Control AI actions before they reach your tools, files, code, and systems.
JintellarCore sits in the execution path for AI workflows, applying policy, routing, approval, and evidence capture before work continues.
Runtime operations console
A control surface for workflow runs, approvals, routing posture, evidence, and operational state.
Core runtime loop
Policy before execution. Proof after execution.
The runtime gives AI workflows a controlled path from request to action to review, so teams can move beyond chat without losing control of what the system is allowed to do.
Classify request and context
Check policy and permissions
Route model or tool access
Require approval or block when needed
Execute within allowed boundaries
Preserve evidence and replay history
Runtime capabilities
Control the paths where AI work becomes real action.
JintellarCore focuses on the points where regulated teams need confidence: routing, permissions, approvals, execution boundaries, evidence, and deployment fit.
Data and model routing
Route work by sensitivity, model policy, deployment boundary, and allowed provider path.
Tool and action control
Constrain tool, API, file, code, and workspace actions before the workflow proceeds.
Human approval gates
Require reviewer approval for sensitive, high-impact, or policy-controlled steps.
Workspace and code boundaries
Run technical work inside approved workspaces with clear command and artifact limits.
Evidence and replay
Attach decisions, approvals, outputs, and artifacts to the same workflow record.
Deployment flexibility
Support workflow packages, private environments, and integration paths around existing systems.
AI Turn Case File
A case file for every governed AI run.
Each run connects prompts, retrieved context, model routes, tool calls, approvals, outputs, artifacts, and policy decisions into one reconstructable record.
Evidence case file
Review the connected record of prompts, context, routes, tool calls, approvals, outputs, artifacts, and decisions.
Deployment modes
Adopt the control layer at the level the workflow needs.
Teams can begin with one packaged workflow, integrate the runtime into existing AI execution paths, or deploy privately for sensitive environments.
Workflow package
Start with one repeatable business process, the required controls, and the evidence reviewers need.
Runtime integration
Place the control layer in the execution path for AI workflows that already touch tools or systems.
Self-hosted / private environment
Run sensitive workflows where your security, data, and deployment requirements are strongest.
Sidecar/tool-control mode
Where full proxying is not practical, govern specific tools, actions, workspaces, or workflow steps.
Differentiation
Built for AI actions, not just AI prompts.
Prompt logging is not enough once AI is allowed to retrieve, call, edit, run, approve, or publish. The control path has to sit before action, then keep proof connected to the workflow.
Give AI a controlled path to act.
Start with the workflows where action, approval, and evidence matter most, then expand the control layer across teams and systems.