Building a Conversational AI Agent
The most basic application is a stand-alone conversational AI Age
How to Build a Multi-Channel Conversational AI Agent in raia
🧭 Overview
The raia platform allows you to create Conversational AI Agents that work across Live Chat, SMS, Email, and Voice. You can deploy them as internal tools or public-facing assistants — with no code required.
Agents are built in Launch Pad, trained in Copilot, and launched/monitored in Mission Control.
🏗️ Phase 1 – Build in Launch Pad
1. Create the Agent
In Launch Pad, click Create Agent:
Choose a Role (e.g., Support, Sales, Analyst)
Set:
Public Name
Internal Name
Description
Tags
2. Define Agent Instructions
Instructions define your agent’s tone, logic, and scope.
Example:
You are a helpful AI assistant for ACME Corp. You answer questions from users across chat, SMS, email, and voice. Greet users warmly, keep answers short, escalate if unsure.
3. Upload Knowledge & Packs
Train your agent using:
Internal documents (PDFs, Docs, Sheets)
Knowledge base exports
Website content
Training Packs (reusable content bundles)
Use raia Academy to:
Clean and format raw content
Convert into structured Markdown or JSON
Push directly to the agent’s vector store
4. Enable Multi-Channel Skills
In the Skills tab, activate:
✅ Live Chat Skill
Customize chat widget
Set welcome message and branding
Embed on your site via HTML snippet
✅ SMS Skill
Assign number (via Twilio)
Set opt-in/out message flow
Use for support, outreach, or reminders
✅ Email Skill
Use
@raiabot.comor your domainSet reply signature and fallback messages
Auto-respond to inbound messages
✅ Voice Skill
Assign number
Add greeting, IVR options, and transcription
Use for inbound or outbound calling
5. Add Integrations via Skills
🔌 Webhook Skill
Push transcripts, metadata, or scores to:
CRM (e.g., Salesforce, HubSpot)
Ticketing system (e.g., Zendesk, Jira)
Internal databases
📬 Notification Skill
Send alerts via SMS or email to:
Notify internal teams when a conversation starts or ends
Route qualified conversations to human agents
🧠 Scoring Skill
Score each conversation:
Based on quality, accuracy, or qualification
Identify high-priority interactions for follow-up
🧪 Phase 2 – Train in Copilot
✅ Internal Testing
Team members ask questions using Copilot
AI responds with answers from its knowledge
Feedback loop:
Thumbs up/down
Edit answers
Mark as “Include in Training”
👤 Human Takeover
In live interactions, support staff can:
Take over conversations directly in Copilot
View history and continue the thread
Hand back to the AI when ready
🚀 Phase 3 – Deploy in Mission Control
1. Activate Skills
In Mission Control, set each channel’s status to Active:
Live Chat → embed and monitor
SMS → send and receive
Email → respond to inbound
Voice → answer and transcribe
2. Launch Outbound Campaigns (optional)
Upload contact list
Write message templates (SMS or Email)
Send via Mission Control
AI handles replies, qualifies leads, and scores conversations
3. Monitor & Optimize
View scores, tags, and summaries in Mission Control
Use filters to review top or low-performing threads
Refine training content weekly
🧠 Use Case Example: Omni-Channel Support Agent
Live Chat
Embedded on help site
SMS
Twilio number for 24/7 user questions
Connect [email protected]
Voice
Phone support with escalation routing
Notifications
Alert support team when agent stuck
Webhooks
Push transcripts into Zendesk
Scoring
Highlight unclear responses
Copilot
Real-time human handoff & training
✅ Setup Checklist
Agent role, name, and instructions defined
✅
Knowledge uploaded and Packs added
✅
Live Chat, SMS, Email, and Voice skills active
✅
Webhook + Notification + Scoring configured
✅
Copilot tested by internal team
✅
Conversation accuracy ≥95%
✅
Live deployment complete
✅
🔁 Ongoing Improvement Tips
Review Copilot feedback weekly
Update training Packs regularly via raia Academy
Monitor scores to detect weak answers
Adjust escalation logic as needed
Rotate human reviewers for objective feedback
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