform. SLiCKG architecture across text, tabular, image, video, and documents. 6 FPS on CPU.
Why MontOps
nabled and napparent have no GIL, no runtime overhead, no approximations — just fast, safe Rust built on ndarray and Arrow.
The Barn Effect streams data in chunks. SHAP and LIME can't scale past a few thousand rows. We have no ceiling.
napparent operates directly on RecordBatches. Plug into DataFusion, Polars, or any Arrow-native stack with zero copies.
From linalg to simulation — nabled covers kinematics, dynamics, control, and sensor fusion in one coherent Rust ecosystem.
The Barn Effect and SLiCKG aren't interpretability wrappers. Every prediction is auditable from first principles.
MIT/Apache-2.0 across all projects. Enterprise consulting, custom deployment, and priority support available.
Whether it's a robotics stack, a data pipeline, or a multi-modal ML system — let's talk.
nik@mont-ops.com