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Calculus AI for Support Team
Feature Availability
Vtiger Editions: One Growth | One Professional | One Enterprise | One AI
Introduction
Calculus AI for Customer Support helps support teams deliver faster, smarter, and more consistent customer service by using artificial intelligence to analyze customer interactions, automate responses, and provide actionable insights. It continuously learns from historical cases, chats, emails, and calls to improve resolution quality and customer satisfaction.
By reducing manual effort and highlighting what needs attention, Calculus AI enables support agents to resolve issues efficiently and helps managers optimize team performance and service quality.
Benefits of Using Calculus AI for Customer Support
The benefits of using Calculus AI for Customer Support are:
- Faster case resolution: Automates responses and highlights relevant information for quicker issue handling.
- Improved response quality: Analyzes conversations to help agents provide accurate and complete replies.
- 24/7 customer assistance: Uses AI-powered chatbots to answer common queries at any time.
- Reduced agent workload: Offloads repetitive questions and routine tasks to AI.
- Enhanced customer satisfaction: Identifies sentiment and intent to deliver empathetic and timely support.
- Data-driven coaching: Provides insights that help managers improve agent performance.
Ask Calculus AI for Customer Support
Ask Calculus AI allows support users to interact with the CRM using natural language commands. Agents and managers can quickly retrieve information, summarize cases, or take action without navigating multiple screens.
Support teams can use Ask Calculus AI to:
- Summarize case details
- Retrieve customer interaction history
- Identify pending or overdue cases
- Create follow-up tasks or activities
Examples:
- Summarize this case
- Show my pending cases
- Create a follow-up task for this case tomorrow
Calculus AI in Cases
Calculus AI in Cases assists support agents by generating contextual responses based on case history, customer interactions, and knowledge sources.
It allows agents to:
- Insert AI-generated responses while replying to cases
- Shorten or refine responses for clarity
- Ensure consistent and accurate communication
This helps agents respond faster while maintaining high-quality support.
Calculus AI in Live Chats
Calculus AI in Live Chats helps agents respond quickly and effectively during customer conversations. While chatting, agents can insert AI-generated replies directly into the chat window.
Agents can:
- Generate instant responses to customer questions
- Shorten or refine replies for better clarity
- Maintain consistent tone and messaging
This reduces response time and improves the live chat experience for customers.
Chatbot
The Chatbot is an AI-powered assistant that interacts with website visitors and customers using natural language. It understands user queries and provides relevant answers by referring to FAQs, articles, and configured knowledge sources.
The Chatbot can:
- Answer frequently asked questions
- Provide instant information to customers
- Reduce the number of incoming support tickets
This enables support teams to focus on complex issues while the chatbot handles routine queries.
Conversation Analysis for Customer Support
Calculus AI analyzes customer conversations across emails, chats, and phone calls to identify patterns, issues, and improvement areas.
Conversation analysis helps:
- Understand customer intent and concerns
- Identify frequently asked questions
- Improve agent communication quality
- Detect conversations that need managerial review
Email Analysis
Calculus AI analyzes incoming and outgoing emails to improve support communication.
- Acts as an email reply assistant
- Suggests relevant responses and templates
- Identifies grammatical issues and incomplete replies
- Ensures accurate and consistent messaging
This helps agents resolve issues faster and with fewer follow-ups.
Voice Analysis
Voice Analysis uses AI and speech recognition to analyze recorded support calls.
It provides insights such as:
- Talk-to-Listen Ratio: Ensures agents listen effectively
- Mood Detection: Identifies agent tone and customer emotion
- Rate of Speech: Evaluates clarity and pacing
- Action Items: Detects next steps discussed during calls
Calls requiring attention are automatically flagged for manager review.
Sentiment Analysis for Customer Support
Calculus AI identifies customer sentiment from emails and calls and categorizes it as positive, neutral, or negative. Sentiment is represented visually, making it easy to assess customer mood.
Negative sentiment helps support teams:
- Identify dissatisfied customers early
- Prioritize urgent cases
- Improve response strategy and empathy
Signals
Signals categorize spoken and written content from support conversations using Natural Language Processing (NLP). They help identify recurring issues, complaints, or keywords in customer interactions.
Support managers can use signals to:
- Detect recurring customer problems
- Monitor escalation patterns
- Improve FAQ and knowledge base content
Dashboards for Support Performance
Calculus AI provides dashboards that help track support team performance and enable continuous improvement.
Coaches Dashboard (For Support Managers)
The Coaches Dashboard helps managers review agent interactions and evaluate performance.
It displays:
- Calls and emails that need review
- Talk-to-Listen ratio analysis
- Mood and speech rate charts
- Performance trends over time
- Coaching suggestions for agents
Players Dashboard (For Support Agents)
The Players Dashboard helps agents track their performance and identify improvement areas.
It shows:
- Interaction summaries
- Manager reviews and ratings
- Coaching materials and recommendations
Agents can also request managers to review specific emails or calls.
Generative AI Designer for Support
The Generative AI Designer allows admins to manage and train AI responses used across Live Chat, Cases, and Chatbot interactions. It centralizes all GPT-related queries and responses in one place.
Support teams can improve AI accuracy by:
- Reviewing chatbot responses
- Updating FAQs and articles
- Refining source documentation