AI engineering inside your security perimeter
Cosine brings agentic software engineering into environments where source code, prompts, outputs, and model activity cannot leave the boundary.
Built for controlled deployment
Cosine can support different deployment models depending on the organization’s security, infrastructure, and governance requirements.
No data egress
Run Cosine where private code and prompts stay inside the boundary you approve.
Model choice
Use Lumen, supported open-weight models, or customer-approved post-trained models.
Auditability
Keep visibility into agent actions, tool use, changes, and review status.
Human review
Every change remains visible, testable, and subject to customer review.
What runs inside the boundary?
Cosine’s air-gapped architecture is designed to bring the core AI engineering loop into the controlled environment.
Inside the boundary, Cosine can support:
Coding agent workflows
Repository exploration
Planning and task execution
File edits and reviewable diffs
Test and command execution
Model inference
Engineering workflow orchestration
Team usage patterns
Security-sensitive review workflows
Bring AI engineering into your secure environment.
Air-gapped FAQ
Air-gapped means running Cosine inside an isolated environment where AI engineering workflows can operate without requiring private source code, prompts, or outputs to leave the boundary.
For air-gapped deployments, the goal is to keep source code and engineering context inside the controlled environment. Exact architecture and data-flow details should be confirmed through a deployment review.
Hardware depends on model choice, concurrency, repository size, and evaluation requirements. Engagement with Cosine starts with an architecture review.
Yes. Cosine can support on-prem and controlled-environment deployment discussions for organizations with strict security or infrastructure requirements.
Yes. Cosine can support Lumen, open-weight models, or customer-approved post-trained variants.
Timing depends on security review, infrastructure readiness, model choice, and evaluation scope.