Choose a model you can run.
For engineering teams adopting open-weight AI
Build a model registry that takes a team from an overwhelming list of releases to a practical deployment choice. Compare licences, memory requirements, task evaluations, and setup notes before committing hardware or engineering time.
Where AI helps. The registry helps teams select AI models using documented requirements and task evaluations. It does not assume every open-weight licence permits every use.
- Filter against available hardware
- Review licence terms before adoption
- Reproduce the documented setup
A way to earn: Sell private team registries and curated model collections.
A focused first release: Document one model category with hardware requirements and repeatable deployment recipes.
The customer leaves with: A shortlist with licence terms, memory requirements, and a deployment recipe for each candidate.
Deploy your chosen model.
For developers building AI applications
Build a hosting service that turns an approved open-weight model into a managed endpoint. Customers keep visibility into the model version, serving configuration, usage, and operating costs.
Where AI helps. The service runs customer-selected AI models and manages inference infrastructure.
- Deploy the version the team approved
- Inspect the live serving configuration
- Monitor usage and endpoint health
A way to earn: Charge for compute usage and managed endpoint plans.
A focused first release: Host a small, documented set of models on one infrastructure provider.
The customer leaves with: A managed endpoint with a pinned model version, inspectable configuration, and usage records.
See what fine-tuning changed.
For teams adapting AI to a specific task
Build a fine-tuning workbench that connects training data to measured task performance. Help teams compare the base and adapted models before adopting a change, then export the model and the evidence behind it.
Where AI helps. The workbench trains AI models on customer data and measures their performance against a separate evaluation set.
- Keep training and evaluation data separate
- Compare both models on the same tasks
- Export the model with its experiment history
A way to earn: Charge for training compute and collaborative workspaces.
A focused first release: Support one training method with a held-out evaluation and an exportable model.
The customer leaves with: A base-versus-adapted comparison on held-out tasks, with the trained model and run records available to export.
