Build, Own, DeployYour Enterprise LLM
Fine-tune open-weight models on your data, deploy them as private endpoints, and take the weights with you.
No black box. No shared model. No lock-in.
From dataset to a model you own
A handful of API calls take a JSONL file to a fine-tuned model served behind an OpenAI-compatible endpoint. Prefer to drive the training loop yourself? Forward-backward passes and optimiser steps are first-class API calls too.
Explore documentation- Step 01
Upload a dataset
Bring a JSONL file of examples. That's the only prerequisite.
- Step 02
Fine-tune a model
Pick an open-weight base and start a managed training job with one call.
- Step 03
Serve it
Serve any checkpoint as a private endpoint the moment training finishes.
- Step 04
Use your OpenAI client
Point your existing SDK at a new base URL. Nothing else changes.
- Step 05
Export the weights
Download any checkpoint. The model runs wherever you need it.
Built with Nucleus
Explore demos our team has built with Nucleus across different industries.
Private models
For every team
Fine-tune, evaluate, serve, and export models your organisation owns, with a workflow for every team that touches them.
Explore the docsML Engineers
Fine-tune and deploy custom models on your enterprise data with full control over training pipelines and inference endpoints.
Research Teams
Drive the training loop call by call. RL, DPO, distillation, and custom losses, with the GPUs handled for you.
Product Teams
Ship features on models that speak your domain. Keep your existing OpenAI or Anthropic client code and swap the base URL.
Data Teams
Turn proprietary datasets into evaluated, versioned models with managed fine-tuning jobs. Upload a JSONL file, get back a private endpoint.
Platform Teams
Serve models behind OpenAI- and Anthropic-compatible endpoints, with usage tracking and rate limits built in.
Enterprise Leaders
Own your AI strategy. Training data stays in your account, weights are exportable, and models can run wherever policy requires.