Stop guessing at reality. Fabi Analytics engineers the GlassBox.AI Architecture™—replacing opaque, statistical black boxes with deterministic, continuous manifold dynamics designed for extreme physical environments.
Continuous equivariant operators for solid-electrolyte modeling.
GlassBox trains continuous, species-aware operators on ab initio data to map atomic geometries to potential energy. E(3) equivariance keeps the energy invariant and the forces (from spatial derivatives) consistent with the surface, so molecular dynamics conserves energy up to integrator tolerance.
No dynamic neighbor lists or graphs are required at inference, allowing the same operator to run on cells of different sizes. The differentiable energy supports free-volume channel analysis and gradient-based barrier calculations, and exports to compact ONNX for efficient inference. Value versus conventional equivariant MLIPs will be measured in resolution flexibility and scaling on larger or defective electrolyte cells.
Standard artificial intelligence treats the physical world as a black box. Our stack is built upon a foundational Continuous Neural Operator (CNO), engineered from the ground up to respect physical laws, geometric structures, and continuous spatial mechanics.
A continuous topological image diagnostic engine bypassing standard Euclidean convolution in favor of explicit manifold learning to isolate biological and physical structural anomalies.
Universal n-dimensional spatial engine mapping non-Euclidean geometries, potential energy surfaces, and dynamic restoring forces across molecular, biological, and microscale systems.
A second-order kinetic optimizer utilizing elastic momentum to preserve stability on stiff PDEs and chaotic spatiotemporal dynamics where standard momentum optimizers catastrophically fail.
Advanced lattice operators engineered to map the intrinsic manifold of crystalline structures, monotonic functions, and complex sub-grid interactions.
Specialized topological extraction for analyzing hollow, porous, and complex interior geometries where traditional Euclidean mapping collapses.
Physics-Informed Neural Networks (PINNs) that learn the continuous manifold of active biokinetics, thermodynamic diffusion, and fluid environments.
Distributed intelligence for autonomous agents utilizing continuous-state routing to navigate multi-agent arrays through complex physical constraints and kinetic perturbations.
We do not rely on generalized off-the-shelf models. Fabi Analytics designs continuous mathematical spaces and dimensional reduction techniques built for deterministic edge deployment.
Constructing continuous neural operators that inherently preserve physical conservation laws, boundaries, and N-dimensional field interactions.
High-fidelity extraction and noise-subtraction pipelines utilizing continuous mathematical transforms and spectral decomposition.
Mesh-free operator compilations and optimized binary binaries built to execute high-density physics computations locally on edge hardware.
Fabi Analytics Inc. was founded by Louis Fabbi, a computational systems architect specializing in the intersection of continuous physics, pure mathematics, and machine learning engineering.
Moving beyond opaque statistical algorithms, the firm leverages manifold learning, geometric topology, and continuous operator theory to construct physics-informed AI architectures. Based in Calgary, Alberta, Fabi Analytics translates theoretical physical science, fluid dynamics, and non-linear mechanics into robust, edge-native computational pipelines.
Strategic Partner: Insight
Insight is a global Fortune 500 solutions integrator that helps organizations design, build, and manage modern IT architectures across North America, EMEA, and APAC.
Fabi Analytics partners with Insight to bridge physics-informed AI systems with enterprise-scale infrastructure, procurement, and deployment—taking GlassBox architectures directly from advanced R&D into production edge environments.
