AI Overview

HotCRM is an AI-first enterprise CRM that embeds artificial intelligence capabilities throughout the entire customer lifecycle. Built on modern AI technologies including NLP, ML, LLMs, and vector embeddings, HotCRM provides intelligent automation and insights to help teams work smarter.

AI Vision

HotCRM's AI capabilities are designed to:

• Reduce manual work through intelligent automation
• Improve decision-making with predictive analytics
• Enhance customer insights using data analysis
• Accelerate sales cycles with smart recommendations
• Improve service quality through intelligent routing and knowledge management

AI Technology Stack

1. Natural Language Processing (NLP)

NLP enables HotCRM to understand and process human language in emails, meeting notes, case descriptions, and more.

Capabilities:

• Text analysis and extraction
• Sentiment analysis (Positive, Neutral, Negative, Angry)
• Entity recognition (names, companies, products)
• Language detection
• Email signature parsing

Use Cases:

• Extract contact information from email signatures
• Analyze customer sentiment in support cases
• Identify key entities in meeting transcriptions
• Categorize case types automatically

2. Machine Learning (ML)

ML models learn from historical data to make predictions and recommendations.

Capabilities:

• Predictive scoring algorithms
• Classification models
• Recommendation engines
• Pattern recognition
• Anomaly detection

Use Cases:

• Lead scoring (0-100 quality score)
• Opportunity win probability prediction
• Customer churn risk analysis
• Smart lead assignment
• Product recommendation

3. Large Language Models (LLM)

LLMs provide generative AI capabilities for content creation and intelligent assistance.

Capabilities:

• Text generation and summarization
• Content creation (emails, proposals)
• Question answering
• Conversation understanding
• Multi-language support

Use Cases:

• Generate marketing email copy
• Summarize customer interactions
• Create sales proposal outlines
• Answer questions from knowledge base
• Generate product talking points

4. Vector Embeddings & RAG

Vector embeddings enable semantic search and retrieval-augmented generation (RAG) for knowledge management.

Capabilities:

• Semantic similarity search
• Context-aware retrieval
• Document embeddings
• Knowledge graph construction
• RAG-based Q&A

Use Cases:

• Semantic knowledge article search
• Find similar cases
• Recommend related content
• AI chatbot with company knowledge
• Intelligent document discovery

AI Enhancement by Object

HotCRM embeds AI capabilities directly into business objects across the platform:

Marketing & Leads

Sales Automation

Customer Service

AI-Enhanced Fields

HotCRM uses a consistent naming convention for AI-generated fields:

// Lead object example
{
  name: 'LeadScore',
  type: 'number',
  label: 'Lead Score',
  min: 0,
  max: 100,
  readonly: true,
  description: 'AI-calculated lead quality score (0-100)'
}

{
  name: 'AISummary',
  type: 'textarea',
  label: 'AI Lead Analysis',
  readonly: true,
  description: 'AI-generated lead quality analysis and recommendations'
}

{
  name: 'AIRecommendedAction',
  type: 'text',
  label: 'AI Recommended Action',
  readonly: true
}

Common AI Field Patterns:

• AI[Feature] - AI-generated content (e.g., AISummary, AINextStepSuggestion)
• [Metric]Score - AI-calculated scores (e.g., LeadScore, QualityScore)
• AI[Type]Probability - AI predictions (e.g., AIWinProbability)
• AI[Subject] - AI analysis fields (e.g., AIRiskFactors, AICompetitiveIntel)

AI Data Flow

graph LR
    A[User Input] --> B[Data Capture]
    B --> C[AI Processing]
    C --> D[ML Models]
    C --> E[LLM API]
    C --> F[Vector DB]
    D --> G[Predictions]
    E --> H[Generated Content]
    F --> I[Semantic Search]
    G --> J[CRM UI]
    H --> J
    I --> J

1. Data Capture: User input and system events
2. AI Processing: Route to appropriate AI service
3. Model Execution: ML models, LLMs, or vector search
4. Results: Predictions, content, or recommendations
5. UI Display: Present AI insights to users

AI Model Training

HotCRM uses historical data to continuously improve AI models:

Lead Scoring Model

• Training Data: Past leads with conversion outcomes
• Features: Industry, company size, engagement, completeness
• Update Frequency: Weekly retraining

Win Probability Model

• Training Data: Historical opportunities with win/loss results
• Features: Deal size, stage, days open, activity count, competitor presence
• Update Frequency: Monthly retraining

Smart Routing Model

• Training Data: Case assignments and resolution times
• Features: Case type, priority, agent skills, current workload
• Update Frequency: Real-time learning

Privacy & Security

AI features in HotCRM respect data privacy and security:

• ✅ Data encryption at rest and in transit
• ✅ Field-level security controls AI data access
• ✅ Opt-out options for sensitive data
• ✅ Audit logging for all AI operations
• ✅ GDPR compliance for EU customers
• ✅ Data residency options for regulated industries

Performance Metrics

Track AI effectiveness with built-in metrics:

• Lead Score Accuracy: Conversion rate by score range
• Win Prediction Accuracy: Actual vs. predicted outcomes
• Agent Routing Efficiency: Average resolution time by routing type
• Knowledge Retrieval Quality: User ratings on AI answers
• Content Generation Usage: Adoption rate of AI-generated content

AI Configuration

Administrators can configure AI features:

// AI Settings (config/ai.ts)
export const aiConfig = {
  leadScoring: {
    enabled: true,
    model: 'lead-score-v2',
    threshold: 70, // High-score threshold
    autoAssign: true // Auto-assign high-score leads
  },
  
  winProbability: {
    enabled: true,
    model: 'win-predict-v1',
    updateFrequency: 'daily'
  },
  
  knowledgeRAG: {
    enabled: true,
    vectorDB: 'pinecone',
    embeddingModel: 'text-embedding-ada-002',
    topK: 5 // Number of relevant articles to retrieve
  },
  
  voiceTranscription: {
    enabled: true,
    provider: 'whisper',
    languages: ['en', 'zh']
  }
};

Next Steps

• AI Capabilities - Detailed AI feature documentation
• Creating Objects - Add AI fields to custom objects
• ObjectQL API - Query AI-enhanced data

Resources

• OpenAI Platform
• Vector Database Guide
• RAG Architecture