Assistants

Topic 
 Detail 
 
 
 
 
 Navigation 
 Admin → Contact Center → Assistants 
 
 
 Purpose 
 AI-powered virtual agents (bots) using NLU to handle voice and digital interactions automatically 
 
 
 Technology 
 Natural Language Understanding (NLU) 
 
 
 Integration 
 Works with Knowledge Base and Architect flows 
 
 
 
 
 Overview 
 Assistants are AI-powered automation tools that enable organizations to build virtual agents capable of interacting with customers through voice and digital channels. They use Natural Language Understanding (NLU) to interpret customer requests and respond using knowledge articles, intents, and Architect flows. 
 Assistants are most effective for automating predictable, repetitive interactions — reducing call volume, improving self-service, and lowering operational costs. 
 
 
 Assistant Components 
 
 
 
 Component 
 Description 
 
 
 
 
 Intents 
 The goal or purpose of the customer's request (e.g., Check_Order_Status, Reset_Password) 
 
 
 Utterances 
 Example phrases customers might say to express an intent — used to train the NLU model 
 
 
 Entities 
 Variables extracted from customer input (e.g., order number, city, product name) 
 
 
 Slots 
 Structured data fields collected during a conversation to fulfill an intent 
 
 
 Actions 
 Responses or operations the assistant performs when an intent is matched 
 
 
 Knowledge Integration 
 Allows the assistant to answer questions directly from knowledge base articles 
 
 
 Architect Flow Integration 
 Transfers conversation control to an Architect flow for advanced routing or logic 
 
 
 
 
 Configuration Settings 
 
 
 
 Option 
 Description 
 
 
 
 
 Language 
 NLU processing language — determines which utterance model is used 
 
 
 Confidence Threshold 
 Minimum confidence score required to trigger an intent — below this, fallback intent fires 
 
 
 Fallback Intent 
 Default action when no intent is recognized (e.g., transfer to agent) 
 
 
 Disambiguation 
 Prompts the customer to clarify when multiple intents closely match 
 
 
 
 
 Example Assistant Flow 
 Customer: "I want to check my order status"
 ↓
Intent Detected: Order_Status
 ↓
Extract Entity: Order_Number
 ↓
Call API via Data Action / Architect Flow
 ↓
Return Response to Customer
 ↓
(If unresolved) Escalate to Human Agent
 
 
 Best Use Scenarios 
 
 
 
 Scenario 
 Description 
 
 
 
 
 Customer Self-Service 
 Resolve common issues without agent involvement 
 
 
 FAQ Automation 
 Answer frequently asked questions automatically using knowledge articles 
 
 
 Order / Account Status 
 Retrieve order status, balance, or appointment info via API 
 
 
 Call Routing by Intent 
 Identify customer intent and route to the correct queue 
 
 
 After-Hours Support 
 Provide automated assistance when agents are unavailable 
 
 
 
 
 Integration Points 
 
 
 
 Integration 
 Description 
 
 
 
 
 Architect Flows 
 Advanced routing or automation logic triggered by the assistant 
 
 
 Knowledge Base 
 Automated responses using knowledge articles — improves containment rate 
 
 
 Digital Channels 
 Chat, messaging (WhatsApp, SMS), and Web Messaging 
 
 
 Voice Channels 
 Voice bots for IVR interactions — speech recognition + NLU 
 
 
 Data Actions 
 API calls triggered by the assistant to retrieve or update external data 
 
 
 
 
 Best Practices 
 
 
 
 Practice 
 Recommendation 
 
 
 
 
 Start with high-volume intents 
 Automate the most frequent customer requests first for maximum impact 
 
 
 Keep intents simple 
 Avoid overly complex intent structures that reduce NLU accuracy 
 
 
 Use knowledge articles 
 Well-written articles dramatically improve bot containment rate 
 
 
 Train with real customer data 
 Use actual customer utterances — not hypothetical ones 
 
 
 Monitor analytics continuously 
 Review bot performance, confidence scores, and fallback rates regularly 
 
 
 Provide easy escalation 
 Always ensure customers can reach a human agent quickly when needed 
 
 
 
 
 
 
 Interview Cheat Sheet 
 
 
 
 Question 
 Answer 
 
 
 
 
 What technology do Assistants use? 
 Natural Language Understanding (NLU) 
 
 
 What is an Intent? 
 The goal or purpose of the customer's request 
 
 
 What are Utterances used for? 
 Training the NLU model with example customer phrases 
 
 
 What is an Entity? 
 A variable extracted from customer input (e.g., order number) 
 
 
 What happens when confidence is below the threshold? 
 The Fallback Intent fires — typically escalates to a human agent 
 
 
 What is Disambiguation? 
 Prompts the customer to clarify when multiple intents closely match