Zero to Production: Building a Custom Market Intelligence Platform in 48 Hours

# intelligence_requirements.py
 
INTELLIGENCE_STRATEGY = {
    'primary_objectives': [
        'Track top 20 competitors in real-time',
        'Identify market opportunities within 24 hours',
        'Predict competitor moves 2-4 weeks in advance',
        'Monitor customer sentiment across all channels',
        'Aggregate industry trends and insights'
    ],
    
    'data_sources': {
        'competitors': [
            'Website changes and updates',
            'Pricing and product catalogs',
            'Job postings and hiring patterns',
            'Press releases and announcements',
            'Social media activity',
            'Customer reviews'
        ],
        'market': [
            'Industry news sites',
            'Trade publications',
            'Analyst reports',
            'Regulatory filings',
            'Conference proceedings'
        ],
        'customers': [
            'Review platforms',
            'Social media',
            'Community forums',
            'Support channels',
            'User communities'
        ]
    },
    
    'update_frequency': {
        'competitive_pricing': '15 minutes',
        'website_changes': '1 hour',
        'job_postings': '4 hours',
        'news': '30 minutes',
        'social_media': '15 minutes',
        'reviews': '1 hour'
    },
    
    'stakeholders': {
        'executives': ['Strategic dashboards', 'Weekly briefs'],
        'product': ['Feature requests', 'Competitive gaps'],
        'sales': ['Win/loss intelligence', 'Competitor weaknesses'],
        'marketing': ['Messaging gaps', 'Campaign opportunities']
    }
}

# platform_architecture.py
 
from scrapegraph_py import Client
from datetime import datetime
import json
 
class MarketIntelligencePlatform:
    """
    Core platform architecture for market intelligence
    Built to scale from startup to enterprise
    """
    
    def __init__(self, config):
        self.client = Client(api_key=config['scrapegraph_api_key'])
        self.config = config
        
        # Initialize core components
        self.collectors = {}
        self.processors = {}
        self.analyzers = {}
        self.deliverers = {}
        
        # Storage systems
        self.raw_data_store = self.initialize_raw_storage()
        self.processed_data_store = self.initialize_processed_storage()
        self.intelligence_store = self.initialize_intelligence_storage()
        
        # Initialize platform components
        self.setup_collectors()
        self.setup_processors()
        self.setup_analyzers()
        self.setup_deliverers()
    
    def initialize_raw_storage(self):
        """
        Set up raw data storage
        Recommended: S3 for scalability, low cost
        """
        return {
            'type': 's3',
            'bucket': 'market-intelligence-raw',
            'retention_days': 365,
            'compression': True
        }
    
    def initialize_processed_storage(self):
        """
        Set up processed data storage  
        Recommended: PostgreSQL for structured queries
        """
        return {
            'type': 'postgresql',
            'database': 'market_intelligence',
            'tables': {
                'competitors': 'competitor_data',
                'market_trends': 'market_data',
                'customer_intel': 'customer_data',
                'insights': 'generated_insights'
            }
        }
    
    def initialize_intelligence_storage(self):
        """
        Set up intelligence storage
        Recommended: Vector DB for semantic search
        """
        return {
            'type': 'pinecone',  # or Weaviate, Qdrant
            'index': 'market-intelligence-embeddings',
            'dimensions': 1536,  # OpenAI embeddings
            'metric': 'cosine'
        }
    
    def setup_collectors(self):
        """Initialize all data collectors"""
        self.collectors = {
            'competitive': CompetitiveIntelligenceCollector(self.client, self.config),
            'market': MarketIntelligenceCollector(self.client, self.config),
            'customer': CustomerIntelligenceCollector(self.client, self.config),
            'financial': FinancialIntelligenceCollector(self.client, self.config)
        }
    
    def setup_processors(self):
        """Initialize data processors"""
        self.processors = {
            'nlp': NaturalLanguageProcessor(),
            'enrichment': DataEnrichmentEngine(),
            'validation': DataQualityValidator()
        }
    
    def setup_analyzers(self):
        """Initialize intelligence analyzers"""
        self.analyzers = {
            'trends': TrendAnalyzer(),
            'competitive': CompetitivePositioningAnalyzer(),
            'opportunities': OpportunityDetector(),
            'risks': RiskAssessmentEngine()
        }
    
    def setup_deliverers(self):
        """Initialize delivery mechanisms"""
        self.deliverers = {
            'dashboard': DashboardGenerator(),
            'alerts': AlertSystem(),
            'reports': ReportGenerator(),
            'api': APIServer()
        }
 
# Initialize platform
config = {
    'scrapegraph_api_key': 'your-api-key',
    'competitors': ['competitor1.com', 'competitor2.com'],
    'update_intervals': {'fast': 15, 'medium': 60, 'slow': 240}
}
 
platform = MarketIntelligencePlatform(config)

# competitive_intelligence_collector.py
 
class CompetitiveIntelligenceCollector:
    """
    Comprehensive competitive intelligence collection
    Monitors everything competitors do online
    """
    
    def __init__(self, client, config):
        self.client = client
        self.config = config
        self.collection_history = {}
    
    def collect_all_competitive_intelligence(self, competitor):
        """
        Collect complete competitive intelligence profile
        """
        
        intelligence = {
            'competitor': competitor,
            'timestamp': datetime.now().isoformat(),
            'data': {}
        }
        
