Role-Based Customer Support & Employee Assistant System with Admin Policy RAG Engine, Multi-File/Folder Uploads, Anti-Hallucination Guardrails & Cited Q&A
The application supports three distinct user roles with strict JWT-based server authentication and backend authorization:
- Full system access & control center
- User Management: Create, edit, assign roles, activate/deactivate, or delete users
- Policy Management: Drag-and-drop single/multi-file or entire folder uploads (PDF, DOCX, DOC, TXT, CSV, XLSX)
- Access Level Configuration: Set document permissions (
PUBLIC,EMPLOYEE,TRAINER,ADMIN) - RAG Processing: Reprocess documents, manage chunking, and view extracted text
- View immutable system activity Audit Logs
- AI Policy Assistant Q&A
- Access Trainer Dashboard & Assigned Employee List
- Create custom AI customer practice scenarios with specified difficulty levels & personas
- Launch Interactive Practice Simulator & Live Console
- AI Policy Assistant Q&A (access to
PUBLIC,EMPLOYEE, andTRAINERpolicies) - Restricted from Admin Dashboard, User Management, and Policy File Uploads/Deletions (HTTP 403 Forbidden enforced)
- Access Employee Dashboard & Practice Console
- Search & download employee-accessible company policy documents
- Ask AI Policy Assistant questions with verified source citations
- Restricted from Admin Dashboard, User Management, Policy Management, and System Settings (HTTP 403 Forbidden enforced)
| Role | Password | Allowed Access | |
|---|---|---|---|
| Admin | admin@example.com |
Admin123! |
Admin Dashboard, User Management, Policy Uploads (RAG), Audit Logs, AI Assistant |
| Trainer | trainer@example.com |
Trainer123! |
Trainer Dashboard, Assigned Employees, Scenarios, Practice Console, AI Assistant |
| Employee | employee@example.com |
Employee123! |
Employee Dashboard, Support Simulator, Policy Library, AI Assistant |
- Multi-File & Folder Batch Upload: Admin selects files or an entire folder (
webkitdirectory). - Text Extraction & Cleaning: Automatically parses text from PDF, DOCX, TXT, CSV, XLSX formats.
- Chunking & Indexing: Segments extracted text into ~500-character vector chunks with section metadata.
- Role Access Restriction: Chunks inherit policy access levels (
PUBLIC,EMPLOYEE,TRAINER,ADMIN). - Anti-Hallucination Guardrail: If requested information is absent in company policies, AI responds:
"I couldn't find this information in the available company policies. Please contact HR or your administrator for clarification."
- Source Citations: AI responses include verified source citations (
Document Title — Section — Page Number).
- Assign training activities.
AI is the intelligence layer of the application.
It performs tasks such as:
- Customer simulation.
- Intent detection.
- Sentiment analysis.
- Knowledge retrieval.
- Response generation.
- Coaching.
- Escalation prediction.
- Performance evaluation.
The AI behaves like a real customer based on:
- Customer persona
- Problem scenario
- Difficulty level
- Conversation history
- Agent responses
The customer can be:
- Calm
- Confused
- Impatient
- Frustrated
- Angry
- Highly demanding
The simulator dynamically changes its behavior based on how the agent responds.
Agents can enter or paste a customer message.
The system analyzes the message and provides:
- Customer intent
- Sentiment
- Emotion
- Frustration level
- Relevant knowledge
- Suggested response
- Escalation risk
Agents can practice using previously recorded support conversations.
Features include:
- Step-by-step transcript replay
- Original response review
- Alternative response generation
- AI evaluation
- Improved response suggestions
The assistant provides guidance during the interaction.
Example:
💡 Coach: Acknowledge the customer's frustration before explaining the refund policy.
It can evaluate:
- Tone
- Empathy
- Clarity
- Professionalism
- Conciseness
- Grammar
- Policy adherence
The application uses Retrieval-Augmented Generation (RAG) to retrieve relevant information from company documents.
Supported knowledge sources can include:
- FAQs
- Refund policies
- Product documentation
- Troubleshooting guides
- Shipping policies
- Internal support documents
Company Documents
↓
Text Extraction
↓
Document Chunking
↓
Embeddings
↓
Vector Database
↓
Semantic Search
↓
Relevant Knowledge
↓
AI Response
The system can provide the source of the recommendation to reduce hallucination.
The system identifies what the customer needs and how they feel.
- Billing Issue
- Refund Request
- Account Problem
- Technical Support
- Delivery Issue
- Subscription Cancellation
- Product Complaint
- Positive
- Neutral
- Negative
- Very Negative
- Frustration
- Anger
- Confusion
- Anxiety
- Satisfaction
- Disappointment
- Urgency
The system continuously evaluates the possibility of escalation.
Example:
Escalation Risk: 78%
Risk Level: HIGH
Possible risk factors:
- Increasing customer frustration
- Repeated complaints
- Previous failed support
- Negative language
- Request for supervisor
- Poor agent response
- Unresolved issue
The system also provides recommended intervention strategies.
After each session, the system generates a performance report.
