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Personalized Content Generation Service - Comprehensive Plan

Executive Summary​

This document outlines the comprehensive plan for developing a Personalized Content Generation Service that will enable automated creation of marketing content, sales materials, and compliance-checked content. The service leverages existing capabilities and open-source libraries to deliver a production-ready solution within 6-8 weeks.

Market Opportunity​

  • Market Size: Content marketing AI projected at $12B by 2028
  • Business Impact: Personalization drives 40% higher conversion rates
  • Target Users: Marketing teams, sales organizations, content creators

Current Readiness Assessment​

  • ✅ Report Generator: 80% (well-structured, multi-format support)
  • ✅ Content Formatting: 85% (structured responses, templates)
  • ✅ User Segmentation: 75% (comprehensive analytics)
  • ⚠️ Content Templates: Need Development (1-2 weeks)
  • ⚠️ Brand Voice System: Need Development (2-3 weeks)

1. What We Already Have​

1.1 Report Generation Capabilities ✅​

Location:

  • packages/agents/process_agents/report_generator.py
  • packages/analytics/reporting.py
  • packages/use_case_components/generators/report_generator.py

Capabilities:

  • ✅ Multi-format output (Markdown, HTML, PDF, JSON, DOCX)
  • ✅ Executive summary generation
  • ✅ Structured sections with citations
  • ✅ Quality scoring system
  • ✅ Template-based generation
  • ✅ LLM-powered synthesis

Integration Points:

# Research Report Generator
from packages.agents.process_agents.report_generator import ReportGenerator

generator = ReportGenerator()
report = await generator.generate_report(
    plan=research_plan,
    findings=findings,
    output_format=ReportFormat.MARKDOWN
)

Strengths:

  • Proven report generation with citations
  • Multi-format export (PDF, DOCX, HTML, Markdown)
  • Quality scoring built-in
  • LLM-based synthesis

Gaps:

  • Not optimized for marketing content
  • Limited personalization by user segment
  • No brand voice consistency enforcement

1.2 Content Formatting & Structuring ✅​

Location:

  • packages/rag/structured_formatting.py
  • packages/rag/response_visualization.py

Capabilities:

  • ✅ Response type detection (summary, guide, comparison, etc.)
  • ✅ Content parsing into structured sections
  • ✅ Table of contents generation
  • ✅ Navigation structure
  • ✅ Formatting configuration system

Integration Points:

from packages.rag.structured_formatting import StructuredFormatter, FormattingConfig

formatter = StructuredFormatter(config=FormattingConfig())
response = await formatter.format_response(
    text=generated_content,
    title="Marketing Blog Post"
)

Strengths:

  • Sophisticated content structuring
  • Multiple response types
  • TOC and navigation generation
  • Configurable formatting

Gaps:

  • Not designed for marketing-specific formats
  • Limited template system

1.3 User Segmentation & Personalization ✅​

Location:

  • packages/analytics/segmentation.py
  • packages/search_interface/personalization.py

Capabilities:

  • ✅ User profiling (power user, casual, researcher, etc.)
  • ✅ Behavior clustering (K-Means, DBSCAN)
  • ✅ Engagement level classification
  • ✅ Preference tracking
  • ✅ User journey analysis

Integration Points:

from packages.analytics.segmentation import UserSegmentation

segmentation = UserSegmentation(analytics_engine)
profiles = await segmentation.create_user_profiles(days=90)

# Use profiles for personalization
for profile in profiles:
    print(f"User: {profile.user_id}, Type: {profile.user_type}")

Strengths:

  • Comprehensive user classification
  • Machine learning-based clustering
  • Behavioral pattern analysis
  • Preference learning

Gaps:

  • Not integrated with content generation
  • Missing content preference mapping
  • No audience segment templates

1.4 Email Drafting System ✅​

Location:

  • packages/agents/process_agents/email_drafter.py

Capabilities:

  • ✅ Template-based email generation
  • ✅ RAG-powered context retrieval
  • ✅ Tone matching (professional, friendly, apologetic)
  • ✅ Quality scoring (confidence, completeness, appropriateness)
  • ✅ LLM personalization

Integration Points:

from packages.agents.process_agents.email_drafter import EmailDrafter

drafter = EmailDrafter(retriever=rag_retriever)
draft = await drafter.draft_response(
    email=incoming_email,
    classification=email_classification
)

Strengths:

  • Proven email generation pipeline
  • Template + RAG + LLM architecture
  • Tone and quality scoring
  • Context-aware generation

Gaps:

  • Limited to email responses (not marketing emails)
  • No multi-channel content support
  • Missing A/B testing integration

1.5 Prompt Optimization ✅​

Location:

  • packages/prompts/optimization.py
  • packages/prompts/dspy_modules.py

Capabilities:

  • ✅ DSPy-based prompt optimization
  • ✅ Bootstrap few-shot learning
  • ✅ MIPRO optimization
  • ✅ Metric-driven evaluation

Integration Points:

from packages.prompts import PromptOptimizer, OptimizationConfig

optimizer = PromptOptimizer(config=OptimizationConfig())
optimized_module = optimizer.optimize(
    module=content_generator,
    training_data=examples,
    metric=quality_metric
)

Strengths:

  • Automatic prompt optimization
  • Data-driven improvement
  • Evaluation framework

Gaps:

  • Not specifically tuned for content generation
  • Need marketing-specific metrics

1.6 Compliance & Validation ✅​

Location:

  • packages/rag/compliance_agent.py
  • packages/rag/compliance_config.py
  • packages/rag/input_sanitization.py
  • config/guardrails.yml

Capabilities:

  • ✅ Compliance checking pipeline
  • ✅ Regulatory validation
  • ✅ Input sanitization
  • ✅ Safety guardrails
  • ✅ Audit logging

Integration Points:

from packages.rag.compliance_agent import ComplianceAgent

compliance = ComplianceAgent()
response = await compliance.handle_compliance_query(
    query=content_to_check,
    user_context={"domain": "marketing"}
)

Strengths:

  • Robust compliance infrastructure
  • Audit trail support
  • Configurable guardrails
  • Multi-domain support

Gaps:

  • Need marketing-specific compliance rules
  • Brand guideline validation missing
  • Fact-checking integration needed

1.7 Template Infrastructure ✅​

Location:

  • packages/use_case_components/templates/query_templates.py
  • packages/agents/agent_config_schema.py (AgentTemplate)
  • data/process_agents/email_templates/response_templates.json

Capabilities:

  • ✅ Query template system
  • ✅ Agent configuration templates
  • ✅ Email response templates (JSON-based)
  • ✅ Domain-specific templates (contract, medical, security)

Integration Points:

from packages.use_case_components.templates.query_templates import QueryTemplateManager

template_manager = QueryTemplateManager()
template = template_manager.get_template("extract_clause", domain="contract")
query = template.format(clause_type="termination")

Strengths:

  • Existing template infrastructure
  • JSON-based template storage
  • Domain-specific templates

Gaps:

  • No marketing content templates
  • Missing blog post, social media templates
  • No brand voice templates

2. Open-Source Libraries to Leverage​

2.1 Content Generation Libraries​

Primary: Continue Using LangChain + OpenAI ✅ (Already in use)​

  • Current Usage: langchain>=0.1.0, openai>=1.12.0
  • Why: Already integrated, proven in email drafter and report generator
  • Use Cases:
    • Blog post generation
    • Email content creation
    • Social media posts
    • Product descriptions

Enhancement: Hugging Face Transformers (Optional)​

# Already have: transformers>=4.36.0
  • Why: Fine-tuning for specific brand voices
  • Use Cases:
    • Custom brand voice models
    • Domain-specific content generation
    • Tone classification

Template Engine: Jinja2 ✅ (Already in use)​

# Install: jinja2>=3.1.0
  • Current Usage: Used in report_generator.py for HTML generation
  • Why: Industry-standard, powerful, flexible
  • Use Cases:
    • Content templates
    • Email templates
    • Report formatting

2.2 Personalization & Recommendation​

User Segmentation: scikit-learn ✅ (Already in use)​

# Already have for clustering in segmentation.py
  • Current Usage: K-Means, DBSCAN in user segmentation
  • Use Cases:
    • Audience clustering
    • Content preference modeling

Content Recommendation: LightFM (NEW - Optional)​

pip install lightfm>=1.17
  • Why: Hybrid collaborative + content filtering
  • Use Cases:
    • Content type recommendations
    • Template recommendations
    • Personalized content suggestions

2.3 Brand Voice & Style​

Style Analysis: spaCy ✅ (Already in use)​

# Already have: spacy>=3.7.0
  • Why: NLP for style analysis and entity recognition
  • Use Cases:
    • Brand terminology extraction
    • Style pattern analysis
    • Entity consistency checking

Text Similarity: sentence-transformers ✅ (Already in use)​

# Already have: sentence-transformers>=2.2.2
  • Why: Measure brand voice consistency
  • Use Cases:
    • Brand voice similarity scoring
    • Content consistency checking
    • Style matching

Style Transfer: PEFT + LoRA (NEW - Optional)​

pip install peft>=0.7.0
  • Why: Fine-tune LLMs for specific brand voices without full retraining
  • Use Cases:
    • Brand voice adaptation
    • Tone transfer
    • Style customization

2.4 Compliance & Fact-Checking​

Fact-Checking: ClaimBuster API (NEW - Optional)​

  • Why: Automated fact-checking
  • Use Cases:
    • Statistical claim verification
    • Factual accuracy checking

Content Moderation: Detoxify (NEW)​

pip install detoxify>=0.5.0
  • Why: Ensure content appropriateness
  • Use Cases:
    • Toxicity detection
    • Inappropriate content filtering
    • Sentiment validation

Plagiarism Detection: copydetect (NEW)​

pip install copydetect>=1.3.0
  • Why: Ensure content originality
  • Use Cases:
    • Plagiarism detection
    • Content uniqueness verification

2.5 Content Quality & SEO​

Readability Analysis: textstat (NEW)​

pip install textstat>=0.7.3
  • Why: Measure content readability
  • Use Cases:
    • Flesch reading ease
    • Grade level analysis
    • Content accessibility

SEO Optimization: yake (NEW)​

pip install yake>=0.4.8
  • Why: Keyword extraction for SEO
  • Use Cases:
    • Keyword optimization
    • Content SEO scoring
    • Meta description generation

Grammar Checking: language-tool-python (NEW)​

pip install language-tool-python>=2.8.0
  • Why: Grammar and style checking
  • Use Cases:
    • Grammar validation
    • Style consistency
    • Professional writing standards

2.6 A/B Testing & Experimentation​

Experimentation: planout (NEW - Optional)​

pip install planout>=0.6
  • Why: A/B testing framework for content
  • Use Cases:
    • Content variant testing
    • Headline optimization
    • CTA testing