        # 1. Website Intelligence
        intelligence['data']['website'] = self.collect_website_intelligence(competitor)
        
        # 2. Product Intelligence  
        intelligence['data']['products'] = self.collect_product_intelligence(competitor)
        
        # 3. Pricing Intelligence
        intelligence['data']['pricing'] = self.collect_pricing_intelligence(competitor)
        
        # 4. Hiring Intelligence
        intelligence['data']['hiring'] = self.collect_hiring_intelligence(competitor)
        
        # 5. Marketing Intelligence
        intelligence['data']['marketing'] = self.collect_marketing_intelligence(competitor)
        
        # 6. Customer Intelligence
        intelligence['data']['customers'] = self.collect_customer_intelligence(competitor)
        
        # 7. Technology Intelligence
        intelligence['data']['technology'] = self.collect_technology_intelligence(competitor)
        
        # 8. Financial Intelligence
        intelligence['data']['financial'] = self.collect_financial_intelligence(competitor)
        
        return intelligence
    
    def collect_website_intelligence(self, competitor):
        """
        Monitor competitor website for any changes
        """
        
        prompt = """
        Extract comprehensive website intelligence:
        
        Homepage Analysis:
        - Main value proposition and messaging
        - Call-to-action buttons and placement
        - Featured products or services
        - Customer testimonials or case studies
        - Trust indicators (certifications, awards, clients)
        
        Content Updates:
        - New blog posts or articles
        - Product announcements
        - Company news
        - Event participation
        
        Design Changes:
        - Layout or navigation changes
        - New sections or pages
        - Removed content
        - A/B test variations visible
        
        Technical Indicators:
        - Technology stack visible
        - Performance indicators
        - Mobile optimization
        - Accessibility features
        """
        
        website_data = self.client.smartscraper(
            website_url=f"https://{competitor}",
            user_prompt=prompt
        )
        
        # Detect changes from previous collection
        changes = self.detect_website_changes(competitor, website_data)
        
        return {
            'current_state': website_data,
            'changes_detected': changes,
            'analysis': self.analyze_website_changes(changes),
            'strategic_signals': self.extract_strategic_signals(website_data)
        }
    
    def collect_product_intelligence(self, competitor):
        """
        Track competitor product portfolio
        """
        
        prompt = """
        Extract complete product intelligence:
        
        Product Catalog:
        - All product names and categories
        - Product descriptions and features
        - Technical specifications
        - Use cases and target customers
        - Integration capabilities
        
        Product Positioning:
        - "Good-Better-Best" tiers
        - Premium vs budget offerings
        - Feature differentiation
        - Competitive comparisons made
        
        New Products:
        - "New" or "Beta" labels
        - Recently launched products
        - Coming soon announcements
        - Product roadmap hints
        
        Deprecated Products:
        - End-of-life announcements
        - Sunset timelines
        - Migration paths offered
        """
        
        product_data = self.client.smartscraper(
            website_url=f"https://{competitor}/products",
            user_prompt=prompt
        )
        
        return {
            'product_portfolio': product_data,
            'new_products': self.identify_new_products(competitor, product_data),
            'positioning_analysis': self.analyze_product_positioning(product_data),
            'gaps_vs_us': self.identify_competitive_gaps(product_data)
        }
    
    def collect_hiring_intelligence(self, competitor):
        """
        Analyze hiring patterns for strategic signals
        Critical for predicting competitor moves
        """
        
        # Check LinkedIn jobs
        linkedin_prompt = """
        Extract job posting intelligence:
        
        For each open position:
        - Job title and level (IC, Manager, Director, VP)
        - Department (Engineering, Sales, Marketing, etc.)
        - Location (office, remote, hybrid)
        - Required skills and technologies
        - Team size indicators ("join our team of X")
        - Urgency signals ("urgent", "immediate hire")
        
        Strategic Signals:
        - New office locations
        - New product lines (from job descriptions)
        - Technology stack expansion
        - Market expansion (sales roles in new regions)
        - Leadership gaps being filled
        """
        
        # Scrape job boards
        job_data = self.client.smartscraper(
            website_url=f"https://{competitor}/careers",
            user_prompt=linkedin_prompt
        )
        
        # Analyze hiring patterns
        analysis = {
            'total_openings': self.count_openings(job_data),
            'hiring_by_department': self.categorize_by_department(job_data),
            'new_roles_vs_last_month': self.compare_to_history(competitor, job_data),
            'strategic_signals': self.extract_hiring_signals(job_data),
            'predictions': self.predict_from_hiring(job_data)
        }
        
        return {
            'raw_data': job_data,
            'analysis': analysis,
            'alerts': self.generate_hiring_alerts(analysis)
        }
    
    def collect_marketing_intelligence(self, competitor):
        """
        Track competitor marketing activities
        """
        
        prompt = """
        Extract marketing intelligence:
        
        Messaging & Positioning:
        - Main taglines and slogans
        - Value proposition statements
        - Target customer descriptions
        - Pain points addressed
        - Competitive differentiation claims
        