Example:
Overall Score 88%
Communication 91%
Knowledge 96%
Problem Solving 89%
Empathy 84%
De-escalation 78%
Policy Adherence 96%
The application tracks long-term agent development.
Example:
Communication Clarity 91%
Policy & KB Adherence 96%
Knowledge Retrieval 93%
Problem Solving 89%
Empathy & Validation 84%
De-escalation Under Stress 78%
This helps identify individual strengths and weaknesses.
The system recommends training scenarios based on the agent's performance.
For example:
Weak Area: De-escalation Recommended Practice: Angry Customer Simulation
The difficulty can also adapt according to the agent's performance.
To encourage continuous learning, the platform can include:
- XP
- Levels
- Badges
- Daily challenges
- Training streaks
- Leaderboards
- Achievements
The platform uses a multi-agent architecture.
AI ORCHESTRATOR
│
┌───────────────────┼───────────────────┐
↓ ↓ ↓
Customer Simulator Intent & Sentiment Knowledge/RAG
│ │ │
└───────────────────┼───────────────────┘
↓
Coaching Agent
│
↓
Escalation Monitor
│
↓
Summary & Report Agent
Generates realistic customer messages.
Analyzes customer intent, emotion, sentiment, and frustration.
Retrieves relevant information from the knowledge base.
Provides response suggestions and communication feedback.
Predicts escalation probability and recommends intervention.
Generates the final session summary and performance report.
Login
↓
Dashboard
↓
Select Interaction Mode
↓
Select Scenario
↓
Start Conversation
↓
Customer Message
↓
AI Analysis
↓
Knowledge + Coaching + Risk Analysis
↓
Agent Response
↓
Next Conversation Turn
↓
Session Complete
↓
Performance Report
↓
Personalized Training Recommendation
- Authentication & User Management
- Dashboard
- Session Configuration
- Simulator Mode
- Manual Message Mode
- Replay Training Mode
- Customer Simulator
- Multi-Agent AI Pipeline
- Knowledge Base & RAG
- Real-Time Coaching
- Escalation Risk Detection
- Performance Reports
- Personalized Coaching
- Training & Scenarios
- Analytics
- Gamification
- Admin Management
User
Role
Team
AgentProfile
Session
Scenario
CustomerPersona
Conversation
Message
IntentAnalysis
SentimentAnalysis
KnowledgeDocument
KnowledgeRecommendation
CoachingRecommendation
SuggestedResponse
EscalationAssessment
PerformanceReport
PerformanceScore
SkillProfile
TrainingPlan
TrainingAssignment
Achievement
Notification
AuditLog
FRONTEND
│
↓
API LAYER
│
↓
SESSION ORCHESTRATOR
│
↓
AI ORCHESTRATOR
│
┌────────────────┼────────────────┐
↓ ↓ ↓
AI AGENTS RAG RISK ENGINE
│ │ │
└────────────────┼────────────────┘
↓
AI RESPONSE
│
↓
REAL-TIME UI
│
↓
PERFORMANCE REPORT
- React
- TypeScript
- Tailwind CSS
- Modern responsive UI
- Python
- FastAPI
- REST APIs
- WebSockets
- Large Language Model
- Multi-Agent Architecture
- Prompt Engineering
- Structured AI Outputs
- Embeddings
- Vector Search
- PostgreSQL + pgvector / Vector Database
- Document Processing
- PostgreSQL
- Redis
- WebSockets
- Docker
- Cloud deployment
The application can implement:
- Authentication
- Role-Based Access Control
- Secure API keys
- PII detection and masking
- Data encryption
- Session access control
- Audit logs
- Knowledge-source validation
- AI confidence scores
Future versions can include:
- 🎙️ Voice-based customer support
- 🌐 Multilingual support
- 📞 Real-time call coaching
- 🧠 Adaptive AI training
- 🔌 CRM integration
- 💬 WhatsApp/Teams integration
- 📈 Advanced team analytics
- 🔍 Knowledge quality monitoring
- 📊 Predictive performance analytics
The key differentiating features of Customer Support Assistant are:
The AI customer's frustration and behavior change according to the agent's responses.
The system provides short, actionable coaching while the conversation is happening.
The agent can understand why a particular response was recommended.
The system identifies potential escalation before the conversation reaches a critical point.
The system can compare:
Agent's Actual Response
↓
Actual Risk
Recommended Response
↓
Potentially Lower Risk
Training difficulty automatically changes according to the agent's performance.
The Customer Support Assistant aims to:
- Improve agent communication skills.
- Increase first-interaction resolution.
- Reduce unnecessary escalations.
- Improve knowledge usage.
- Improve customer satisfaction.
- Reduce training time.
- Provide personalized agent development.
- Transform traditional reactive training into continuous AI-assisted learning.
Customer Support Assistant transforms customer-support training from a reactive, post-interaction process into a proactive, real-time learning experience.
It combines AI simulation, multi-agent analysis, RAG-powered knowledge retrieval, live coaching, escalation detection, and performance analytics into a single platform.
Project: Customer Support Assistant Type: AI-Powered Customer Support & Training Platform Architecture: Multi-Agent AI + RAG Primary User: Customer Support Agent Platform Manager: Admin / Manager