> ## Documentation Index > Fetch the complete documentation index at: https://api.agentflow-enterprise.com/llms.txt > Use this file to discover all available pages before exploring further. # AgentFlow Enterprise Quickstart: Run Your First Lead > Set up AgentFlow Enterprise and send your first lead through the full AI qualification pipeline, from intake form to CRM handoff in minutes. AgentFlow Enterprise moves inbound leads from raw signal to qualified action across five structured stages: Lead Intake, AI Qualification, Operator Review, Billing Readiness, and CRM/Workflow Handoff. This guide walks you through exploring the live demo, requesting access, configuring your first intake channel, and submitting a test lead so you can see every stage fire in sequence before you go to production. AgentFlow Enterprise is a production-conscious SaaS foundation. Every provider account, intake channel, and workflow rule requires configuration before you use it commercially. Work through the steps below to understand what is already in place and what you still need to set up for your specific deployment. ## What you'll accomplish By the end of this guide you will have: * Explored the live demo workflow end-to-end * Requested access and booked an onboarding briefing * Configured at least one lead intake channel (form or webhook) * Submitted a test lead and observed it progress through all five pipeline stages * Confirmed operator review and handoff behavior before touching production traffic *** Visit the public demo at [agentflow-enterprise.com/demo](https://agentflow-enterprise.com/demo) before you configure anything. The demo lets you walk through the intended workflow experience—submitting a sample lead, watching the AI qualification run, and seeing the operator review panel—without touching any real data or provider accounts. Pay attention to: * How the intake form maps lead fields to qualification criteria * The confidence score and structured output the AI produces * The operator review screen where a human makes the final routing decision * The billing readiness prompt and the downstream handoff confirmation **What to expect:** The demo runs on shared infrastructure and is designed for evaluation, not load testing. Some provider calls may be stubbed or rate-limited in the public environment. AgentFlow Enterprise is not a self-serve sign-up. Access is granted through a direct engagement to make sure your deployment is scoped correctly from day one. You have two ways to start: Fill in the contact form on the main site to describe your use case—agency delivery, SaaS licensing, or acquisition review—and the team will respond with next steps. Schedule a direct call to discuss your pipeline requirements, provider setup, and any integration constraints before configuration begins. **What to expect:** You will receive a private environment invitation, provider configuration notes, and a scoped onboarding plan based on your workflow requirements. Bring details about your CRM, expected lead volume, and any existing webhook infrastructure to the briefing. Once you have access to your private environment, set up the channel through which leads enter the pipeline. AgentFlow supports two intake methods: The built-in intake form captures lead fields—name, company, use case, and any custom qualification criteria you define—and passes them directly to the AI Qualification stage. 1. Open the **Intake** section in your operator dashboard. 2. Enable the form and map each field to the qualification schema for your use case. 3. Publish the form to your domain or embed it in an existing landing page. 4. Test submission with a disposable email address before going live. Add at least one qualifying question—budget range, company size, or intended use case—so the AI has enough signal to produce a meaningful confidence score on the first pass. If leads already arrive from a third-party source (a CRM, a landing page builder, or an external form), configure a webhook to push them into AgentFlow instead of using the built-in form. 1. Open the **Integrations** section in your operator dashboard and copy your unique inbound webhook URL. 2. In your external source, add a POST action that sends lead data to that URL on every new submission. 3. Map the incoming JSON fields to AgentFlow's qualification schema using the field mapping editor. 4. Enable signature verification in both systems to ensure only authorized sources can push leads. Always enable webhook signature verification. AgentFlow treats inbound webhook events as a trust boundary—unverified payloads are rejected before they reach the qualification engine. With your intake channel live, submit a test lead and observe each pipeline stage in the operator dashboard. Use realistic but fictitious data—a plausible company name, a specific use case, and a budget range—so the AI qualification engine has enough context to produce a representative output rather than a default fallback. **What to expect at each stage:** AgentFlow captures the submitted fields, timestamps the record, and stores it securely. The lead appears in your dashboard under **Incoming** with a status of `pending_qualification`. No human action is required here—the pipeline continues automatically. OpenAI processes the lead fields against your qualification criteria and returns a structured output: a confidence score, a fit label (strong / moderate / weak), and a plain-language qualification summary. This takes a few seconds. The result is attached to the lead record and visible in the **Qualification** column of the operator dashboard. The lead moves to **Awaiting Review** and appears in the operator queue. As the reviewing operator, you see the raw lead data alongside the AI's qualification output. You then approve, flag for follow-up, or reject the lead. Every decision is logged with a timestamp and operator ID for audit purposes. Approved leads trigger the billing readiness check. If your deployment is configured with Stripe or PayPal, this stage surfaces the relevant checkout or invoice action. In a test run, the billing step will display in the dashboard but will not initiate a real charge unless you are operating in live mode with a verified payment provider. The qualified, reviewed lead is packaged and dispatched to your configured downstream system—a CRM, a Slack notification, a webhook endpoint, or a Google Sheets row. The handoff is operator-controlled: you define the trigger condition and destination in the **Integrations** panel before this stage fires. *** ## Next steps Now that you have seen a lead travel the full pipeline, move on to the deeper guides: Understand how all five pipeline stages connect, which integrations power each one, and how the system is architected for operator control. Configure OpenAI qualification criteria, tune confidence thresholds, and review structured output formats for your specific lead type. Set up inbound and outbound webhooks with signature verification so your existing tools push leads in and pull qualified results out. Package AgentFlow as an AI RevOps foundation for client delivery, including branding, provider isolation, and commercial licensing considerations.