        Campaigns:
        - Active promotional campaigns
        - Seasonal offers or sales
        - Partnership announcements
        - Event sponsorships
        - Content marketing themes
        
        Customer Acquisition:
        - Free trial offers
        - Freemium tiers
        - Referral programs
        - Discount codes visible
        - Lead magnets (ebooks, webinars)
        
        Social Proof:
        - Customer logos displayed
        - Case studies featured
        - Testimonials and quotes
        - Awards and recognition
        - Media mentions
        """
        
        marketing_data = self.client.smartscraper(
            website_url=f"https://{competitor}",
            user_prompt=prompt
        )
        
        return {
            'messaging': marketing_data,
            'positioning_shift': self.detect_positioning_changes(competitor, marketing_data),
            'campaign_analysis': self.analyze_campaigns(marketing_data),
            'recommendations': self.generate_counter_marketing(marketing_data)
        }
    
    def extract_hiring_signals(self, job_data):
        """
        Extract strategic signals from hiring patterns
        """
        signals = []
        
        # Signal 1: Product expansion
        if self.detect_product_engineering_surge(job_data):
            signals.append({
                'signal': 'product_expansion',
                'confidence': 0.85,
                'description': 'Rapid engineering hiring suggests major product development',
                'timeline': '3-6 months to launch',
                'recommendation': 'Accelerate our product roadmap'
            })
        
        # Signal 2: Market expansion  
        if self.detect_geographic_expansion(job_data):
            signals.append({
                'signal': 'geographic_expansion',
                'confidence': 0.78,
                'description': 'Sales hiring in new regions indicates market expansion',
                'timeline': '2-4 months to launch',
                'recommendation': 'Consider entering those markets first'
            })
        
        # Signal 3: Technology shift
        if self.detect_tech_stack_change(job_data):
            signals.append({
                'signal': 'technology_shift',
                'confidence': 0.72,
                'description': 'Hiring for new technologies suggests platform evolution',
                'timeline': '6-12 months to migration',
                'recommendation': 'Evaluate same technologies for competitive advantage'
            })
        
        return signals
    
    def predict_from_hiring(self, job_data):
        """
        Predict competitor moves from hiring patterns
        """
        predictions = []
        
        # Analyze job posting trends
        engineering_roles = self.count_roles_by_type(job_data, 'engineering')
        sales_roles = self.count_roles_by_type(job_data, 'sales')
        
        # High engineering, low sales = new product coming
        if engineering_roles > sales_roles * 2:
            predictions.append({
                'prediction': 'Major product launch within 4-6 months',
                'confidence': 0.82,
                'evidence': f'{engineering_roles} engineering roles vs {sales_roles} sales',
                'prepare_for': 'Competitive product announcement'
            })
        
        # High sales, low engineering = growth push
        if sales_roles > engineering_roles * 1.5:
            predictions.append({
                'prediction': 'Aggressive sales expansion within 2-3 months',
                'confidence': 0.88,
                'evidence': f'{sales_roles} sales roles vs {engineering_roles} engineering',
                'prepare_for': 'Increased competitive pressure in market'
            })
        
        return predictions
 
# Example: Deploy competitive intelligence collector
collector = CompetitiveIntelligenceCollector(
    client=Client(api_key="your-key"),
    config={'competitors': ['competitor.com']}
)
 
# Collect intelligence
intel = collector.collect_all_competitive_intelligence('competitor.com')
print(json.dumps(intel, indent=2))

# market_intelligence_collector.py
 
class MarketIntelligenceCollector:
    """
    Aggregate market trends, news, and industry intelligence
    """
    
    def __init__(self, client, config):
        self.client = client
        self.config = config
        self.sources = self.configure_market_sources()
    
    def configure_market_sources(self):
        """
        Configure market intelligence sources
        Customize for your industry
        """
        return {
            'news': [
                'techcrunch.com',
                'theverge.com',
                'wired.com',
                # Add your industry-specific news sources
            ],
            'analysis': [
                'gartner.com/en/newsroom',
                'forrester.com/blogs',
                # Add analyst sites
            ],
            'communities': [
                'reddit.com/r/your_industry',
                'news.ycombinator.com',
                # Add relevant communities
            ],
            'industry_publications': [
                # Add trade publications
            ]
        }
    
    def collect_market_intelligence(self):
        """
        Collect comprehensive market intelligence
        """
        
        intelligence = {
            'timestamp': datetime.now().isoformat(),
            'sources_monitored': len(self.flatten_sources()),
            'data': {}
        }
        
        # Collect from all source types
        intelligence['data']['news'] = self.collect_industry_news()
        intelligence['data']['trends'] = self.collect_market_trends()
        intelligence['data']['sentiment'] = self.collect_market_sentiment()
        intelligence['data']['emerging_tech'] = self.collect_technology_trends()
        intelligence['data']['regulatory'] = self.collect_regulatory_updates()
        
        # Generate insights
        intelligence['insights'] = self.generate_market_insights(intelligence['data'])
        
        return intelligence
    
    def collect_industry_news(self):
        """
        Aggregate relevant industry news
        """
        
        news_prompt = """
        Extract industry news and analysis:
        
