AI Processing Pipeline for Market Intelligence

Executive Summary

A comprehensive AI processing system that transforms raw news and economic data into actionable S&OP intelligence through multi-stage analysis, risk assessment, and business impact evaluation.

1. Multi-Stage AI Processing Pipeline

Stage 1: Data Ingestion and Normalization

// Data ingestion service
class DataIngestionService {
  constructor() {
    this.processors = {
      'newsapi': new NewsAPIProcessor(),
      'worldbank': new WorldBankProcessor(),
      'freightwaves': new FreightWavesProcessor(),
      'supplychaindive': new SupplyChainDiveProcessor(),
      'trade_data': new TradeDataProcessor()
    };
  }

  async ingestData(sources, timeframe = '24h') {
    const rawData = await Promise.all(
      sources.map(async (source) => {
        const processor = this.processors[source.type];
        return await processor.fetch(source.config, timeframe);
      })
    );

    // Normalize data structure
    return this.normalizeDataStructure(rawData.flat());
  }

  normalizeDataStructure(rawItems) {
    return rawItems.map(item => ({
      id: this.generateUniqueId(item),
      source: item.source,
      type: item.type || 'news', // news, economic_data, trade_data
      title: item.title || item.name,
      content: item.description || item.content || item.summary,
      published_at: new Date(item.publishedAt || item.date),
      url: item.url,
      raw_data: item,
      
      // Standardized fields for processing
      geographic_mentions: [],
      industry_mentions: [],
      keywords: [],
      numerical_data: this.extractNumericalData(item),
      
      // Processing metadata
      processed_at: new Date(),
      processing_version: '1.0'
    }));
  }
}

Stage 2: Content Analysis and Feature Extraction

class ContentAnalysisEngine {
  constructor() {
    this.nlpProcessor = new NLPProcessor();
    this.industryClassifier = new IndustryClassifier();
    this.geographicExtractor = new GeographicExtractor();
    this.riskPatternMatcher = new RiskPatternMatcher();
  }

  async analyzeContent(normalizedItems) {
    return await Promise.all(
      normalizedItems.map(async (item) => {
        const analysis = await this.performDeepAnalysis(item);
        return {
          ...item,
          analysis: analysis
        };
      })
    );
  }

  async performDeepAnalysis(item) {
    const content = `${item.title} ${item.content}`;
    
    // Parallel analysis tasks
    const [
      sentimentAnalysis,
      entityExtraction,
      industryClassification,
      geographicAnalysis,
      riskIndicators,
      temporalAnalysis,
      numericalExtraction
    ] = await Promise.all([
      this.analyzeSentiment(content),
      this.extractEntities(content),
      this.classifyIndustries(content),
      this.extractGeographicInfo(content),
      this.identifyRiskPatterns(content),
      this.analyzeTimelineUrgency(content, item.published_at),
      this.extractAndAnalyzeNumbers(content)
    ]);

    return {
      sentiment: sentimentAnalysis,
      entities: entityExtraction,
      industries: industryClassification,
      geography: geographicAnalysis,
      risk_patterns: riskIndicators,
      timeline: temporalAnalysis,
      numerical_insights: numericalExtraction,
      content_quality_score: this.assessContentQuality(item, content)
    };
  }

  async analyzeSentiment(content) {
    // Use a combination of lexicon-based and ML approaches
    const lexiconScore = this.calculateLexiconSentiment(content);
    const contextualScore = await this.getMLSentiment(content);
    
    // Weighted combination
    const finalScore = (lexiconScore * 0.4) + (contextualScore * 0.6);
    
    return {
      score: finalScore, // -1 to 1
      classification: this.classifySentiment(finalScore),
      confidence: this.calculateSentimentConfidence(lexiconScore, contextualScore),
      market_sentiment: this.interpretMarketSentiment(finalScore, content)
    };
  }

  async extractEntities(content) {
    const entities = {
      companies: await this.extractCompanies(content),
      products: await this.extractProducts(content),
      locations: await this.extractLocations(content),
      people: await this.extractPeople(content),
      organizations: await this.extractOrganizations(content),
      supply_chain_terms: await this.extractSupplyChainTerms(content)
    };

    return {
      ...entities,
      entity_density: this.calculateEntityDensity(entities, content),
      key_relationships: this.identifyEntityRelationships(entities)
    };
  }

