Scirizz is building a portfolio of deep-technology products across AI security, scientific computing, engineering simulation, biotechnology, and mission systems. Products are designed for SaaS, private-cloud, on-premises, and air-gapped deployment where the workload requires it.
Model files, agents, tool use, generated code, scientific pipelines, and heterogeneous infrastructure create attack surfaces conventional security tools were not designed to understand.
An AI-native security platform for models, agents, and ML pipelines. Discover AI assets, inspect model artifacts, generate an AI bill of materials, test prompt and agent attacks, enforce runtime policies, and monitor model integrity without requiring sensitive data to leave the environment.
AI-assisted source and binary assurance for high-consequence software. Analyze C, C++, Rust, Python, CUDA and compiled artifacts; correlate static, dynamic and dependency evidence; explain vulnerable paths; and produce reviewable patches rather than opaque one-click changes.
Scientific and AI workloads often become trapped in a language, vendor runtime, accelerator, or decades-old codebase. We are building tools that make those transitions measurable and testable.
A cross-architecture compiler and runtime for scientific kernels and AI inference. Import computational graphs and native kernels, optimize through a common IR, autotune for target hardware, and generate deployable workloads for heterogeneous CPU, GPU, NPU and accelerator environments.
A semantic scientific-code migration engine for Fortran, MATLAB, SAS, CUDA, C and C++. Transmute uses compiler analysis plus AI to translate legacy systems while generating numerical-equivalence tests, dependency maps, confidence scores and performance benchmarks.
Physics-based computation becomes much more valuable when simulation, uncertainty, optimization, surrogate models, and experimental data are part of the same workflow.
An AI-accelerated simulation and digital-twin workbench for ODE/PDE and multiphysics systems. Combine numerical solvers, reduced-order models, physics-informed surrogates, sensor data, parameter estimation, uncertainty quantification and inverse problems.
A constrained engineering design explorer that couples simulation with Bayesian optimization, evolutionary methods and generative AI. Search geometry, materials, operating parameters and control policies against competing objectives and manufacturability constraints.
Biology and chemistry teams need AI that understands scientific provenance, specialized data types, computational workflows, and reproducibility—not only natural-language chat.
A private AI-powered bioinformatics environment combining omics pipelines, scientific data management, knowledge graphs, literature intelligence, statistical analysis and reproducible agent workflows. Designed for sensitive datasets and organization-specific methods.
An AI-orchestrated molecular and materials discovery platform that links property prediction, structure generation, docking, molecular dynamics, quantum-chemistry tools and active learning into traceable computational campaigns.
Some of the hardest AI problems are not text problems: they involve different sensors, uncertain physical environments, specialized instruments, edge compute and human review.
A multimodal sensor-fusion platform for EO/IR, hyperspectral, radar, RF, acoustic and telemetry data. Align asynchronous streams, learn cross-sensor representations, detect anomalies and deploy optimized inference to constrained edge systems.
A secure scientific-agent platform that can reason across papers, datasets, code, simulation tools, notebooks, HPC jobs and instrument APIs while preserving approvals, provenance and reproducibility. Built for supervised autonomy rather than unconstrained agents.
Scientific, industrial, biotechnology, cybersecurity and defense workloads often cannot be forced into a public multi-tenant SaaS model. The portfolio is designed around deployment flexibility from the beginning.
For products still under active development, Scirizz can build around a real high-value workflow rather than designing in isolation. Design partners help define interfaces, benchmarks, deployment constraints and the first production use cases.
Evaluate an early platform against a defined workload and benchmark.
Adapt the product core to proprietary data, hardware, code, infrastructure or security requirements.
Integrate selected compiler, model, simulation or security technology into an existing product or platform.
Bring us the workload, benchmark, failure mode, scientific constraint, or legacy system. We can determine whether to extend a platform or build a new one.
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