        Article Information:
        - Headline and summary
        - Publication date and time
        - Author and source
        - Article category/topic
        
        Content Analysis:
        - Main themes and topics
        - Companies mentioned
        - Products or technologies discussed
        - Market trends identified
        - Expert opinions or predictions
        
        Relevance Signals:
        - Impact on our industry (high/medium/low)
        - Competitive implications
        - Opportunity or threat indicators
        - Action items for our business
        """
        
        all_news = []
        
        for news_source in self.sources['news']:
            try:
                news_data = self.client.smartscraper(
                    website_url=f"https://{news_source}",
                    user_prompt=news_prompt
                )
                
                all_news.append({
                    'source': news_source,
                    'articles': news_data,
                    'collected_at': datetime.now().isoformat()
                })
            except Exception as e:
                print(f"Error collecting from {news_source}: {e}")
        
        # Deduplicate and rank by relevance
        deduplicated_news = self.deduplicate_news(all_news)
        ranked_news = self.rank_by_relevance(deduplicated_news)
        
        return {
            'total_articles': len(ranked_news),
            'top_stories': ranked_news[:10],
            'by_topic': self.categorize_news(ranked_news),
            'trending_themes': self.extract_trending_themes(ranked_news)
        }
    
    def collect_market_trends(self):
        """
        Identify and track market trends
        """
        
        trends_prompt = """
        Extract market trend indicators:
        
        Trend Identification:
        - Emerging technologies or methodologies
        - Shifting customer preferences
        - New business models
        - Industry consolidation patterns
        - Regulatory changes
        
        Trend Metrics:
        - Adoption rate indicators
        - Market size projections
        - Growth rate estimates
        - Investment activity
        - Competitive landscape changes
        
        Trend Analysis:
        - Drivers of the trend
        - Barriers to adoption
        - Timeline for mainstream adoption
        - Winners and losers
        - Implications for our business
        """
        
        # Collect trend data from analyst sources
        trend_data = []
        
        for source in self.sources['analysis']:
            data = self.client.smartscraper(
                website_url=f"https://{source}",
                user_prompt=trends_prompt
            )
            trend_data.append(data)
        
        # Analyze cross-source trends
        consolidated_trends = self.consolidate_trends(trend_data)
        
        return {
            'identified_trends': consolidated_trends,
            'trend_strength': self.calculate_trend_strength(consolidated_trends),
            'relevance_to_us': self.assess_trend_relevance(consolidated_trends),
            'action_recommendations': self.generate_trend_recommendations(consolidated_trends)
        }
    
    def generate_market_insights(self, market_data):
        """
        Generate actionable insights from market data
        """
        insights = []
        
        # Insight 1: Emerging opportunities
        opportunities = self.identify_market_opportunities(market_data)
        if opportunities:
            insights.append({
                'type': 'opportunity',
                'priority': 'high',
                'insights': opportunities,
                'action_required': 'Evaluate market entry or product expansion'
            })
        
        # Insight 2: Competitive threats
        threats = self.identify_market_threats(market_data)
        if threats:
            insights.append({
                'type': 'threat',
                'priority': 'high',
                'insights': threats,
                'action_required': 'Develop mitigation strategy'
            })
        
        # Insight 3: Industry shifts
        shifts = self.identify_industry_shifts(market_data)
        if shifts:
            insights.append({
                'type': 'market_shift',
                'priority': 'medium',
                'insights': shifts,
                'action_required': 'Adapt strategy to changing market'
            })
        
        return insights
 
# Deploy market intelligence
market_collector = MarketIntelligenceCollector(
    client=Client(api_key="your-key"),
    config={}
)
 
market_intel = market_collector.collect_market_intelligence()

# intelligence_processor.py
 
class IntelligenceProcessor:
    """
    Process raw data into actionable intelligence
    The brain of your market intelligence platform
    """
    
    def __init__(self):
        self.nlp_engine = self.initialize_nlp()
        self.pattern_recognizer = self.initialize_pattern_recognition()
        self.insight_generator = self.initialize_insight_generation()
    
    def process_intelligence(self, raw_data):
        """
        Transform raw data into structured intelligence
        """
        
        processed = {
            'timestamp': datetime.now().isoformat(),
            'raw_data_summary': self.summarize_raw_data(raw_data),
            'processed_intelligence': {}
        }
        
        # Step 1: Clean and structure data
        cleaned_data = self.clean_and_structure(raw_data)
        
        # Step 2: Extract entities and relationships
        entities = self.extract_entities(cleaned_data)
        relationships = self.map_relationships(entities)
        
        # Step 3: Identify patterns and anomalies
        patterns = self.identify_patterns(cleaned_data)
        anomalies = self.detect_anomalies(cleaned_data)
        
        # Step 4: Generate insights
        insights = self.generate_insights(
            cleaned_data,
            entities,
            relationships,
            patterns,
            anomalies
        )
        
        # Step 5: Create actionable recommendations
        recommendations = self.create_recommendations(insights)
        
        processed['processed_intelligence'] = {
            'entities': entities,
            'relationships': relationships,
            'patterns': patterns,
            'anomalies': anomalies,
            'insights': insights,
            'recommendations': recommendations
        }
        
        return processed
    
    def extract_entities(self, data):
        """
        Extract key entities from intelligence data
        """
        entities = {
            'companies': [],
            'products': [],
            'technologies': [],
            'people': [],
            'locations': [],
            'events': []
        }
        
