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CRM Software Design Document (SDD)

Contact Relationship Management System​

Document Information​

  • Version: 1.0
  • Date: October 2025
  • Status: Draft
  • References: crm-requirements-document.md

Table of Contents​

  1. System Architecture
  2. Component Design
  3. Data Architecture
  4. API Design
  5. Integration Architecture
  6. Security Architecture
  7. Performance Design
  8. Deployment Architecture

System Architecture​

High-Level Architecture​

Architecture Principles​

  1. Microservices Pattern: Each major feature is a separate service
  2. Event-Driven: Asynchronous processing for enrichment and viral mechanics
  3. API-First: All functionality exposed through RESTful APIs
  4. Cache-Heavy: Redis caching for performance
  5. Cloud-Native: Designed for GCP services

Component Design​

Contact Service​

Purpose: Core CRUD operations for contact management

Key Components:

// src/services/contact_service.rs
pub struct ContactService {
    db_pool: PgPool,
    cache: RedisClient,
    event_publisher: PubSubClient,
}

pub struct Contact {
    pub id: Uuid,
    pub user_id: Uuid,
    pub email: String,
    pub name: String,
    pub company: Option<String>,
    pub enrichment_status: EnrichmentStatus,
    pub social_profiles: HashMap<String, String>,
    pub custom_fields: JsonValue,
    pub tags: Vec<String>,
    pub relationship_stage: RelationshipStage,
    pub created_at: DateTime<Utc>,
    pub updated_at: DateTime<Utc>,
}

Key Methods:

  • create_contact(): Create with duplicate detection
  • bulk_import(): Import with progress tracking
  • search_contacts(): Full-text search with filters
  • merge_contacts(): Intelligent duplicate merging

Enrichment Service​

Purpose: AI-powered contact data enrichment

Architecture:

// src/services/enrichment_service.rs
pub struct EnrichmentService {
    providers: Vec<Box<dyn EnrichmentProvider>>,
    cache: RedisClient,
    rate_limiter: RateLimiter,
}

#[async_trait]
pub trait EnrichmentProvider {
    async fn enrich(&self, contact: &Contact) -> Result<EnrichmentData>;
    fn provider_name(&self) -> &str;
    fn cost_per_enrichment(&self) -> f64;
}

pub struct EnrichmentOrchestrator {
    queue: PubSubSubscriber,
    service: EnrichmentService,
    credit_manager: CreditManager,
}

Enrichment Flow:

  1. Contact created/imported → Publish enrichment event
  2. Orchestrator consumes event → Check user credits
  3. Query multiple providers in parallel
  4. Merge and deduplicate data
  5. Update contact with enriched data
  6. Award credits for data contribution

Social Graph Service​

Purpose: Relationship mapping and network analysis

Graph Model:

// src/services/social_graph_service.rs
pub struct SocialGraphService {
    graph_db: GraphDatabase, // Could use PostgreSQL with recursive CTEs
    cache: RedisClient,
    analytics: GraphAnalytics,
}

pub struct Relationship {
    pub from_contact_id: Uuid,
    pub to_contact_id: Uuid,
    pub relationship_type: RelationType,
    pub strength: f32, // 0.0 to 1.0
    pub interaction_count: i32,
    pub last_interaction: DateTime<Utc>,
}

pub struct NetworkPath {
    pub nodes: Vec<Uuid>,
    pub total_distance: i32,
    pub connection_quality: f32,
}

Key Algorithms:

  • Dijkstra's algorithm for shortest path
  • PageRank for influence scoring
  • Community detection for clustering
  • Relationship strength calculation

Communication Hub​

Purpose: Unified communication tracking and analytics

Design:

// src/services/communication_hub.rs
pub struct CommunicationHub {
    email_integrations: Vec<EmailProvider>,
    sms_gateway: SmsGateway,
    message_queue: MessageQueue,
    analytics: CommunicationAnalytics,
}

pub struct Communication {
    pub id: Uuid,
    pub contact_id: Uuid,
    pub channel: CommunicationChannel,
    pub direction: Direction, // Inbound/Outbound
    pub content: String,
    pub sentiment_score: f32,
    pub timestamp: DateTime<Utc>,
}

