Launch Layer is built for enterprises that require governed AI infrastructure — dedicated deployments, customer-controlled data boundaries, and full visibility into every model interaction.
No shared tenancy. No external data transfers. No SaaS trust assumptions. Your deployment, your network, your rules.
Every Launch Layer deployment is a dedicated instance running inside the customer's cloud or on-premise environment. The application, proxy layer, and all processing logic operate within your network boundary.
Runs in your AWS, Azure, GCP, or on-prem environment. You choose the region. You own the infrastructure. No shared compute, no shared storage, no multi-tenant exposure.
Updates are deployed when your team approves them. API keys are stored in your secrets manager. Infrastructure changes go through your change management process.
Internal access only
Dedicated instance
Scoped, validated calls
All processing runs inside your network. Only scoped AI API calls exit through your approved egress rules.
Documents are processed in memory inside your environment. Nothing is written to external disks, logged externally, or transmitted to Launch Layer. When the session ends, the data is gone.
API keys live in your secrets manager or environment variables — never in client-side code, browser storage, or network responses. Credentials are injected server-side at request time.
Launch Layer uses AI providers that do not train on API-submitted data. Your documents, prompts, and outputs are never retained by the model provider for training or improvement.
Authentication integrates with your identity provider via SSO. Access is scoped by role — every user sees only what their function requires.
Viewer — Read-only access to artifacts and dashboards.
Analyst — Upload documents, run AI modules, export outputs.
Manager — Configure templates, review audit logs, approve artifacts.
Admin — API key rotation, user provisioning, platform configuration.
Every action — document upload, AI call, artifact export, permission change — is logged with user identity, timestamp, and source IP. Logs feed directly into your SIEM or monitoring stack.
Server-side enforcement — Every API request is validated against the user's role before execution. Permissions are never enforced only in the UI.
Least-privilege by default — New users start with read-only access. Elevated permissions require explicit admin approval.
Session-scoped tokens — Tokens expire after a configurable period and cannot be reused across sessions.
Every AI interaction is scoped, validated, and routed through infrastructure your network team controls.
AI requests route through a server-side proxy inside your network. The proxy injects credentials, enforces token limits, and validates every request before it reaches the AI provider. No browser-side keys. No direct client-to-model connections.
System prompts are server-defined and constrained to transformation tasks. User-supplied content is treated as data, not instructions. Malformed or oversized payloads are rejected at the proxy layer. Follows OWASP LLM Top 10 guidance.
Each request enforces a maximum token count and a hard timeout. Limits are configured per endpoint and cannot be overridden by the client. This prevents cost overruns from adversarial inputs or unexpectedly large documents.
Your network team controls which AI provider endpoints are reachable. Restrict egress to specific IPs, domains, or regions. The platform supports VPN or private link routing to the AI provider if required.
The platform architecture is built to support formal enterprise security reviews today, with a defined path toward third-party certifications.
The architecture was designed from day one to support these certifications without requiring structural changes.
Request a deployment architecture walkthrough or schedule a security review with your team.