2.7 Content Scheduling & Distribution​

Social Media: python-social-auth (NEW - Optional)​

pip install social-auth-app-django>=5.0.0
  • Why: Multi-platform content distribution
  • Use Cases:
    • LinkedIn posting
    • Twitter integration
    • Multi-channel distribution

3. Service Architecture​

┌─────────────────────────────────────────────────────────────┐
│                  Content Generation Service                  │
└─────────────────────────────────────────────────────────────┘
                              │
         ┌────────────────────┼────────────────────┐
         │                    │                    │
         ▼                    ▼                    ▼
┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐
│  Marketing      │  │  Sales          │  │  Compliance     │
│  Content        │  │  Content        │  │  Checker        │
│  Generator      │  │  Automation     │  │                 │
└─────────────────┘  └─────────────────┘  └─────────────────┘
         │                    │                    │
         └────────────────────┼────────────────────┘
                              │
         ┌────────────────────┼────────────────────┐
         │                    │                    │
         ▼                    ▼                    ▼
┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐
│  Template       │  │  Personalization│  │  Brand Voice    │
│  Engine         │  │  Engine         │  │  Enforcer       │
│  (Jinja2)       │  │  (Segmentation) │  │  (Style Match)  │
└─────────────────┘  └─────────────────┘  └─────────────────┘
         │                    │                    │
         └────────────────────┼────────────────────┘
                              │
         ┌────────────────────┼────────────────────┐
         │                    │                    │
         ▼                    ▼                    ▼
┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐
│  Content        │  │  RAG Context    │  │  LLM Generation │
│  Templates      │  │  Retrieval      │  │  (GPT-4o)       │
│  Library        │  │                 │  │                 │
└─────────────────┘  └─────────────────┘  └─────────────────┘
         │                    │                    │
         └────────────────────┼────────────────────┘
                              │
         ┌────────────────────┼────────────────────┐
         │                    │                    │
         ▼                    ▼                    ▼
┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐
│  Quality        │  │  Compliance     │  │  Analytics &    │
│  Scoring        │  │  Validation     │  │  Tracking       │
└─────────────────┘  └─────────────────┘  └─────────────────┘

3.1 Core Components​

Component 1: Content Generator Engine​

  • Purpose: Generate various content types
  • Built On:
    • Existing ReportGenerator (80% complete)
    • Existing EmailDrafter (90% complete)
    • New MarketingContentGenerator
  • Content Types:
    • Blog posts
    • Email campaigns
    • Social media posts
    • Product descriptions
    • Case studies
    • Whitepapers
    • Press releases

Component 2: Template Library​

  • Purpose: Store and manage content templates
  • Built On:
    • Extend existing QueryTemplateManager
    • Jinja2 templates
  • Template Types:
    • Marketing templates (blog, social, email)
    • Sales templates (outreach, proposals)
    • Brand voice templates
    • Industry-specific templates

Component 3: Personalization Engine​

  • Purpose: Tailor content to user segments
  • Built On:
    • Existing UserSegmentation (75% complete)
    • Existing SearchPersonalizationEngine
  • Features:
    • User segment identification
    • Content preference mapping
    • Personalized content recommendations
    • Dynamic content adaptation

Component 4: Brand Voice System​

  • Purpose: Ensure brand consistency
  • Built On:
    • New development (0%)
    • sentence-transformers for similarity
    • spaCy for style analysis
  • Features:
    • Brand voice definition and storage
    • Style consistency scoring
    • Terminology enforcement
    • Tone matching

Component 5: Compliance Checker​

  • Purpose: Validate content compliance
  • Built On:
    • Existing ComplianceAgent (70% complete)
    • New marketing-specific rules
  • Features:
    • Brand guideline compliance
    • Legal/regulatory review
    • Fact-checking integration
    • Plagiarism detection
    • Grammar validation

Component 6: Quality Assurance​

  • Purpose: Score and validate content quality
  • Built On:
    • Extend existing quality scoring
    • New metrics for marketing content
  • Metrics:
    • Readability score
    • SEO score
    • Brand voice consistency
    • Engagement prediction
    • Compliance score

4. Data Models​

4.1 Content Generation Request​

from pydantic import BaseModel, Field
from typing import List, Optional, Dict, Any
from enum import Enum

class ContentType(str, Enum):
    BLOG_POST = "blog_post"
    EMAIL_CAMPAIGN = "email_campaign"
    SOCIAL_MEDIA_POST = "social_media_post"
    PRODUCT_DESCRIPTION = "product_description"
    CASE_STUDY = "case_study"
    WHITEPAPER = "whitepaper"
    PRESS_RELEASE = "press_release"
    SALES_OUTREACH = "sales_outreach"
    PROPOSAL = "proposal"

class AudienceSegment(str, Enum):
    EXECUTIVES = "executives"
    TECHNICAL = "technical"
    BUSINESS_USERS = "business_users"
    CONSUMERS = "consumers"
    PARTNERS = "partners"
    INVESTORS = "investors"

class Tone(str, Enum):
    PROFESSIONAL = "professional"
    FRIENDLY = "friendly"
    AUTHORITATIVE = "authoritative"
    CONVERSATIONAL = "conversational"
    INSPIRATIONAL = "inspirational"
    HUMOROUS = "humorous"

class ContentGenerationRequest(BaseModel):
    request_id: str = Field(default_factory=lambda: f"req_{uuid.uuid4().hex[:12]}")
    content_type: ContentType
    topic: str = Field(..., description="Main topic or subject")
    audience_segment: AudienceSegment
    tone: Tone = Tone.PROFESSIONAL
    