Viral Mechanics Engine​

Purpose: Drive viral growth through gamification and incentives

Components:

// src/services/viral_engine.rs
pub struct ViralEngine {
    credit_system: CreditSystem,
    gamification: GamificationEngine,
    analytics: ViralAnalytics,
}

pub struct ViralAction {
    pub action_type: ActionType, // Import, Share, Referral, etc.
    pub user_id: Uuid,
    pub credits_earned: i32,
    pub xp_earned: i32,
    pub triggered_achievements: Vec<Achievement>,
}

pub struct ViralMetrics {
    pub k_factor: f32,
    pub viral_cycle_time: Duration,
    pub conversion_rate: f32,
}

Data Architecture​

Primary Database Schema​

-- Core Contacts Table
CREATE TABLE contacts (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id UUID NOT NULL REFERENCES users(id),
    email VARCHAR(255) NOT NULL,
    name VARCHAR(255) NOT NULL,
    company VARCHAR(255),
    title VARCHAR(255),
    phone VARCHAR(50),
    enrichment_status VARCHAR(50) DEFAULT 'pending',
    enrichment_data JSONB,
    social_profiles JSONB,
    custom_fields JSONB,
    tags TEXT[],
    relationship_stage VARCHAR(50),
    source VARCHAR(100),
    created_at TIMESTAMPTZ DEFAULT NOW(),
    updated_at TIMESTAMPTZ DEFAULT NOW(),
    CONSTRAINT unique_user_email UNIQUE(user_id, email)
);

-- Social Graph Relationships
CREATE TABLE relationships (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    from_contact_id UUID REFERENCES contacts(id),
    to_contact_id UUID REFERENCES contacts(id),
    relationship_type VARCHAR(50),
    strength DECIMAL(3,2),
    interaction_count INTEGER DEFAULT 0,
    last_interaction TIMESTAMPTZ,
    metadata JSONB,
    created_at TIMESTAMPTZ DEFAULT NOW(),
    CONSTRAINT unique_relationship UNIQUE(from_contact_id, to_contact_id)
);

-- Communication History
CREATE TABLE communications (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    contact_id UUID REFERENCES contacts(id),
    user_id UUID REFERENCES users(id),
    channel VARCHAR(50),
    direction VARCHAR(10),
    subject TEXT,
    content TEXT,
    sentiment_score DECIMAL(3,2),
    metadata JSONB,
    occurred_at TIMESTAMPTZ,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Viral Mechanics
CREATE TABLE viral_actions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id UUID REFERENCES users(id),
    action_type VARCHAR(50),
    credits_earned INTEGER,
    xp_earned INTEGER,
    metadata JSONB,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Enrichment Credits
CREATE TABLE user_credits (
    user_id UUID PRIMARY KEY REFERENCES users(id),
    balance INTEGER DEFAULT 100,
    lifetime_earned INTEGER DEFAULT 100,
    lifetime_spent INTEGER DEFAULT 0,
    updated_at TIMESTAMPTZ DEFAULT NOW()
);

Caching Strategy​

Redis Cache Structure:

# Contact Cache
contact:{user_id}:{contact_id} -> Contact JSON (TTL: 1 hour)

# Search Cache
search:{user_id}:{query_hash} -> Contact IDs (TTL: 15 minutes)

# Social Graph Cache
graph:paths:{from_id}:{to_id} -> Path JSON (TTL: 1 hour)
graph:network:{user_id} -> Network Stats (TTL: 30 minutes)

# Enrichment Cache
enrichment:{email_hash} -> Enrichment Data (TTL: 7 days)

# User Credits Cache
credits:{user_id} -> Credit Balance (TTL: 5 minutes)

API Design​

RESTful Endpoints​

Contact Management:

POST   /api/crm/contacts                 Create contact
GET    /api/crm/contacts                 List contacts (paginated)
GET    /api/crm/contacts/:id             Get contact details
PUT    /api/crm/contacts/:id             Update contact
DELETE /api/crm/contacts/:id             Delete contact
POST   /api/crm/contacts/bulk-import     Import contacts
POST   /api/crm/contacts/merge           Merge duplicates
GET    /api/crm/contacts/search          Search contacts

