Capabilities

Research depth with engineering discipline.

Scirizz works at the intersection of scientific computing, AI/ML, AI-powered cybersecurity, numerical modeling, compilers, and domain science. We can enter at the research question, the algorithm, the prototype, or the production bottleneck.

AI/ML Model Training, Fine-Tuning & Optimization

End-to-end model engineering for technical workloads where accuracy, data quality, latency, memory, hardware, privacy, and scientific validity all matter.

Custom ML and deep-learning model development in PyTorch and modern model ecosystems
Foundation-model adaptation: SFT, LoRA/QLoRA, PEFT, domain adaptation and task-specific fine-tuning
Training pipelines, distributed/multi-GPU training, checkpointing, experiment tracking and hyperparameter optimization
LLMs, VLMs, multimodal models, computer vision, transformers, GNNs, time-series and domain models
Scientific ML: PINNs, surrogate models, differentiable models and physics/data hybrid approaches
Model evaluation, calibration, uncertainty, robustness, drift, ablations, error analysis and benchmark design
Compression and efficiency: knowledge distillation, structured/unstructured pruning, sparsity and low-rank methods
Quantization: FP8/INT8/INT4, PTQ/QAT, accuracy-retention studies and mixed-precision strategies
Inference optimization with ONNX, TensorRT, OpenVINO and hardware-aware graph/runtime tuning
Cloud, on-prem, air-gapped, edge, workstation and HPC deployment architectures

Full AI/ML engineering capability →

AI-Powered Cybersecurity & Software Assurance

Security engineering for AI systems, software, models, agents, pipelines, and compute environments where conventional tooling lacks context.

AI model supply-chain scanning, provenance and integrity analysis
LLM, agent and MCP security testing and runtime controls
AI-assisted vulnerability discovery and secure code review
Static, dynamic, dependency and binary analysis workflows
Anomaly detection for cyber-physical, sensor and compute systems
Private, self-hosted and air-gapped security architectures

Simulation, Modeling & Optimization

Computational models for systems where experiments are expensive, slow, or incomplete.

ODE/PDE and multiphysics workflows
Monte Carlo and stochastic simulation
Parameter estimation and inverse problems
Numerical optimization and metaheuristics
Sensitivity, uncertainty and robustness analysis
Digital models and simulation-based decision support

Scientific Computing, Compilers & HPC

Performance work from algorithms and memory layout to code generation and heterogeneous execution.

Compilers, transpilers, IRs and code generation
LLVM and domain-specific compiler pipelines
GPU/CPU acceleration and parallel computing
ONNX and AI inference optimization
SIMD, memory, runtime and systems performance
Cloud, on-prem, cluster and edge deployments

Biotechnology & Bioinformatics

Software and analytical systems for biological data, computational biology, and translational R&D.

Bioinformatics and omics data pipelines
Computational and systems biology
Sequence, structure and biological data analysis
Scientific literature and knowledge extraction
Statistical modeling and reproducible analysis
AI-assisted discovery and research automation

Defense & Mission Engineering

Research software for sensing, decision support, modeling, autonomy, and compute-constrained environments.

EO/IR, imagery and multimodal sensor analytics
Mission and scenario simulation
Edge AI and optimized inference
Signal processing and detection workflows
Human-machine decision support
Secure, controlled and on-prem deployment architectures

Physics, Chemistry & Engineering

Computational methods and custom software for specialized scientific and engineering domains.

Computational physics and mathematical modeling
Chemistry, materials and scientific data workflows
Optics, photonics and imaging computation
Controls, signal processing and system identification
Scientific visualization and analysis tooling
Research prototypes and feasibility demonstrators

How we work

From research question to validated software.

For exploratory R&D, the fastest path is usually a disciplined sequence of measurable technical risks rather than a large software build up front.

Frame the technical risk

Define the scientific question, measurable success criteria, data constraints, compute envelope, and transition target.

Prototype the minimum proof

Build the smallest credible simulation, model, compiler pass, algorithm, or software demonstrator that can falsify the key assumptions.

Validate and benchmark

Compare against baselines, characterize failure modes, quantify uncertainty, and document reproducible results.

Engineer for transition

Harden interfaces, optimize compute, add tests and observability, and prepare the technology for customer integration or the next TRL.

Technical environments

Designed to meet the workload where it lives.

Our approach is stack-agnostic: we choose tools and runtimes around numerical needs, interoperability, deployment constraints, and the customer's existing environment.

PyTorch / Transformers

LoRA / QLoRA / PEFT

DDP / FSDP / Multi-GPU

ONNX / TensorRT

OpenVINO / Edge

CUDA / GPU Compute

INT8 / INT4 / FP8

Distillation / Pruning

Python / C++ / Rust

LLVM / MLIR / Codegen

Cloud / HPC / Clusters

On-prem / Air-gapped

Need capability beyond an off-the-shelf tool?

That is the kind of problem Scirizz is designed to take on.

Discuss the requirement →