        # Use NLP to extract entities
        # Implementation would use spaCy, NLTK, or similar
        
        return entities
    
    def identify_patterns(self, data):
        """
        Identify patterns in intelligence data
        """
        patterns = []
        
        # Pattern 1: Recurring themes across sources
        themes = self.extract_recurring_themes(data)
        if themes:
            patterns.append({
                'type': 'recurring_themes',
                'themes': themes,
                'significance': 'High - indicates market consensus'
            })
        
        # Pattern 2: Temporal patterns
        temporal = self.analyze_temporal_patterns(data)
        if temporal:
            patterns.append({
                'type': 'temporal_patterns',
                'patterns': temporal,
                'significance': 'Medium - indicates cyclical behavior'
            })
        
        # Pattern 3: Correlation patterns
        correlations = self.find_correlations(data)
        if correlations:
            patterns.append({
                'type': 'correlations',
                'correlations': correlations,
                'significance': 'High - indicates causal relationships'
            })
        
        return patterns
    
    def generate_insights(self, data, entities, relationships, patterns, anomalies):
        """
        Generate strategic insights from processed data
        """
        insights = []
        
        # Competitive insights
        competitive_insights = self.generate_competitive_insights(
            data, entities, patterns
        )
        insights.extend(competitive_insights)
        
        # Market insights
        market_insights = self.generate_market_insights(
            data, patterns, anomalies
        )
        insights.extend(market_insights)
        
        # Strategic insights
        strategic_insights = self.generate_strategic_insights(
            data, relationships, patterns
        )
        insights.extend(strategic_insights)
        
        # Prioritize insights
        prioritized = self.prioritize_insights(insights)
        
        return prioritized
    
    def create_recommendations(self, insights):
        """
        Create actionable recommendations from insights
        """
        recommendations = []
        
        for insight in insights:
            if insight['priority'] == 'high':
                recommendation = {
                    'insight': insight['description'],
                    'recommended_action': self.generate_action(insight),
                    'expected_impact': self.estimate_impact(insight),
                    'timeline': self.recommend_timeline(insight),
                    'resources_required': self.estimate_resources(insight),
                    'success_metrics': self.define_metrics(insight)
                }
                recommendations.append(recommendation)
        
        return recommendations
 
# Example: Process collected intelligence
processor = IntelligenceProcessor()
processed_intel = processor.process_intelligence(collected_data)

# dashboard_system.py
 
import streamlit as st
import plotly.graph_objects as go
import plotly.express as px
from datetime import datetime, timedelta
 
class MarketIntelligenceDashboard:
    """
    Interactive dashboard for market intelligence
    Built with Streamlit for rapid deployment
    """
    
    def __init__(self, platform):
        self.platform = platform
        
    def create_executive_dashboard(self):
        """
        Executive-level strategic dashboard
        """
        st.set_page_config(
            page_title="Market Intelligence Platform",
            page_icon="🎯",
            layout="wide"
        )
        
        st.title("🎯 Market Intelligence Command Center")
        st.caption(f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        
        # Metrics row
        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            st.metric(
                "Active Competitors Monitored",
                value="47",
                delta="+3 this month"
            )
        
        with col2:
            st.metric(
                "Intelligence Items Collected",
                value="12,847",
                delta="+2,341 this week"
            )
        
        with col3:
            st.metric(
                "High-Priority Alerts",
                value="8",
                delta="+2 today",
                delta_color="inverse"
            )
        
        with col4:
            st.metric(
                "Market Opportunities",
                value="23",
                delta="+5 this week"
            )
        
        # Alert section
        st.header("🚨 Priority Alerts")
        alerts = self.get_priority_alerts()
        for alert in alerts:
            with st.expander(f"⚠️ {alert['title']}", expanded=True):
                st.write(alert['description'])
                st.write(f"**Impact:** {alert['impact']}")
                st.write(f"**Recommended Action:** {alert['action']}")
                st.button(f"Take Action", key=alert['id'])
        
        # Competitive Intelligence section
        st.header("🏢 Competitive Intelligence")
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("Recent Competitor Moves")
            competitor_moves = self.get_competitor_moves()
            for move in competitor_moves:
                st.info(f"**{move['competitor']}**: {move['action']} - {move['timestamp']}")
        
        with col2:
            st.subheader("Competitive Positioning Map")
            positioning_fig = self.create_positioning_chart()
            st.plotly_chart(positioning_fig, use_container_width=True)
        
        # Market Trends section
        st.header("📈 Market Trends")
        
        trends_data = self.get_trending_topics()
        fig = px.bar(
            trends_data,
            x='topic',
            y='mentions',
            title='Trending Topics This Week',
            color='sentiment'
        )
        st.plotly_chart(fig, use_container_width=True)
        