    # Personalization
    user_context: Optional[Dict[str, Any]] = None
    target_keywords: Optional[List[str]] = None
    brand_voice_id: Optional[str] = None
    
    # Content specifications
    min_words: Optional[int] = None
    max_words: Optional[int] = None
    include_call_to_action: bool = True
    include_statistics: bool = False
    
    # Compliance
    require_fact_checking: bool = True
    require_plagiarism_check: bool = True
    
    # Generation parameters
    temperature: float = 0.7
    use_rag: bool = True
    rag_sources: Optional[List[str]] = None
    
    # Metadata
    campaign_id: Optional[str] = None
    created_by: str
    metadata: Dict[str, Any] = Field(default_factory=dict)

4.2 Generated Content Response​

class ContentQualityScores(BaseModel):
    overall_quality: float = Field(..., ge=0, le=1)
    brand_voice_consistency: float = Field(..., ge=0, le=1)
    readability_score: float = Field(..., ge=0, le=100)
    seo_score: float = Field(..., ge=0, le=100)
    engagement_prediction: float = Field(..., ge=0, le=1)
    compliance_score: float = Field(..., ge=0, le=1)
    grammar_score: float = Field(..., ge=0, le=1)

class ComplianceCheck(BaseModel):
    passed: bool
    issues: List[str] = Field(default_factory=list)
    warnings: List[str] = Field(default_factory=list)
    recommendations: List[str] = Field(default_factory=list)

class GeneratedContent(BaseModel):
    content_id: str = Field(default_factory=lambda: f"content_{uuid.uuid4().hex[:12]}")
    request_id: str
    
    # Content
    title: str
    body: str
    summary: Optional[str] = None
    call_to_action: Optional[str] = None
    
    # Metadata
    content_type: ContentType
    word_count: int
    character_count: int
    estimated_reading_time_minutes: int
    
    # Quality scores
    quality_scores: ContentQualityScores
    
    # Compliance
    compliance_check: ComplianceCheck
    fact_checks: List[Dict[str, Any]] = Field(default_factory=list)
    plagiarism_check_passed: bool = True
    
    # Generation details
    template_used: Optional[str] = None
    brand_voice_id: Optional[str] = None
    retrieved_context: List[str] = Field(default_factory=list)
    generation_time_seconds: float
    tokens_used: int
    cost: float
    
    # Variants (for A/B testing)
    variants: List[Dict[str, Any]] = Field(default_factory=list)
    
    # Timestamps
    created_at: datetime = Field(default_factory=datetime.utcnow)
    expires_at: Optional[datetime] = None

4.3 Brand Voice Profile​

class BrandVoiceProfile(BaseModel):
    voice_id: str = Field(default_factory=lambda: f"voice_{uuid.uuid4().hex[:12]}")
    name: str = Field(..., description="Brand voice name")
    description: str
    
    # Style characteristics
    tone: List[str] = Field(..., description="e.g., professional, friendly, authoritative")
    vocabulary_level: str = Field(..., description="e.g., technical, accessible, simple")
    sentence_structure: str = Field(..., description="e.g., short, varied, complex")
    
    # Brand-specific
    key_phrases: List[str] = Field(default_factory=list)
    avoid_phrases: List[str] = Field(default_factory=list)
    preferred_terminology: Dict[str, str] = Field(default_factory=dict)
    
    # Style examples
    example_texts: List[str] = Field(default_factory=list)
    reference_embeddings: Optional[List[float]] = None
    
    # Compliance rules
    compliance_rules: List[str] = Field(default_factory=list)
    legal_disclaimers: List[str] = Field(default_factory=list)
    
    # Metadata
    created_by: str
    created_at: datetime = Field(default_factory=datetime.utcnow)
    updated_at: datetime = Field(default_factory=datetime.utcnow)
    version: int = 1

4.4 Content Template​

class ContentTemplate(BaseModel):
    template_id: str = Field(default_factory=lambda: f"template_{uuid.uuid4().hex[:12]}")
    name: str
    description: str
    content_type: ContentType
    
    # Template content
    template_text: str = Field(..., description="Jinja2 template")
    required_variables: List[str] = Field(default_factory=list)
    optional_variables: List[str] = Field(default_factory=list)
    
    # Configuration
    default_tone: Tone = Tone.PROFESSIONAL
    target_audience: List[AudienceSegment] = Field(default_factory=list)
    min_words: Optional[int] = None
    max_words: Optional[int] = None
    
    # Style
    structure_guidelines: Dict[str, Any] = Field(default_factory=dict)
    formatting_rules: Dict[str, Any] = Field(default_factory=dict)
    
    # Usage metrics
    usage_count: int = 0
    average_quality_score: Optional[float] = None
    
    # Metadata
    created_by: str
    created_at: datetime = Field(default_factory=datetime.utcnow)
    tags: List[str] = Field(default_factory=list)

5. Implementation Phases​

Phase 1: Foundation (Week 1-2)​

Goals​

  • Set up service architecture
  • Extend existing components
  • Create data models

Tasks​

Week 1:

  1. ✅ Create service directory structure
  2. ✅ Define data models (Pydantic schemas)
  3. ✅ Extend ReportGenerator for marketing content
  4. ✅ Create ContentGenerator base class
  5. ✅ Set up API endpoints (FastAPI)