  async classifyIndustries(content) {
    const industryKeywords = {
      'automotive': {
        keywords: ['automotive', 'car', 'vehicle', 'tesla', 'ford', 'gm', 'toyota', 'ev', 'electric vehicle'],
        weight_multipliers: { 'supply chain': 1.5, 'production': 1.3, 'semiconductor': 2.0 }
      },
      'agroscience': {
        keywords: ['agriculture', 'crop', 'fertilizer', 'seed', 'farming', 'pesticide', 'harvest'],
        weight_multipliers: { 'weather': 2.0, 'yield': 1.8, 'commodity': 1.5 }
      },
      'pharmaceutical': {
        keywords: ['pharmaceutical', 'drug', 'medicine', 'fda', 'clinical trial', 'api', 'vaccine'],
        weight_multipliers: { 'regulatory': 2.0, 'approval': 1.8, 'shortage': 1.7 }
      },
      'technology': {
        keywords: ['technology', 'software', 'semiconductor', 'chip', 'ai', 'cloud', 'data'],
        weight_multipliers: { 'supply chain': 1.5, 'shortage': 2.0 }
      },
      'energy': {
        keywords: ['energy', 'oil', 'gas', 'renewable', 'solar', 'wind', 'coal'],
        weight_multipliers: { 'price': 1.8, 'supply': 1.6 }
      }
    };

    const classifications = {};
    
    for (const [industry, config] of Object.entries(industryKeywords)) {
      let score = 0;
      const contentLower = content.toLowerCase();
      
      // Base keyword matching
      config.keywords.forEach(keyword => {
        const matches = (contentLower.match(new RegExp(keyword, 'g')) || []).length;
        score += matches * 0.1;
      });
      
      // Apply weight multipliers for contextual terms
      Object.entries(config.weight_multipliers).forEach(([term, multiplier]) => {
        if (contentLower.includes(term)) {
          score *= multiplier;
        }
      });
      
      classifications[industry] = {
        score: Math.min(1.0, score),
        confidence: this.calculateIndustryConfidence(score, content),
        supporting_evidence: this.extractSupportingEvidence(content, config.keywords)
      };
    }
    
    return {
      classifications,
      primary_industry: this.getPrimaryIndustry(classifications),
      industry_diversity_score: this.calculateIndustryDiversity(classifications)
    };
  }

  async identifyRiskPatterns(content) {
    const riskPatterns = {
      'supply_disruption': {
        patterns: [
          /supply.*disrupt/gi,
          /shortage/gi,
          /delay.*deliver/gi,
          /production.*halt/gi,
          /factory.*shutdown/gi
        ],
        severity_multipliers: {
          'critical': 1.0,
          'major': 0.8,
          'significant': 0.6,
          'minor': 0.3
        }
      },
      'labor_issues': {
        patterns: [
          /strike/gi,
          /labor.*dispute/gi,
          /worker.*shortage/gi,
          /union.*negotiation/gi,
          /wage.*increase/gi
        ],
        severity_multipliers: {
          'indefinite': 1.0,
          'extended': 0.8,
          'planned': 0.5
        }
      },
      'regulatory_changes': {
        patterns: [
          /regulation.*change/gi,
          /policy.*update/gi,
          /compliance.*requirement/gi,
          /tariff/gi,
          /trade.*war/gi
        ],
        severity_multipliers: {
          'immediate': 1.0,
          'pending': 0.6,
          'proposed': 0.3
        }
      },
      'natural_disasters': {
        patterns: [
          /hurricane/gi,
          /earthquake/gi,
          /flood/gi,
          /typhoon/gi,
          /wildfire/gi
        ],
        severity_multipliers: {
          'category 5': 1.0,
          'category 4': 0.8,
          'severe': 0.7
        }
      },
      'economic_indicators': {
        patterns: [
          /inflation.*rise/gi,
          /interest.*rate.*increase/gi,
          /recession.*risk/gi,
          /gdp.*decline/gi,
          /unemployment.*rise/gi
        ],
        severity_multipliers: {
          'accelerating': 1.0,
          'steady': 0.6,
          'moderate': 0.4
        }
      }
    };

    const detectedRisks = {};
    let maxSeverity = 0;

    for (const [riskType, config] of Object.entries(riskPatterns)) {
      const matches = [];
      let baseScore = 0;

      config.patterns.forEach(pattern => {
        const patternMatches = content.match(pattern) || [];
        matches.push(...patternMatches);
        baseScore += patternMatches.length * 0.2;
      });

      if (matches.length > 0) {
        // Apply severity multipliers
        let severityScore = baseScore;
        Object.entries(config.severity_multipliers).forEach(([term, multiplier]) => {
          if (content.toLowerCase().includes(term)) {
            severityScore *= multiplier;
          }
        });

