Engineering intelligence into real products.Make It Possible.

Large language models that read and write text; we build assistants, agents and automation on them.

Retrieval-augmented generation: the model answers from your own documents, with sources, not from memory.

Open-source deep-learning framework; we use it to train, fine-tune and export custom models.

Google's machine-learning framework; we train models with it and deploy them to servers, browsers and phones.

Hub and libraries for open models; we use them to pick, fine-tune and run open-weight models.

Framework for chaining prompts, tools and retrieval; we use it to wire up RAG pipelines and agents.

High-throughput LLM serving engine; we use it to run open models fast and cheaply on your GPUs.

Runs open models locally with one command; we use it for private prototypes and on-premises assistants.

llama.cppOn-device inference

Lean C/C++ inference for quantised models; we use it to run LLMs on CPUs and edge devices.

Python's classic machine-learning toolkit; we use it for classification, regression, clustering and quick baselines.

Fast multi-dimensional arrays for Python; the numerical foundation under our data and ML code.

Fast DataFrame library written in Rust; we use it to clean and reshape large datasets in Python.

Open-source computer-vision library; we use it to resize, clean and analyse images and video frames.

Real-time object-detection models; we train them to find and count objects in images and live video.

Classic Python toolkit for language data; we use it to tokenise, tag and clean text before modelling.