Week 2: 6. ✅ Create template library structure 7. ✅ Design Jinja2 marketing templates (blog, email, social) 8. ✅ Integrate existing user segmentation 9. ✅ Create PersonalizationEngine wrapper 10. ✅ Set up testing framework

Deliverables:

  • Service architecture implemented
  • Data models defined
  • Basic API endpoints
  • Template infrastructure
  • Unit tests

Dependencies:

  • Existing: LangChain, OpenAI, Jinja2
  • New: None required

Phase 2: Marketing Content Generator (Week 3-4)​

Goals​

  • Implement marketing-specific content generation
  • Create content templates
  • Integrate RAG for context

Tasks​

Week 3:

  1. ✅ Implement BlogPostGenerator
  2. ✅ Implement EmailCampaignGenerator
  3. ✅ Implement SocialMediaGenerator
  4. ✅ Create 10+ blog post templates
  5. ✅ Create 10+ email templates

Week 4: 6. ✅ Create 20+ social media templates (LinkedIn, Twitter, Facebook) 7. ✅ Integrate RAG for contextual content 8. ✅ Implement content variation generator (A/B testing) 9. ✅ Add SEO optimization (keyword integration) 10. ✅ Integration tests

Deliverables:

  • Marketing content generators
  • 40+ content templates
  • RAG integration
  • A/B testing support

Dependencies:

  • Existing: ReportGenerator, EmailDrafter, RAG retriever
  • New: yake (SEO), textstat (readability)

Phase 3: Brand Voice System (Week 4-5)​

Goals​

  • Develop brand voice consistency system
  • Create brand voice profiles
  • Implement style matching

Tasks​

Week 4-5:

  1. ✅ Design BrandVoiceProfile schema
  2. ✅ Implement brand voice analyzer (sentence-transformers)
  3. ✅ Create brand voice consistency scorer
  4. ✅ Implement style transfer prompts
  5. ✅ Create brand voice templates (3-5 examples)
  6. ✅ Integrate with content generator
  7. ✅ Add terminology enforcement
  8. ✅ Build brand voice training interface
  9. ✅ Validation tests
  10. ✅ Documentation

Deliverables:

  • Brand voice system
  • Style consistency scoring
  • Brand voice profiles
  • Terminology enforcement

Dependencies:

  • Existing: sentence-transformers, spaCy
  • New: None required (optional: PEFT for fine-tuning)

Phase 4: Sales Content Automation (Week 5-6)​

Goals​

  • Implement sales-specific content generation
  • Create sales templates
  • Personalized outreach system

Tasks​

Week 5:

  1. ✅ Implement SalesOutreachGenerator
  2. ✅ Implement ProposalGenerator
  3. ✅ Implement CaseStudyGenerator
  4. ✅ Create 10+ sales email templates
  5. ✅ Create 5+ proposal templates

Week 6: 6. ✅ Integrate CRM data for personalization 7. ✅ Implement dynamic personalization (company name, pain points) 8. ✅ Add follow-up email sequencing 9. ✅ Create case study template system 10. ✅ Integration tests

Deliverables:

  • Sales content generators
  • Personalized outreach system
  • Proposal and case study generation
  • 15+ sales templates

Dependencies:

  • Existing: EmailDrafter, UserSegmentation
  • New: None required

Phase 5: Compliance & Quality Assurance (Week 6-7)​

Goals​

  • Implement compliance checking for marketing content
  • Add quality scoring
  • Integrate fact-checking

Tasks​

Week 6-7:

  1. ✅ Extend ComplianceAgent for marketing
  2. ✅ Create marketing compliance rules
  3. ✅ Implement fact-checking integration
  4. ✅ Add plagiarism detection (copydetect)
  5. ✅ Add grammar checking (language-tool-python)
  6. ✅ Implement readability scoring (textstat)
  7. ✅ Implement SEO scoring
  8. ✅ Create comprehensive quality scorer
  9. ✅ Add content moderation (detoxify)
  10. ✅ Compliance tests

Deliverables:

  • Marketing compliance checker
  • Fact-checking integration
  • Quality scoring system
  • Grammar and plagiarism detection

Dependencies:

  • Existing: ComplianceAgent
  • New: detoxify, copydetect, language-tool-python, textstat

Phase 6: Testing & Refinement (Week 7-8)​

Goals​

  • End-to-end testing
  • Performance optimization
  • User acceptance testing

Tasks​

Week 7:

  1. ✅ End-to-end integration tests
  2. ✅ Performance testing (load testing)
  3. ✅ Quality assurance testing
  4. ✅ Security testing
  5. ✅ Bug fixes

Week 8: 6. ✅ User acceptance testing 7. ✅ Documentation completion 8. ✅ API documentation (OpenAPI/Swagger) 9. ✅ Deployment preparation 10. ✅ Launch readiness review

Deliverables:

  • Fully tested service
  • Complete documentation
  • Performance optimized
  • Production-ready

6. API Endpoints​

6.1 Content Generation Endpoints​

POST /api/v1/content/generate

Generate content based on specifications

Request:

{
  "content_type": "blog_post",
  "topic": "The Future of AI in Marketing",
  "audience_segment": "business_users",
  "tone": "professional",
  "target_keywords": ["AI", "marketing automation", "personalization"],
  "brand_voice_id": "voice_abc123",
  "min_words": 800,
  "max_words": 1200,
  "use_rag": true,
  "require_fact_checking": true
}

Response:

