Interactive static demo · fictional data
LLM workflow cockpit.
Click through a simulated AI assistant pipeline. The page demonstrates practical LLM product skills: routing, RAG, tool calls, guardrails, evaluations, and explainable traces.
Demo note: this is a static browser demo with fictional requests and mock outputs. It is designed to show the engineering pattern without exposing private keys or real user data.
Incoming requestSummarize messages, check calendar, draft a safe reply.received
WorkflowRoute → retrieve → plan tools → generate → evaluate.ready
Risk gateRequires approval before sending external messages.human
92%grounded confidence
2planned tool calls
5eval checks
Intent router
The router turns a vague request into a structured workflow decision. This is where an AI system chooses whether it needs retrieval, tools, approval, or a simpler direct answer.
User: "I have school, bills, and a website update. Tell me what matters today and draft the first message, but don't send anything without asking me."
Structured route
{
"intent": "multi_step_assistant_workflow",
"domains": ["school", "finance", "website"],
"needs_retrieval": true,
"needs_tools": true,
"risk_level": "medium",
"approval_required": true,
"response_style": "concise action plan"
}