Inference and agents
Plan how your models run, how agents use approved tools, and how a task recovers when a call fails.
COMPANY PROFILE
Own your intelligence.
We are building managed inference and dedicated GPU services. Choose your models, agree data permissions and approve changes you control. We are building software to serve models and run agent tasks, alongside the GPU capacity those workloads need. Compute and managed services can be scoped separately.
Model improvement
Test your tasks against the current setup. Try retrieval, tools and routing before post-training; validate each new version and its model and data rights.
Dedicated compute
Define the GPU capacity, networking, location and operating support your workload needs.
Discuss a workload
Tell us about the task, models, expected usage and deployment needs. Begin with a non-sensitive brief.
Services are in development. We are discussing requirements and pilot scopes; availability and delivery timing are confirmed for each project.
Own your intelligence
Keep the tasks, approved examples and operating knowledge that make AI useful for your business.
Models
Compare open models and provider APIs on the same tasks. Keep your evaluation useful when the model changes.
Data
Agree what the system may access, where it is processed and whether it may be used for improvement. Access and training permissions are separate.
Deployment
For deployed models, agree infrastructure and operating responsibilities. For provider APIs, check the locations and controls the provider offers.
Releases
Approve changes you control, with tests and rollback. External API models and services follow the provider’s release and retirement policies.
Deployment control does not imply ownership of a third-party base model. Model licences, provider terms and data permissions apply.
Keep tasks, permitted examples and evaluation records reusable when models change. Compare the current and proposed setup on the same evidence before release.