{
  "content_id": "content_xyz789",
  "title": "The Future of AI in Marketing: Transform Your Strategy",
  "body": "...",
  "summary": "...",
  "quality_scores": {
    "overall_quality": 0.92,
    "brand_voice_consistency": 0.95,
    "readability_score": 68.5,
    "seo_score": 85,
    "engagement_prediction": 0.78
  },
  "compliance_check": {
    "passed": true,
    "issues": [],
    "warnings": []
  },
  "generation_time_seconds": 12.5,
  "cost": 0.045
}
POST /api/v1/content/generate-batch

Generate multiple content variations for A/B testing

GET /api/v1/content/{content_id}

Retrieve generated content

POST /api/v1/content/{content_id}/regenerate

Regenerate content with different parameters

6.2 Template Management​

POST /api/v1/templates

Create new content template

GET /api/v1/templates

List all templates (with filters)

GET /api/v1/templates/{template_id}

Get template details

PUT /api/v1/templates/{template_id}

Update template

6.3 Brand Voice Management​

POST /api/v1/brand-voices

Create brand voice profile

GET /api/v1/brand-voices

List brand voices

POST /api/v1/brand-voices/{voice_id}/analyze

Analyze text for brand voice consistency

Request:

{
  "text": "Content to analyze..."
}

Response:

{
  "consistency_score": 0.87,
  "matches": ["professional tone", "key phrases used"],
  "violations": ["avoided phrase found"],
  "recommendations": ["Consider replacing..."]
}

6.4 Compliance & Quality​

POST /api/v1/compliance/check

Check content for compliance

POST /api/v1/quality/analyze

Analyze content quality

Request:

{
  "content": "Text to analyze...",
  "brand_voice_id": "voice_abc123"
}

Response:

{
  "quality_scores": {
    "overall_quality": 0.92,
    "readability_score": 68.5,
    "seo_score": 85,
    "grammar_score": 0.98
  },
  "issues": [],
  "recommendations": ["Add more transition words", "Include statistics"]
}

6.5 Analytics & Reporting​

GET /api/v1/analytics/content-performance

Get content performance metrics

GET /api/v1/analytics/generation-stats

Get generation statistics

7. Integration Strategy​

7.1 Integration with Existing Services​

Integration Point 1: RAG System​

# Use existing RAG retriever for contextual content
from packages.rag.retriever import EnhancedRetriever

retriever = EnhancedRetriever(...)
context = await retriever.retrieve(
    query=f"information about {topic}",
    k=5,
    rerank=True
)

Integration Point 2: User Segmentation​

# Use existing segmentation for personalization
from packages.analytics.segmentation import UserSegmentation

segmentation = UserSegmentation(analytics_engine)
user_profile = await segmentation.get_user_profile(user_id)

# Generate content personalized for user type
content = await generator.generate(
    topic=topic,
    user_segment=user_profile.user_type,
    preferences=user_profile.preferences
)

Integration Point 3: Compliance System​

# Use existing compliance agent for validation
from packages.rag.compliance_agent import ComplianceAgent

compliance = ComplianceAgent()
validation = await compliance.validate_content(
    content=generated_content,
    domain="marketing"
)

Integration Point 4: Prompt Optimization​

# Use DSPy for continuous improvement
from packages.prompts import PromptOptimizer

optimizer = PromptOptimizer()
optimized_generator = optimizer.optimize(
    module=content_generator,
    training_data=feedback_examples,
    metric=engagement_metric
)

7.2 Integration with External Systems​

CRM Integration (Optional - Future)​

# Salesforce, HubSpot integration for personalization
from external_integrations.crm import CRMClient

crm = CRMClient()
lead_data = await crm.get_lead(lead_id)

content = await generator.generate_sales_email(
    lead_data=lead_data,
    template="cold_outreach"
)

Email Marketing Platform (Optional - Future)​

# SendGrid, Mailchimp integration
from external_integrations.email import EmailPlatform

email_platform = EmailPlatform()
await email_platform.create_campaign(
    content=generated_email,
    segment=audience_segment
)

Social Media Management (Optional - Future)​

# Hootsuite, Buffer integration
from external_integrations.social import SocialMediaManager

social_manager = SocialMediaManager()
await social_manager.schedule_post(
    content=generated_post,
    platforms=["linkedin", "twitter"],
    schedule_time=datetime(...)
)

8. Testing Strategy​

8.1 Unit Tests​

# Test content generation
def test_blog_post_generation():
    generator = BlogPostGenerator()
    request = ContentGenerationRequest(
        content_type=ContentType.BLOG_POST,
        topic="AI in Healthcare",
        audience_segment=AudienceSegment.TECHNICAL,
        tone=Tone.PROFESSIONAL
    )
    
    result = await generator.generate(request)
    
    assert result.content_id is not None
    assert len(result.body) >= request.min_words
    assert result.quality_scores.overall_quality > 0.7
# Test brand voice consistency
def test_brand_voice_consistency():
    brand_voice = BrandVoiceProfile(...)
    analyzer = BrandVoiceAnalyzer()
    
    score = analyzer.analyze_consistency(
        text=generated_content,
        brand_voice=brand_voice
    )
    
    assert score >= 0.8

8.2 Integration Tests​

# Test end-to-end content generation pipeline
def test_e2e_content_generation():
    # 1. Request generation
    request = ContentGenerationRequest(...)
    
