AI-Powered Customer Service

AI is transforming customer service through intelligent chatbots, sentiment analysis, and automated ticket routing. Companies achieve 40-60% cost reduction, 90% faster response times, and 25-35% higher satisfaction scores.

Core Capabilities

1. AI Chatbots & Virtual Agents

• 24/7 Availability: Answer questions anytime, any language
• Intent Recognition: Understand customer needs from natural language
• Context Awareness: Remember conversation history and user data
• Seamless Handoff: Transfer complex issues to human agents
• Automation Rate: Handle 60-80% of routine inquiries without human intervention

2. Sentiment Analysis

• Real-time Emotion Detection: Identify frustrated/angry customers
• Priority Routing: Escalate negative sentiment to senior agents
• Quality Monitoring: Analyze agent performance and customer satisfaction
• Trend Analysis: Identify systemic issues from conversation patterns

3. Intelligent Ticket Routing

• Category Classification: Auto-categorize tickets by topic
• Skills Matching: Route to agents with relevant expertise
• Priority Scoring: Rank tickets by urgency and impact
• Load Balancing: Distribute tickets evenly across agents

4. Knowledge Base Enhancement

• Auto-Generate Articles: Create KB articles from resolved tickets
• Answer Suggestions: Recommend KB articles to agents
• Gap Analysis: Identify missing knowledge base content
• Search Optimization: Semantic search for better article discovery

Technology Stack

• Chatbots: GPT-4, Claude, Dialogflow, Rasa, custom fine-tuned models
• Sentiment: BERT, RoBERTa, DistilBERT, AWS Comprehend
• Speech: Whisper, Azure Speech, Google Speech-to-Text
• Integration: Zendesk, Freshdesk, Salesforce Service Cloud, Intercom

Implementation Phases

Phase 1: Pilot (4-6 weeks)

• Deploy chatbot for top 20 FAQs
• Test with 10% of traffic
• Measure automation rate and satisfaction

Phase 2: Expansion (6-8 weeks)

• Add sentiment analysis and routing
• Integrate with CRM and knowledge base
• Scale to 50% of inquiries

Phase 3: Optimization (ongoing)

• Continuous training on new conversations
• Add new intents and capabilities
• A/B test improvements

Pricing

• Small Business: ₹5-12L/year (5K-20K tickets/month)
• Mid-Market: ₹20-50L/year (50K-200K tickets/month)
• Enterprise: ₹80L-3Cr/year (500K+ tickets/month, custom development)

Case Study: SaaS Company

• Scale: 30K support tickets/month, 25-person support team
• Solution: AI chatbot + sentiment analysis + intelligent routing
• Results:
  • Automation rate: 72% of inquiries handled by chatbot
  • Response time: 8 hours → 45 minutes (-91%)
  • Support costs: ₹60L/year → ₹28L/year (-53%)
  • CSAT: 3.8 → 4.5/5 (+18%)
  • ROI: 6 months, ₹32L annual savings

Success Metrics

• Automation Rate: Target 60-80% for routine inquiries
• First Response Time: <1 minute for chatbot
• Resolution Time: 30-50% reduction
• CSAT: Maintain or improve (target 4.0+/5)
• Cost per Ticket: 40-60% reduction