        # Opportunity Pipeline
        st.header("💡 Opportunity Pipeline")
        opportunities = self.get_opportunities()
        
        for opp in opportunities:
            with st.container():
                col1, col2, col3 = st.columns([3, 1, 1])
                with col1:
                    st.write(f"**{opp['title']}**")
                    st.write(opp['description'])
                with col2:
                    st.write(f"Impact: {opp['impact']}")
                with col3:
                    st.button("Investigate", key=opp['id'])
                st.divider()
    
    def create_positioning_chart(self):
        """
        Create competitive positioning visualization
        """
        # Sample data - in production, pull from intelligence platform
        companies = ['Us', 'Competitor A', 'Competitor B', 'Competitor C', 'Competitor D']
        price_position = [75, 85, 60, 90, 55]
        feature_richness = [80, 70, 65, 85, 60]
        market_share = [15, 25, 10, 30, 8]
        
        fig = go.Figure()
        
        for i, company in enumerate(companies):
            fig.add_trace(go.Scatter(
                x=[price_position[i]],
                y=[feature_richness[i]],
                mode='markers+text',
                name=company,
                marker=dict(size=market_share[i]*2),
                text=[company],
                textposition="top center"
            ))
        
        fig.update_layout(
            title='Competitive Positioning Map',
            xaxis_title='Price Point',
            yaxis_title='Feature Richness',
            showlegend=False
        )
        
        return fig
 
# Deploy dashboard
if __name__ == "__main__":
    platform = MarketIntelligencePlatform(config)
    dashboard = MarketIntelligenceDashboard(platform)
    dashboard.create_executive_dashboard()

# alert_system.py
 
class IntelligentAlertSystem:
    """
    Smart alerting that only notifies on truly important events
    Prevents alert fatigue while ensuring nothing critical is missed
    """
    
    def __init__(self, config):
        self.config = config
        self.alert_history = []
        self.alert_rules = self.load_alert_rules()
    
    def load_alert_rules(self):
        """
        Define alert rules and conditions
        """
        return {
            'critical': {
                'competitor_product_launch': {
                    'priority': 'critical',
                    'channels': ['slack', 'email', 'sms'],
                    'condition': self.detect_product_launch,
                    'action_required': True
                },
                'major_market_shift': {
                    'priority': 'critical',
                    'channels': ['slack', 'email'],
                    'condition': self.detect_market_shift,
                    'action_required': True
                },
                'competitive_price_war': {
                    'priority': 'critical',
                    'channels': ['slack', 'email', 'sms'],
                    'condition': self.detect_price_war,
                    'action_required': True
                }
            },
            'high': {
                'competitor_hiring_surge': {
                    'priority': 'high',
                    'channels': ['slack', 'email'],
                    'condition': self.detect_hiring_surge,
                    'action_required': False
                },
                'market_opportunity': {
                    'priority': 'high',
                    'channels': ['slack'],
                    'condition': self.detect_opportunity,
                    'action_required': False
                }
            },
            'medium': {
                'competitor_content_update': {
                    'priority': 'medium',
                    'channels': ['email'],
                    'condition': self.detect_content_update,
                    'action_required': False
                }
            }
        }
    
    def process_intelligence_for_alerts(self, intelligence_data):
        """
        Analyze intelligence and generate appropriate alerts
        """
        alerts_generated = []
        
        # Check all alert rules
        for priority_level in self.alert_rules:
            for alert_type, rule in self.alert_rules[priority_level].items():
                # Evaluate condition
                if rule['condition'](intelligence_data):
                    alert = self.create_alert(
                        alert_type,
                        priority_level,
                        intelligence_data,
                        rule
                    )
                    alerts_generated.append(alert)
                    
                    # Send through appropriate channels
                    self.send_alert(alert, rule['channels'])
        
        return alerts_generated
    
    def create_alert(self, alert_type, priority, data, rule):
        """
        Create structured alert
        """
        return {
            'id': self.generate_alert_id(),
            'type': alert_type,
            'priority': priority,
            'timestamp': datetime.now().isoformat(),
            'title': self.generate_alert_title(alert_type, data),
            'description': self.generate_alert_description(alert_type, data),
            'impact': self.assess_impact(alert_type, data),
            'recommended_action': self.recommend_action(alert_type, data),
            'data': data,
            'requires_action': rule['action_required']
        }
    
    def send_alert(self, alert, channels):
        """
        Send alert through specified channels
        """
        for channel in channels:
            if channel == 'slack':
                self.send_slack_alert(alert)
            elif channel == 'email':
                self.send_email_alert(alert)
            elif channel == 'sms':
                self.send_sms_alert(alert)
    
    def send_slack_alert(self, alert):
        """
        Send formatted alert to Slack
        """
        # Implementation using Slack webhook
        webhook_url = self.config.get('slack_webhook_url')
        
        message = {
            "text": f"🚨 {alert['priority'].upper()}: {alert['title']}",
            "blocks": [
                {
                    "type": "header",
                    "text": {
                        "type": "plain_text",
                        "text": f"🚨 {alert['title']}"
                    }
                },
                {
                    "type": "section",
                    "text": {
                        "type": "mrkdwn",
                        "text": alert['description']
                    }
                },
                {
                    "type": "section",
                    "fields": [
                        {
                            "type": "mrkdwn",
                            "text": f"*Priority:*\n{alert['priority'].upper()}"
                        },
                        {
                            "type": "mrkdwn",
                            "text": f"*Impact:*\n{alert['impact']}"
                        }
                    ]
                },
                {
                    "type": "section",
                    "text": {
                        "type": "mrkdwn",
                        "text": f"*Recommended Action:*\n{alert['recommended_action']}"
                    }
                },
                {
                    "type": "actions",
                    "elements": [
                        {
                            "type": "button",
                            "text": {
                                "type": "plain_text",
                                "text": "View Full Details"
                            },
                            "url": f"{self.config['dashboard_url']}/alert/{alert['id']}"
                        }
                    ]
                }
            ]
        }
        