    # 2. Generate content
    content = await content_service.generate(request)
    
    # 3. Check quality
    assert content.quality_scores.overall_quality > 0.7
    
    # 4. Check compliance
    assert content.compliance_check.passed is True
    
    # 5. Verify storage
    retrieved = await content_service.get_content(content.content_id)
    assert retrieved.content_id == content.content_id

8.3 Performance Tests​

# Load testing
def test_concurrent_generation():
    requests = [ContentGenerationRequest(...) for _ in range(100)]
    
    start = time.time()
    results = await asyncio.gather(*[
        content_service.generate(req) for req in requests
    ])
    duration = time.time() - start
    
    assert duration < 60  # All requests in under 60 seconds
    assert all(r.content_id is not None for r in results)

9. Deployment Plan​

9.1 Infrastructure Requirements​

# Docker Compose for development
version: '3.8'
services:
  content_generation_api:
    build: .
    ports:
      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - REDIS_URL=redis://redis:6379
    depends_on:
      - redis
      - postgres
  
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
  
  postgres:
    image: postgres:15-alpine
    environment:
      - POSTGRES_DB=content_generation
      - POSTGRES_PASSWORD=${DB_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  postgres_data:

9.2 Monitoring & Observability​

# Metrics to track
metrics = {
    "content_generation_requests_total": Counter,
    "content_generation_duration_seconds": Histogram,
    "content_quality_score": Gauge,
    "brand_voice_consistency_score": Gauge,
    "compliance_failures_total": Counter,
    "api_errors_total": Counter,
    "token_usage_total": Counter,
    "generation_cost_dollars": Counter
}

9.3 Cost Estimation​

ComponentCost per 1000 requestsNotes
LLM API (GPT-4o)$15-30~1500 tokens per request
RAG Retrieval$0.10Vector search
Compliance Checking$0.50Additional LLM calls
Infrastructure$2-5Servers, caching
Total$17.60-35.60per 1000 requests

Monthly Cost Projections:

  • 10,000 requests/month: $176-356
  • 100,000 requests/month: $1,760-3,560
  • 1,000,000 requests/month: $17,600-35,600

10. Success Metrics​

10.1 Technical Metrics​

MetricTargetMeasurement
API Response Time< 15s (p95)Prometheus
Content Quality Score> 0.85Internal scoring
Brand Voice Consistency> 0.90Style matching
Compliance Pass Rate> 95%Validation checks
Uptime> 99.5%Monitoring

10.2 Business Metrics​

MetricTargetMeasurement
Content Generation Volume10,000/monthUsage analytics
Cost per Content Piece< $0.05Cost tracking
User Satisfaction> 4.2/5Feedback surveys
Time Saved vs Manual> 80%User surveys
Conversion Rate Improvement+20%A/B testing

11. Risk Assessment​

11.1 Technical Risks​

RiskImpactMitigation
LLM API rate limitsHighImplement queuing, caching, fallback
Content quality inconsistencyMediumMulti-stage validation, human review option
Brand voice driftMediumRegular calibration, feedback loop
Scalability issuesMediumHorizontal scaling, load balancing

11.2 Business Risks​

RiskImpactMitigation
User adoptionHighTraining, documentation, easy onboarding
Cost overrunsMediumBudget monitoring, usage caps
Compliance violationsHighStrict validation, human oversight
CompetitionMediumContinuous improvement, unique features

12. Next Steps (Immediate Actions)​

Week 1 Actions​

  1. Monday: Review and approve this plan
  2. Tuesday: Set up project structure and repository
  3. Wednesday: Define data models and API contracts
  4. Thursday: Extend ReportGenerator for marketing content
  5. Friday: Create initial content templates (blog, email)

Week 2 Actions​

  1. Monday: Implement BlogPostGenerator
  2. Tuesday: Implement EmailCampaignGenerator
  3. Wednesday: Integrate RAG for contextual generation
  4. Thursday: Create PersonalizationEngine wrapper
  5. Friday: Week 1-2 review and testing

13. Dependencies & Prerequisites​

13.1 Required Dependencies (Already Available)​

# Already in requirements.txt
langchain>=0.1.0
langgraph>=0.0.40
openai>=1.12.0
sentence-transformers>=2.2.2
transformers>=4.36.0
spacy>=3.7.0
jinja2>=3.1.0  # Add if not present
scikit-learn>=1.3.0  # Already have via numpy/scipy

13.2 New Dependencies to Add​

# Add to requirements.txt

# Quality & Compliance
textstat>=0.7.3           # Readability analysis
language-tool-python>=2.8.0  # Grammar checking
detoxify>=0.5.0           # Content moderation
copydetect>=1.3.0         # Plagiarism detection

# SEO & Content Optimization
yake>=0.4.8               # Keyword extraction

# Optional (Future Enhancements)
peft>=0.7.0              # LoRA fine-tuning for brand voice
lightfm>=1.17            # Content recommendations
planout>=0.6             # A/B testing framework

13.3 Infrastructure Prerequisites​

  • ✅ OpenAI API key with GPT-4o access
  • ✅ Redis for caching (already in use)
  • ✅ PostgreSQL for data storage
  • ⚠️ Storage for content templates (S3 or local)
  • ⚠️ Monitoring (Prometheus + Grafana)

14. Documentation Plan​

14.1 Technical Documentation​

  1. API Documentation (OpenAPI/Swagger)

    • All endpoints with examples
    • Request/response schemas
    • Error codes and handling

  2. Integration Guide

    • How to integrate with existing systems
    • Code examples in Python
    • Common patterns and best practices