Enrichment:

POST   /api/crm/enrichment/enrich/:id    Trigger enrichment
GET    /api/crm/enrichment/status/:id    Check enrichment status
GET    /api/crm/enrichment/credits       Get credit balance
POST   /api/crm/enrichment/verify        Verify and earn credits

Social Graph:

GET    /api/crm/graph/network/:id        Get contact's network
GET    /api/crm/graph/path               Find path between contacts
GET    /api/crm/graph/influence/:id      Get influence score
POST   /api/crm/graph/introduce          Request introduction

Communication Hub:

GET    /api/crm/communications/:contact  Get communication history
POST   /api/crm/communications/track     Track communication
GET    /api/crm/communications/analytics Communication analytics
POST   /api/crm/communications/remind    Set reminder

Viral Mechanics:

GET    /api/crm/viral/stats              Get viral metrics
POST   /api/crm/viral/refer              Create referral
GET    /api/crm/viral/leaderboard        Get leaderboard
GET    /api/crm/viral/achievements       Get achievements

GraphQL Schema (Optional)​

type Contact {
  id: ID!
  name: String!
  email: String!
  company: String
  enrichmentData: EnrichmentData
  relationships: [Relationship!]!
  communications(limit: Int, offset: Int): [Communication!]!
  networkStats: NetworkStats!
}

type Query {
  contact(id: ID!): Contact
  contacts(filter: ContactFilter, limit: Int, offset: Int): ContactConnection!
  shortestPath(fromId: ID!, toId: ID!): NetworkPath
  myViralStats: ViralStats!
}

type Mutation {
  createContact(input: CreateContactInput!): Contact!
  enrichContact(id: ID!): EnrichmentJob!
  requestIntroduction(fromId: ID!, toId: ID!, message: String): IntroductionRequest!
}

Integration Architecture​

Platform Integration Points​

1. Authentication Integration:

// Shared with main platform
pub trait AuthProvider {
    async fn validate_token(&self, token: &str) -> Result<User>;
    async fn get_user_permissions(&self, user_id: Uuid) -> Result<Permissions>;
}

2. Event Bus Integration:

# Pub/Sub Topics
topics:
  - contact.created
  - contact.enriched
  - viral.action.completed
  - introduction.requested
  - credits.earned

3. Billing Integration:

// Shared billing service
pub trait BillingProvider {
    async fn check_subscription(&self, user_id: Uuid) -> Result<Subscription>;
    async fn track_usage(&self, user_id: Uuid, feature: &str, amount: i32);
}

External Service Integrations​

Enrichment Providers:

  • Clearbit: Company and person data
  • FullContact: Social profiles
  • Hunter.io: Email verification
  • Custom scrapers: LinkedIn, Twitter

Communication Providers:

  • SendGrid: Email tracking
  • Twilio: SMS gateway
  • Gmail API: Email sync
  • Outlook API: Calendar/email sync

Security Architecture​

Data Protection​

Encryption:

  • At rest: AES-256 for PII fields
  • In transit: TLS 1.3 minimum
  • Field-level encryption for sensitive data

Access Control:

pub enum ContactPermission {
    ViewOwn,
    ViewTeam,
    EditOwn,
    EditTeam,
    Delete,
    Export,
    ShareExternal,
}

pub struct ContactAccessControl {
    pub fn can_access(&self, user: &User, contact: &Contact, permission: ContactPermission) -> bool {
        // Implement RBAC logic
    }
}

Privacy Controls​

Data Minimization:

  • Store only necessary data
  • Automatic PII purging after retention period
  • Anonymization for analytics

Consent Management:

pub struct ConsentManager {
    pub fn record_consent(&self, contact_id: Uuid, consent_type: ConsentType);
    pub fn check_consent(&self, contact_id: Uuid, action: &str) -> bool;
    pub fn withdraw_consent(&self, contact_id: Uuid, consent_type: ConsentType);
}