        const finalScore = Math.min(1.0, severityScore);
        maxSeverity = Math.max(maxSeverity, finalScore);

        detectedRisks[riskType] = {
          score: finalScore,
          matches: matches.length,
          evidence: matches.slice(0, 3), // Top 3 matches
          confidence: this.calculateRiskConfidence(matches, content)
        };
      }
    }

    return {
      detected_risks: detectedRisks,
      overall_severity: maxSeverity,
      risk_categories: Object.keys(detectedRisks),
      risk_density: Object.keys(detectedRisks).length / Object.keys(riskPatterns).length
    };
  }
}

Stage 3: Business Impact Assessment

class BusinessImpactAssessor {
  constructor() {
    this.financialModels = new FinancialImpactModels();
    this.supplyChainMapper = new SupplyChainImpactMapper();
    this.marketAnalyzer = new MarketImpactAnalyzer();
  }

  async assessBusinessImpact(analyzedItems) {
    return await Promise.all(
      analyzedItems.map(async (item) => {
        const impact = await this.calculateComprehensiveImpact(item);
        return {
          ...item,
          business_impact: impact
        };
      })
    );
  }

  async calculateComprehensiveImpact(item) {
    const [
      financialImpact,
      supplyChainImpact,
      operationalImpact,
      marketImpact,
      timelineImpact
    ] = await Promise.all([
      this.assessFinancialImpact(item),
      this.assessSupplyChainImpact(item),
      this.assessOperationalImpact(item),
      this.assessMarketImpact(item),
      this.assessTimelineImpact(item)
    ]);

    // Calculate weighted overall impact score
    const overallImpact = this.calculateWeightedImpact({
      financial: financialImpact,
      supply_chain: supplyChainImpact,
      operational: operationalImpact,
      market: marketImpact,
      timeline: timelineImpact
    });

    return {
      financial: financialImpact,
      supply_chain: supplyChainImpact,
      operational: operationalImpact,
      market: marketImpact,
      timeline: timelineImpact,
      overall_score: overallImpact,
      impact_category: this.categorizeImpact(overallImpact),
      confidence_level: this.calculateImpactConfidence(item)
    };
  }

  async assessFinancialImpact(item) {
    const content = item.content.toLowerCase();
    const numericalData = item.analysis.numerical_insights;
    
    // Extract financial indicators
    const revenueImpact = this.estimateRevenueImpact(content, numericalData);
    const costImpact = this.estimateCostImpact(content, numericalData);
    const marketCapImpact = this.estimateMarketCapImpact(content, numericalData);
    
    // Risk-based financial modeling
    const riskMultiplier = this.calculateRiskMultiplier(item.analysis.risk_patterns);
    const industryMultiplier = this.getIndustryMultiplier(item.analysis.industries);
    
    const totalImpact = (revenueImpact + costImpact + marketCapImpact) * riskMultiplier * industryMultiplier;
    
    return {
      estimated_total_impact: totalImpact,
      revenue_impact: revenueImpact,
      cost_impact: costImpact,
      market_cap_impact: marketCapImpact,
      impact_range: {
        min: totalImpact * 0.3,
        max: totalImpact * 2.5
      },
      currency: 'USD',
      confidence: this.calculateFinancialConfidence(numericalData, content)
    };
  }

  async assessSupplyChainImpact(item) {
    const analysis = item.analysis;
    
    // Supply chain risk factors
    const logisticsRisk = this.assessLogisticsRisk(analysis);
    const supplierRisk = this.assessSupplierRisk(analysis);
    const demandRisk = this.assessDemandRisk(analysis);
    const inventoryRisk = this.assessInventoryRisk(analysis);
    
    return {
      logistics_risk: logisticsRisk,
      supplier_risk: supplierRisk,
      demand_risk: demandRisk,
      inventory_risk: inventoryRisk,
      overall_supply_risk: Math.max(logisticsRisk, supplierRisk, demandRisk, inventoryRisk),
      affected_regions: this.identifyAffectedRegions(analysis),
      recovery_timeline: this.estimateRecoveryTimeline(analysis),
      mitigation_strategies: this.suggestMitigationStrategies(analysis)
    };
  }

  estimateRevenueImpact(content, numericalData) {
    let impact = 0;
    