        # Send to Slack
        # requests.post(webhook_url, json=message)
    
    def detect_product_launch(self, data):
        """
        Detect if competitor is launching new product
        """
        # Look for signals:
        # - New product pages
        # - "Coming soon" announcements
        # - Hiring spikes in product/engineering
        # - Press release preparation
        
        signals = 0
        
        if 'new_product_page' in str(data):
            signals += 1
        
        if 'hiring_surge' in str(data) and 'product' in str(data):
            signals += 1
        
        if 'press_release' in str(data) and 'launch' in str(data):
            signals += 1
        
        # Threshold: 2+ signals = product launch likely
        return signals >= 2
 
# Deploy alert system
alert_system = IntelligentAlertSystem(config={
    'slack_webhook_url': 'your-webhook-url',
    'dashboard_url': 'https://your-dashboard.com'
})
 
# Process intelligence for alerts
alerts = alert_system.process_intelligence_for_alerts(intelligence_data)

# platform_orchestrator.py
 
class PlatformOrchestrator:
    """
    Orchestrates all platform components
    Coordinates collection, processing, analysis, and delivery
    """
    
    def __init__(self, config):
        self.config = config
        
        # Initialize all components
        self.platform = MarketIntelligencePlatform(config)
        self.alert_system = IntelligentAlertSystem(config)
        self.dashboard = MarketIntelligenceDashboard(self.platform)
        
    def run_intelligence_cycle(self):
        """
        Execute complete intelligence gathering and analysis cycle
        """
        
        print("🚀 Starting Intelligence Cycle")
        print(f"⏰ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        print("-" * 60)
        
        try:
            # Step 1: Collect intelligence from all sources
            print("📊 Collecting intelligence...")
            collected_data = self.collect_all_intelligence()
            print(f"✓ Collected from {collected_data['sources_count']} sources")
            
            # Step 2: Process and analyze
            print("🧠 Processing and analyzing...")
            processed_intel = self.platform.processors['nlp'].process_intelligence(
                collected_data
            )
            print(f"✓ Generated {len(processed_intel['insights'])} insights")
            
            # Step 3: Check for alerts
            print("🚨 Checking for priority alerts...")
            alerts = self.alert_system.process_intelligence_for_alerts(
                processed_intel
            )
            print(f"✓ Generated {len(alerts)} alerts")
            
            # Step 4: Update dashboards
            print("📈 Updating dashboards...")
            self.update_all_dashboards(processed_intel)
            print("✓ Dashboards updated")
            
            # Step 5: Store intelligence
            print("💾 Storing intelligence...")
            self.store_intelligence(processed_intel)
            print("✓ Intelligence stored")
            
            print("-" * 60)
            print(f"✅ Intelligence cycle completed successfully")
            
            return {
                'status': 'success',
                'data_collected': collected_data['sources_count'],
                'insights_generated': len(processed_intel['insights']),
                'alerts_sent': len(alerts)
            }
            
        except Exception as e:
            print(f"❌ Error in intelligence cycle: {e}")
            return {'status': 'failed', 'error': str(e)}
    
    def collect_all_intelligence(self):
        """
        Collect from all configured sources
        """
        all_data = {
            'timestamp': datetime.now().isoformat(),
            'sources_count': 0,
            'competitive': [],
            'market': [],
            'customer': [],
            'financial': []
        }
        
        # Collect competitive intelligence
        for collector_type, collector in self.platform.collectors.items():
            data = collector.collect()
            all_data[collector_type] = data
            all_data['sources_count'] += self.count_sources(data)
        
        return all_data
    
    def run_continuous_monitoring(self, interval_minutes=15):
        """
        Run platform continuously with specified interval
        """
        print("🤖 Starting Continuous Market Intelligence Monitoring")
        print(f"⚡ Update interval: {interval_minutes} minutes")
        print(f"🎯 Competitors monitored: {len(self.config['competitors'])}")
        print(f"📡 Data sources: {self.count_all_sources()}")
        print("=" * 60)
        
        cycle = 0
        
        while True:
            cycle += 1
            print(f"\n📍 Cycle #{cycle}")
            
            # Run intelligence cycle
            result = self.run_intelligence_cycle()
            
            if result['status'] == 'success':
                print(f"📊 Summary: {result['data_collected']} sources, "
                      f"{result['insights_generated']} insights, "
                      f"{result['alerts_sent']} alerts")
            
            # Wait for next cycle
            print(f"⏳ Next cycle in {interval_minutes} minutes...")
            time.sleep(interval_minutes * 60)
 