  3. Developer Guide

    • Architecture overview
    • Component descriptions
    • Extension points

14.2 User Documentation​

  1. Getting Started Guide

    • Quick start tutorial
    • Basic examples
    • Common use cases

  2. User Guide

    • Content types and use cases
    • Personalization options
    • Brand voice management
    • Template creation

  3. Best Practices

    • Content quality tips
    • SEO optimization
    • A/B testing strategies

15. Conclusion​

This plan leverages 80% of existing capabilities in RecoAgent while adding focused enhancements for personalized content generation. The service will be production-ready in 6-8 weeks with the following key advantages:

Your Competitive Edge​

  1. ✅ Proven Foundation: Built on battle-tested report generator and email drafter
  2. ✅ RAG Integration: Context-aware content from your knowledge base
  3. ✅ User Segmentation: Personalized content based on sophisticated user profiling
  4. ✅ Compliance First: Built-in compliance checking and validation
  5. ✅ Quality Assurance: Multi-dimensional quality scoring

Unique Differentiators​

  • Report Generation Heritage: Professional, well-structured long-form content
  • Compliance Expertise: Regulatory validation and audit trails
  • Source Verification: RAG-based factual grounding
  • User Segmentation: Data-driven personalization

Market Position​

  • Target Market: $12B content marketing AI opportunity
  • Immediate Value: 40% higher conversion through personalization
  • Time to Market: 6-8 weeks (vs 6+ months building from scratch)
  • Cost Efficiency: Leverage existing 80% infrastructure

Appendix A: File Structure​

recoagent/
├── packages/
│   ├── content_generation/
│   │   ├── __init__.py
│   │   ├── core.py                      # Base content generator
│   │   ├── marketing_generator.py        # Marketing content
│   │   ├── sales_generator.py           # Sales content
│   │   ├── blog_generator.py            # Blog posts
│   │   ├── email_generator.py           # Email campaigns
│   │   ├── social_generator.py          # Social media
│   │   ├── brand_voice/
│   │   │   ├── __init__.py
│   │   │   ├── analyzer.py              # Brand voice analysis
│   │   │   ├── enforcer.py              # Style enforcement
│   │   │   └── profiles.py              # Voice profiles
│   │   ├── templates/
│   │   │   ├── __init__.py
│   │   │   ├── manager.py               # Template management
│   │   │   ├── blog/                    # Blog templates
│   │   │   ├── email/                   # Email templates
│   │   │   └── social/                  # Social templates
│   │   ├── personalization/
│   │   │   ├── __init__.py
│   │   │   ├── engine.py                # Personalization
│   │   │   └── segment_mapper.py        # Segment mapping
│   │   ├── quality/
│   │   │   ├── __init__.py
│   │   │   ├── scorer.py                # Quality scoring
│   │   │   ├── readability.py           # Readability
│   │   │   ├── seo.py                   # SEO analysis
│   │   │   └── grammar.py               # Grammar check
│   │   └── compliance/
│   │       ├── __init__.py
│   │       ├── checker.py               # Compliance check
│   │       ├── fact_checker.py          # Fact checking
│   │       └── plagiarism.py            # Plagiarism detect
│   │
├── apps/
│   └── api/
│       └── content_generation_api.py     # FastAPI endpoints
│
├── data/
│   └── content_templates/
│       ├── blog_templates/
│       ├── email_templates/
│       ├── social_templates/
│       └── brand_voices/
│
├── docs/
│   └── docs/
│       └── services/
│           └── personalized-content-generation/
│               ├── SERVICE_PLAN.md       # This document
│               ├── QUICK_START.md        # Getting started
│               ├── API_REFERENCE.md      # API docs
│               ├── USER_GUIDE.md         # User guide
│               └── BEST_PRACTICES.md     # Best practices
│
└── tests/
    └── content_generation/
        ├── test_generators.py
        ├── test_brand_voice.py
        ├── test_quality.py
        └── test_integration.py

Appendix B: Sample Templates​

B.1 Blog Post Template (Jinja2)​

# {{ title }}

{{ subtitle }}

*Published on {{ publish_date }} | Reading time: {{ reading_time }} minutes*

---

## Introduction

{{ introduction }}

{% for section in sections %}
## {{ section.title }}

{{ section.content }}

{% if section.statistics %}
**Key Statistics:**
{% for stat in section.statistics %}
- {{ stat }}
{% endfor %}
{% endif %}

{% if section.example %}
**Example:**
{{ section.example }}
{% endif %}

{% endfor %}

## Conclusion

{{ conclusion }}

{% if call_to_action %}
---

## {{ call_to_action.title }}

{{ call_to_action.content }}

[{{ call_to_action.button_text }}]({{ call_to_action.link }})
{% endif %}

---

*Tags: {{ tags|join(', ') }}*

B.2 Email Campaign Template​

Subject: {{ subject }}

Hi {{ recipient_name }},

{{ opening }}

{{ body_paragraph_1 }}

{% if include_statistics %}
**Did you know?**
{{ statistics }}
{% endif %}

{{ body_paragraph_2 }}

{% if testimonial %}
> "{{ testimonial.quote }}"
> — {{ testimonial.author }}, {{ testimonial.title }}
{% endif %}

{{ closing }}

{% if call_to_action %}
[{{ call_to_action.button_text }}]({{ call_to_action.link }})
{% endif %}

Best regards,
{{ sender_name }}
{{ sender_title }}

---
{{ footer }}
[Unsubscribe]({{ unsubscribe_link }})

Document Version: 1.0
Last Updated: October 2024
Author: RecoAgent Planning Team
Status: Ready for Review & Implementation