PyTorch’s dynamic computation graph is ideal for fast research and custom architectures. We prototype quickly, then harden promising ideas into maintainable modules. This lets you innovate without sacrificing reliability later.
We fine-tune foundation models with techniques like LoRA and QLoRA, and run efficient distributed training when datasets grow. For generative tasks, we deliver stable diffusion pipelines tuned to your brand and safety needs. Tooling such as Lightning and Weights & Biases keeps experiments reproducible.
Serving is handled with TorchServe or lightweight FastAPI wrappers depending on latency needs. Quantisation and tensor RT acceleration reduce footprint without degrading quality. Monitoring closes the loop so models improve as real-world data arrives.