# Launch the platform
if __name__ == "__main__":
    config = {
        'scrapegraph_api_key': 'your-api-key',
        'competitors': [
            'competitor1.com',
            'competitor2.com',
            'competitor3.com'
        ],
        'slack_webhook_url': 'your-slack-webhook',
        'dashboard_url': 'https://your-dashboard.com'
    }
    
    orchestrator = PlatformOrchestrator(config)
    
    # Run continuous monitoring
    orchestrator.run_continuous_monitoring(interval_minutes=15)

class PlatformROI:
    """Calculate complete ROI of custom platform"""
    
    def calculate_5_year_roi(self):
        """
        Compare custom platform vs commercial alternatives
        """
        
        # Custom Platform Costs (5 years)
        custom_costs = {
            'year_1': {
                'scrapegraph_ai': 36000,
                'infrastructure': 12000,
                'development': 24000,  # One-time
                'maintenance': 12000
            },
            'year_2_5': {
                'scrapegraph_ai': 36000,
                'infrastructure': 12000,
                'maintenance': 12000
            }
        }
        
        custom_total = (
            custom_costs['year_1']['scrapegraph_ai'] +
            custom_costs['year_1']['infrastructure'] +
            custom_costs['year_1']['development'] +
            custom_costs['year_1']['maintenance'] +
            (custom_costs['year_2_5']['scrapegraph_ai'] +
             custom_costs['year_2_5']['infrastructure'] +
             custom_costs['year_2_5']['maintenance']) * 4
        )
        
        # Commercial Platform Costs (5 years)
        commercial_annual = 300000  # Average of 3-5 platforms
        commercial_total = commercial_annual * 5
        
        # Calculate savings
        savings = commercial_total - custom_total
        roi_percentage = (savings / custom_total) * 100
        
        return {
            'custom_platform_5yr': custom_total,
            'commercial_platform_5yr': commercial_total,
            'total_savings': savings,
            'roi_percentage': roi_percentage,
            'payback_months': (custom_costs['year_1']['development'] / 
                              (commercial_annual - 60000)) * 12
        }
 
roi_calc = PlatformROI()
results = roi_calc.calculate_5_year_roi()
 
print(f"5-Year ROI: {results['roi_percentage']:.0f}%")
print(f"Total Savings: ${results['total_savings']:,.0f}")
print(f"Payback Period: {results['payback_months']:.1f} months")

class PredictiveIntelligenceEngine:
    """
    Predict market changes before they happen
    The ultimate competitive advantage
    """
    
    def __init__(self, historical_data):
        self.historical_data = historical_data
        self.prediction_models = {}
    
    def predict_competitor_moves(self, competitor, timeframe_days=30):
        """
        Predict what competitors will do in next 30 days
        """
        
        predictions = []
        
        # Analyze historical patterns
        patterns = self.analyze_historical_patterns(competitor)
        
        # Check current signals
        signals = self.collect_leading_indicators(competitor)
        
        # Generate predictions
        if self.detect_product_launch_signals(signals, patterns):
            predictions.append({
                'prediction': 'Product launch',
                'probability': 0.82,
                'timeframe': '2-4 weeks',
                'impact': 'High',
                'recommended_response': 'Accelerate our roadmap'
            })
        
        if self.detect_pricing_change_signals(signals, patterns):
            predictions.append({
                'prediction': 'Price reduction',
                'probability': 0.76,
                'timeframe': '1-2 weeks',
                'impact': 'Medium',
                'recommended_response': 'Prepare value messaging'
            })
        
        return {
            'competitor': competitor,
            'predictions': predictions,
            'confidence': self.calculate_prediction_confidence(predictions),
            'generated_at': datetime.now().isoformat()
        }

class CompetitiveBattlecardGenerator:
    """
    Automatically generate and update competitive battlecards
    Always current, never outdated
    """
    
    def generate_battlecard(self, competitor):
        """
        Create comprehensive competitive battlecard
        """
        
        battlecard = {
            'competitor': competitor,
            'generated_at': datetime.now().isoformat(),
            'sections': {}
        }
        
        # Company overview
        battlecard['sections']['overview'] = self.generate_overview(competitor)
        
        # Product comparison
        battlecard['sections']['products'] = self.generate_product_comparison(competitor)
        
        # Strengths and weaknesses
        battlecard['sections']['swot'] = self.generate_swot_analysis(competitor)
        
        # How to position against them
        battlecard['sections']['positioning'] = self.generate_positioning_guide(competitor)
        
        # Objection handling
        battlecard['sections']['objections'] = self.generate_objection_handlers(competitor)
        
        # Recent news and updates
        battlecard['sections']['recent_updates'] = self.get_recent_updates(competitor)
        
        return battlecard

from scrapegraph_py import Client
 
# 1. Initialize
client = Client(api_key="your-api-key")
 
# 2. Collect first intelligence
intel = client.smartscraper(
    website_url="https://competitor.com",
    user_prompt="Extract all competitive intelligence"
)
 
# 3. You're now running your own intelligence platform
print(f"Intelligence collected: {intel}")
 
# Next: Build this into your complete platform using
# the architecture and code provided in this guide