CRM with AI Agents

Project Overview

Project Name: Customer Relationship Management with AI Agents System
Duration: 8-12 weeks

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Description

This project involves developing an AI-enhanced Customer Relationship Management (CRM) system that utilizes intelligent agents to automate customer interactions, predict customer behavior, and optimize sales processes. Students will learn to design and conceptualize AI agents that can handle customer service, sales automation, and relationship management through advanced machine learning and natural language processing techniques.

Learning Objectives
By completing this project, students will
  • Understand CRM fundamentals and customer lifecycle management
  • Learn AI agent architectures and multi-agent system design
  • Comprehend customer data analysis and behavioral modeling
  • Develop knowledge in conversational AI, recommendation systems, and predictive analytics
  • Understand business process automation and workflow optimization
  • Design intelligent customer engagement and retention strategies
  • Learn privacy regulations and ethical AI considerations in business contexts

Project Scope and Features

1

Intelligent Customer Service Agents

  • Natural language understanding for customer inquiries
  • Automated ticket routing and priority classification
  • Multi-channel communication management (email, chat, voice, social media)
  • Sentiment analysis and emotion detection in customer interactions

Sales Automation and Lead Management

  • Lead scoring and qualification algorithms
  • Predictive sales forecasting models
  • Automated follow-up and nurturing campaigns
  • Sales opportunity identification and recommendation systems
2
3

Customer Analytics and Behavioral Modeling

  • Customer segmentation and persona development
  • Churn prediction and retention modeling
  • Customer lifetime value calculations
  • Purchase behavior analysis and pattern recognition

Personalization and Recommendation Engines

  • Product and service recommendation systems
  • Personalized marketing campaign optimization
  • Dynamic pricing and offer customization
  • Content personalization and user experience adaptation
4

Technical Framework Requirements

Core Technologies and Concepts

  • AI Agent Architectures: Reactive agents, deliberative agents, hybrid architectures
  • Natural Language Processing: Intent recognition, entity extraction, dialogue management
  • Machine Learning Techniques: Classification, clustering, regression, deep learning
  • Recommendation Systems: Collaborative filtering, content-based filtering, hybrid approaches

System Architecture Components

  • Customer data integration and management platforms
  • AI agent orchestration and coordination frameworks
  • Real-time communication and interaction processing
  • Analytics and business intelligence dashboards
  • Workflow automation and business process engines
  • Security and privacy protection mechanisms

Phase-wise Implementation Strategy

Phase 1: CRM and Business Process Foundation (Week 1-2)

Customer Relationship Management Theory
CRM Fundamentals
  • Customer lifecycle stages and journey mapping
  • Sales pipeline management and opportunity tracking
  • Customer service processes and support workflows
  • Marketing automation and campaign management
  • Customer data management and data quality principles
  • Business process modeling and optimization
Customer Experience Strategy
  • Omnichannel customer engagement principles
  • Customer touchpoint identification and optimization
  • Service quality measurement and improvement
  • Customer satisfaction and Net Promoter Score(NPS) analysis
  • Voice of Customer(VoC) programs and feedback management
Business Domain Research
Industry-Specific CRM Applications
  • B2B vs B2C customer relationship differences
  • Sector - specific requirements(retail, healthcare, finance, manufacturing)
  • Enterprise vs SMB CRM needs and constraints
  • Global vs local market considerations
  • Regulatory compliance requirements by industry
Digital Transformation in Customer Management
  • Traditional CRM limitations and pain points
  • AI - driven transformation opportunities
  • Integration with existing business systems
  • Change management and user adoption strategies
  • ROI measurement and success metrics

Phase 2: Lane Detection and Vehicle Control (Week 3–4)

Lane Detection
Vision Pipeline for Lane Detection
  • ROI (Region of Interest) extraction
  • Grayscale and Gaussian blur preprocessing
  • Lane curve estimation using Hough lines
Control System Integration
  • Implement PID controller for steering
  • Adjust vehicle speed based on road curvature
  • Initial tests in simulation environment

Phase 3: Object Detection and Traffic Sign Recognition (Week 5–6)

Object Detection
Object Detection
  • Implement YOLOv5/SSD model for obstacle detection
  • Annotate and use datasets for vehicle and pedestrian detection
  • Distance estimation from camera input
Traffic Sign Detection
  • CNN model training on German Traffic Sign Dataset (GTSRB)
  • Classify signs like Stop, Turn Left/Right, Speed Limit
  • Connect recognition to driving decisions

Phase 4: Path Planning and Navigation (Week 7–8)

Path Planning
Trajectory Planning
  • Implement waypoint system with adjustable turns
  • Use A* or Dijkstra for obstacle-avoiding routing
  • Integrate path smoothing for sharp turns
Autonomous Navigation in Simulated Environment
  • Run trials in CARLA/Udacity Simulator
  • Adjust path in real time based on vision inputs

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Our Locations

Come and chat with us about your goals over a cup of coffee.

Hyderabad, Telangana

2nd Floor, Hitech City Rd, Above Domino's, opp. Cyber Towers, Jai Hind Enclave, Hyderabad, Telangana.

Bengaluru, Karnataka

3rd Floor, Site No 1&2 Saroj Square, Whitefield Main Road, Munnekollal Village Post, Marathahalli, Bengaluru, Karnataka.