Performance Design​

Optimization Strategies​

1. Database Optimization:

  • Composite indexes on (user_id, email)
  • Partial indexes for active contacts
  • Materialized views for analytics
  • Connection pooling with pgbouncer

2. Caching Strategy:

  • Write-through cache for contacts
  • Cache-aside for enrichment data
  • Distributed caching with Redis Cluster
  • Cache warming for popular queries

3. Async Processing:

  • Background enrichment jobs
  • Batch import processing
  • Deferred analytics calculation
  • Event-driven viral tracking

4. Query Optimization:

-- Optimized contact search with FTS
CREATE INDEX idx_contacts_search ON contacts 
USING gin(to_tsvector('english', name || ' ' || email || ' ' || company));

-- Efficient relationship queries
CREATE INDEX idx_relationships_graph ON relationships(from_contact_id, to_contact_id)
INCLUDE (strength, relationship_type);

Scalability Considerations​

Horizontal Scaling:

  • Stateless services for easy scaling
  • Read replicas for PostgreSQL
  • Sharding strategy for large datasets
  • Auto-scaling based on CPU/memory

Performance Targets:

  • Contact search: <100ms for 1M contacts
  • Enrichment: <2s average per contact
  • Graph traversal: <500ms for 3 degrees
  • API response: <200ms p95

Deployment Architecture​

Container Structure​

# Base image for Rust services
FROM rust:1.75-alpine AS builder
WORKDIR /app
COPY cargo.toml Cargo.lock ./
COPY crm-module/src ./src
RUN cargo build --release

FROM alpine:3.19
RUN apk add --no-cache ca-certificates
COPY --from=builder /app/target/release/crm-service /usr/local/bin/
EXPOSE 8080
CMD ["crm-service"]

Kubernetes Deployment​

apiVersion: apps/v1
kind: Deployment
metadata:
  name: crm-contact-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: crm-contact-service
  template:
    metadata:
      labels:
        app: crm-contact-service
    spec:
      containers:
      - name: contact-service
        image: gcr.io/project/crm-contact-service:latest
        ports:
        - containerPort: 8080
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: crm-secrets
              key: database-url
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"

Monitoring & Observability​

Metrics:

  • Contact creation rate
  • Enrichment success rate
  • API latency percentiles
  • Cache hit rates
  • Viral K-factor

Logging:

use tracing::{info, error, instrument};

#[instrument]
pub async fn create_contact(&self, input: CreateContactInput) -> Result<Contact> {
    info!("Creating contact for user {}", input.user_id);
    // Implementation
}

Distributed Tracing:

  • OpenTelemetry integration
  • Trace ID propagation
  • Performance bottleneck identification

Testing Strategy​

Unit Tests​

#[cfg(test)]
mod tests {
    use super::*;
    
    #[tokio::test]
    async fn test_contact_creation() {
        let service = ContactService::new_test();
        let contact = service.create_contact(/*...*/).await.unwrap();
        assert_eq!(contact.email, "test@example.com");
    }
}

Integration Tests​

  • Test against real PostgreSQL
  • Mock external services
  • Test event publishing
  • Verify cache behavior

Performance Tests​

  • Load test with k6
  • Benchmark critical paths
  • Stress test enrichment queue
  • Graph traversal performance

Migration Strategy​

From Existing System​

  1. Export existing contacts
  2. Transform to new schema
  3. Batch import with deduplication
  4. Gradual enrichment rollout
  5. Parallel run for validation

Database Migrations​

-- Migration 001: Initial schema
CREATE TABLE contacts (...);

-- Migration 002: Add viral mechanics
CREATE TABLE viral_actions (...);

-- Migration 003: Performance indexes
CREATE INDEX CONCURRENTLY ...;

Risk Mitigation​

Technical Risks​

  1. Enrichment API Rate Limits: Multiple providers, caching, backoff
  2. Data Quality: Verification system, community validation
  3. Scale: Designed for 10M+ contacts from day one
  4. Privacy: GDPR compliance built-in

Business Risks​

  1. Viral Growth Control: Circuit breakers for exponential growth
  2. Credit System Abuse: Fraud detection, rate limiting
  3. Data Accuracy: Multiple source verification
  4. Competition: Unique value proposition focus