Custom Neural Networks
Architecture design and training — CNNs, Transformers, RNNs and custom architectures for your specific task and data.
AI/ML
PyTorch Development
🔥 Deep learning & ML research built with PyTorch
We build and train PyTorch models for production — computer vision, NLP, time-series and custom neural networks. Research-to-production, end to end.
PyTorch 2.xtorch.compile
CUDA / GPUAccelerated training
ProductionServing & monitoring
What We Build
What we build with PyTorch
Computer Vision
Object detection, segmentation, classification and OCR models — YOLO, EfficientDet, ViT and custom architectures.
NLP & Language Models
Text classification, NER, sentiment analysis, summarisation and custom LLM fine-tuning with PyTorch + Hugging Face.
Time-Series & Forecasting
Demand forecasting, anomaly detection and predictive maintenance — LSTM, Transformer and custom time-series models.
Research to Production
Take a research notebook to a production ML pipeline — optimised inference, ONNX export and serving infrastructure.
Model Optimisation
torch.compile, quantisation, pruning and TensorRT optimisation — faster inference at lower cost.
Deliverables
Every PyTorch project includes
✓PyTorch 2.x with torch.compile
✓Hugging Face Transformers integration
✓CUDA-accelerated training pipeline
✓W&B or MLflow experiment tracking
✓ONNX export for cross-platform serving
✓TorchServe or FastAPI model serving
✓GPU instance setup (A100/H100)
✓Model monitoring and drift detection
FAQ
Common questions
PyTorch or TensorFlow — which do you recommend?
PyTorch for most new projects — more Pythonic, dominant in research and the Hugging Face ecosystem is PyTorch-native. TensorFlow when TFLite mobile deployment or TF Serving ecosystem is needed.
Can you fine-tune a pre-trained model on our data?
Yes — we fine-tune Hugging Face models (BERT, RoBERTa, Llama, Mistral) on your labelled data using PyTorch and LoRA/QLoRA.
How much GPU compute do we need?
Depends on model size and dataset. We model the compute cost before you commit and recommend cloud GPU instances or on-prem hardware.
Can you convert our PyTorch model to run on mobile?
Yes — via ONNX export + Core ML (iOS) or TFLite conversion + Android NNAPI, or using TorchScript for mobile.
Ready to build with PyTorch?
The researcher's framework, production-ready — PyTorch for serious ML engineering.
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Ready to start your project?
Tell us what you want to build. We reply within 4 business hours.
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