Architecture
Where Quantlix sits in production
Quantlix is the AI Runtime Control Plane: a runtime layer in the request path between your application and AI models, tools, and knowledge sources, plus the management surface where you configure deployments, policies, providers, evals, and observability. Run it managed or self-hosted when your data boundary requires it.
Request lifecycle
- Your application calls Quantlix over HTTPS with JSON.
- Quantlix resolves the deployment, workflow, provider model, and policy configuration.
- Contracts, policies, redaction, and budget gates run before provider inference.
- Allowed requests are sent to the configured model, tool, retrieval source, or workflow node.
- Quantlix stores execution metadata, traces, policy decisions, and audit evidence.
Data flow
App -> Quantlix API / Workflow runtime -> Contract, policy, redaction, budget checks -> Provider model, retrieval source, or MCP tool -> Trace, audit event, model output
In workflow use cases, each node writes structured output to the execution payload. Downstream nodes read named fields such as question, raw_tickets.text, retrieval, or model_request.
Managed vs self-hosted
Managed
Quantlix hosts the control plane. You configure providers, deployments, workflows, policies, users, and audit exports through the portal and API.
Self-hosted
You run the API, portal, orchestrator, database, and supporting infrastructure in your own environment. This is the right path when data residency and network boundaries are strict.
What is stored
- Organizations, teams, projects, deployments, providers, and workflow definitions.
- Execution records, node executions, trace IDs, timing, status, and error payloads.
- Policy decisions, enforcement events, budget outcomes, and audit export metadata.
- Provider credentials are encrypted before storage.