AI Chatbot Onboarding Checklist
Everything you need to launch successfully
Launching an AI chatbot successfully requires a structured onboarding process. Vatdi simplifies onboarding with a no-code platform, but following this checklist ensures you cover every base: content preparation, training, customization, testing, launch, and optimization. Use it to go from zero to live with confidence.
Pre-Launch Checklist
Before going live, complete these steps: define your chatbot goals, gather training content (FAQs, docs, product data), upload to Vatdi, customize the widget appearance, configure lead capture fields, set up human handover rules, and test with 20-30 sample questions.
Launch Day Checklist
On launch day: embed the widget on your site, verify it loads correctly on desktop and mobile, test the lead capture form, confirm human handover routing works, and brief your support team on the AI handover flow. Start with high-traffic pages first.
Post-Launch Optimization
After launch: review conversations daily for the first week, identify questions the AI struggles with, add missing content to the training data, adjust handover triggers if needed, and set up weekly review cadence for ongoing optimization.
Key Benefits
Ensure nothing is missed during chatbot setup
Brief your team on AI-to-human handover procedures
Establish a post-launch optimization routine
Use as a template for rolling out chatbots to new sites
Frequently Asked Questions
Basic onboarding with Vatdi takes under 5 minutes. A thorough onboarding following this full checklist takes 1-3 days including content preparation and testing.
Launching without enough training data. Invest time in uploading comprehensive FAQs, product docs, and policies before going live.
Absolutely. Brief your team on how handover works, what the AI handles, and when they will receive escalated conversations.
Use the Vatdi preview panel to ask 20-30 common customer questions. Verify the answers are accurate and the handover flow works correctly.
Review every conversation for the first week. Look for incorrect answers, missing content, and unexpected questions. Refine training data based on findings.
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