- Phone:+ 91 9265723824
- Info@anvizon.com
Anvizon is a full-service software development company based in Ahmedabad, helping businesses transform through technology.
Project Info
Client Domain
Technology / Customer Service
Solution Type
AI-driven Conversational Agent System
Core Deliverables
Web-based AI Dashboard, Automated Response Engine, Integration APIs
Goal
Reduce human support load, increase query resolution speed, and provide 24/7 intelligent assistance.
Challenge
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The client faced high volumes of repetitive customer queries.
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Manual support teams required scaling as customer count increased, which raised costs.
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Response time delays were affecting user satisfaction.
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The client needed an intelligent system that learns continuously, not a basic chatbot.
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The system had to integrate smoothly with existing CRM and product data sources.
Our Solution
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Designed and developed an AI Agent System capable of understanding natural language queries.
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Trained the agent on product knowledge, FAQs, user behavior patterns, and historical support tickets.
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Integrated a self-improving feedback loop, allowing the agent to learn from new queries.
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Built a zero-human-delay workflow where the agent handles queries automatically, escalating only complex ones.
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Implemented multi-channel accessibility through web chat, mobile app chat, and WhatsApp.
AI Model Layer
LLM-based conversational understanding.
Context memory for following conversation flow.
Sentiment detection for tone-appropriate responses.
Knowledge Base Layer
Product FAQs, guidelines, and policy documents structured into searchable vectors
Instant retrieval of correct information using vector similarity search.
Automation & Execution Layer
Auto-ticket creation, status updates, appointment scheduling, order lookup, etc.
Workflows triggered based on interpreted user intent.
Integration Layer
Integration Layer
Connected with CRM, product management systems, notification services, and analytics dashboards.
User Interface
Web widget for website / portal.
Mobile SDK for app integration.
Chat-based interactions with friendly and professional tone.
Data Engineering
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Ticket logs
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Conversation transcripts
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Intent mapping
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Customer behavior patterns
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Agent accuracy
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Query categories
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User satisfaction trends
Channel Platforms
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Website Chat Widget: Real-time support for website visitors
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Mobile App Chat: In-app issue reporting and guidance
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CRM Integrati: Automatic agent-human handovers and ticket syncing
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WhatsApp / Messaging Channels: Easy communication for returning customers
Development
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Requirement & Use Case Analysis
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Conversational Design & Intent Taxonomy
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Data Collection & Knowledge Base Structuring
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Model Training & Natural Language Understanding Development
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Backend Integration & Agent Execution Workflow
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Frontend Widget / App Interface Development
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Testing with Real Conversations
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Deployment, Monitoring & Continuous Learning Enhancements
Impact
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Reduction in support workload: 40–65% of repetitive queries resolved without human intervention
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Improved response time: From several minutes to instant resolutions.
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Higher customer satisfaction: Faster support, consistent quality, no wait time.
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Reduced operational cost: Fewer human agents required for repetitive tasks.
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Scalable: Easily handles peak traffic or seasonal spikes.
Conclusion
The AI Agent System provided a smart, scalable, and always-available support layer, improving service efficiency and end-user experience. By automating routine tasks and enabling self-learning, the system continues to become more accurate and capable over time, supporting business growth without proportional staffing increases.