Data & training design
Dataset auditing, train/validation/test strategy, leakage detection, class imbalance, labeling strategy, synthetic data, augmentation, sampling, curriculum design, and reproducible experiment plans.
AI / ML engineering
Train the model. Optimize the system.
Scirizz handles the full AI/ML lifecycle: dataset and experiment design, custom training, foundation-model fine-tuning, rigorous evaluation, model compression, quantization, inference acceleration, and deployment to cloud, on-prem, air-gapped, edge, workstation, or HPC environments.
End-to-end capability
More than an API wrapper.
We work on the model itself, the training process, the evaluation methodology, and the systems software needed to make it fast and reliable on the target hardware.
Custom model training
PyTorch model development, transformers, CNNs, GNNs, multimodal architectures, time-series models, scientific ML, transfer learning, hyperparameter optimization, checkpointing, and distributed training.
Fine-tuning & adaptation
SFT, LoRA, QLoRA, PEFT/adapters, domain adaptation, task adaptation, instruction tuning, retrieval integration, preference optimization where appropriate, and customer-data private training.
Evaluation & reliability
Task-specific benchmarks, confusion/error analysis, calibration, uncertainty, ablations, robustness, drift, hallucination and failure analysis, regression suites, and statistically defensible comparisons.
Compression & efficiency
Knowledge distillation, structured and unstructured pruning, sparsity, low-rank methods, mixed precision, FP8/INT8/INT4 quantization, PTQ/QAT, and accuracy-versus-cost studies.
Inference & deployment
ONNX export and graph optimization, TensorRT, OpenVINO, CUDA, batching, memory tuning, KV-cache optimization, model serving, CPU/GPU/NPU selection, edge deployment and private infrastructure.
Training infrastructure
Scale experiments without losing control.
For models that outgrow a single workstation, Scirizz can engineer the training and experiment infrastructure around the workload rather than forcing the workload into a generic MLOps template.
Single & multi-GPU
GPU memory profiling, gradient accumulation, mixed precision, DDP/FSDP-style sharding strategies, efficient checkpointing, resume/recovery, utilization analysis, and distributed bottleneck diagnosis.
Experiment & data discipline
Versioned datasets, deterministic pipelines, experiment tracking, metric design, reproducibility, automated sweeps, Bayesian/evolutionary hyperparameter search, and model/data lineage.
Private / air-gapped AI
Self-hosted model training and inference, local model registries, private RAG, isolated GPU servers, secure artifact handling, no-data-egress architectures, and controlled software supply chains.
Cloud, HPC & clusters
Workload-aware GPU selection, schedulers, containers, batch jobs, multi-node training, storage/data throughput, cost-performance tuning, and transition between local, cloud, and HPC environments.
Model families
AI for technical domains, not only text.
Our strongest fit is where the model has to understand difficult data, scientific constraints, specialized sensors, or domain-specific objectives.
LLMs, VLMs & agents
Domain adaptation, private assistants, tool-using agents, multimodal reasoning, RAG, evaluation, security, latency and cost optimization.
Computer vision & sensing
Detection, segmentation, tracking, EO/IR, multimodal fusion, hyperspectral workflows, edge vision, synthetic-data training and model acceleration.
Scientific ML
PINNs, neural operators, surrogate models, inverse problems, differentiable modeling, reduced-order models, hybrid physics/ML and uncertainty-aware prediction.
Biotech & bioinformatics AI
Omics, sequence and structure models, literature intelligence, knowledge graphs, multimodal biological data, scientific agents and reproducible analysis.
Cybersecurity ML
Anomaly detection, model and agent security, malicious-code analysis, vulnerability prioritization, security classifiers, behavioral models and private inference.
Tabular, time-series & optimization
Forecasting, anomaly detection, surrogate objectives, Bayesian optimization, evolutionary search, constrained ML and decision-support models.
Hardware-aware AI
Accuracy is only one objective.
A production model also has to fit memory, latency, throughput, power, hardware, privacy, and deployment constraints. We benchmark those tradeoffs explicitly and can optimize from PyTorch down through ONNX, runtime graphs, kernels and target accelerators.
Typical engagements
Bring the model problem, not a predetermined solution.
“Our model is not accurate enough.”
We audit the data, labels, architecture, loss, training dynamics and evaluation design, then run targeted experiments to find the limiting factor.
“We need to fine-tune privately.”
We design an on-prem or controlled fine-tuning workflow around your data, compute and security constraints and validate whether full fine-tuning, LoRA/QLoRA or another adaptation method is justified.
“It is too slow or too expensive.”
We profile the workload and test quantization, distillation, pruning, batching, graph/runtime optimization and alternative hardware to reduce latency, memory or cost while measuring accuracy impact.
“We need this on an edge device.”
We adapt the model to the target compute envelope, validate numerical/accuracy changes, optimize preprocessing and runtime, and produce a reproducible deployment benchmark.
Model engineering
Have a training, accuracy, GPU, or inference problem?
Send us the model, target metric, dataset constraints, current benchmark, and target hardware. We can start from the bottleneck.
Start an AI/ML project →