    // Direct revenue mentions
    const revenuePatterns = [
      /revenue.*down.*(\d+)%/gi,
      /sales.*decline.*(\d+)%/gi,
      /\$(\d+(?:\.\d+)?).*billion.*loss/gi,
      /\$(\d+(?:\.\d+)?).*million.*impact/gi
    ];
    
    revenuePatterns.forEach(pattern => {
      const matches = content.matchAll(pattern);
      for (const match of matches) {
        const number = parseFloat(match[1]);
        if (match[0].includes('billion')) {
          impact += number * 1000000000;
        } else if (match[0].includes('million')) {
          impact += number * 1000000;
        } else if (match[0].includes('%')) {
          // Percentage impact - estimate based on typical industry revenues
          impact += this.estimatePercentageImpact(number);
        }
      }
    });
    
    // Indirect impact from numerical data
    if (numericalData.percentages.length > 0) {
      const maxPercentage = Math.max(...numericalData.percentages);
      if (maxPercentage > 10) {
        impact += this.estimateIndirectRevenueImpact(maxPercentage);
      }
    }
    
    return impact;
  }
}

Stage 4: Relevance Scoring and Prioritization

class RelevanceScorer {
  constructor() {
    this.userProfileService = new UserProfileService();
    this.industryContextService = new IndustryContextService();
    this.geographicContextService = new GeographicContextService();
  }

  async scoreRelevance(assessedItems, userContext = null) {
    return await Promise.all(
      assessedItems.map(async (item) => {
        const relevanceScore = await this.calculateRelevanceScore(item, userContext);
        return {
          ...item,
          relevance_score: relevanceScore,
          priority_rank: 0 // Will be set after sorting
        };
      })
    );
  }

  async calculateRelevanceScore(item, userContext) {
    const factors = {
      industry_alignment: await this.scoreIndustryAlignment(item, userContext),
      geographic_relevance: await this.scoreGeographicRelevance(item, userContext),
      business_impact_magnitude: this.scoreImpactMagnitude(item),
      timeline_urgency: this.scoreTimelineUrgency(item),
      content_quality: this.scoreContentQuality(item),
      source_credibility: this.scoreSourceCredibility(item),
      freshness: this.scoreFreshness(item),
      user_interaction_history: await this.scoreUserInteractionHistory(item, userContext)
    };

    // Weighted combination of factors
    const weights = {
      industry_alignment: 0.25,
      geographic_relevance: 0.15,
      business_impact_magnitude: 0.20,
      timeline_urgency: 0.15,
      content_quality: 0.10,
      source_credibility: 0.05,
      freshness: 0.05,
      user_interaction_history: 0.05
    };

    let relevanceScore = 0;
    for (const [factor, score] of Object.entries(factors)) {
      relevanceScore += score * weights[factor];
    }

    return {
      overall_score: Math.min(1.0, relevanceScore),
      factor_breakdown: factors,
      explanation: this.generateRelevanceExplanation(factors, weights)
    };
  }

  scoreImpactMagnitude(item) {
    const impact = item.business_impact;
    let score = 0;

    // Financial impact scoring
    if (impact.financial.estimated_total_impact > 1000000000) { // $1B+
      score += 0.4;
    } else if (impact.financial.estimated_total_impact > 100000000) { // $100M+
      score += 0.3;
    } else if (impact.financial.estimated_total_impact > 10000000) { // $10M+
      score += 0.2;
    }

    // Supply chain risk scoring
    if (impact.supply_chain.overall_supply_risk > 0.8) {
      score += 0.3;
    } else if (impact.supply_chain.overall_supply_risk > 0.6) {
      score += 0.2;
    } else if (impact.supply_chain.overall_supply_risk > 0.4) {
      score += 0.1;
    }

    // Risk pattern severity
    if (impact.overall_score > 0.8) {
      score += 0.3;
    } else if (impact.overall_score > 0.6) {
      score += 0.2;
    }

    return Math.min(1.0, score);
  }
}

Stage 5: Knowledge Base Integration

class KnowledgeBaseIntegrator {
  constructor() {
    this.vectorStore = new VectorStore();
    this.embeddingService = new EmbeddingService();
    this.ragUpdater = new RAGUpdater();
  }

  async integrateIntoKnowledgeBase(scoredItems) {
    // Filter high-value items for knowledge base
    const highValueItems = scoredItems.filter(item => 
      item.relevance_score.overall_score > 0.6 ||
      item.business_impact.overall_score > 0.7
    );

    const processedItems = await Promise.all(
      highValueItems.map(async (item) => {
        return await this.processForKnowledgeBase(item);
      })
    );

    // Update RAG system
    await this.ragUpdater.updateWithNewIntelligence(processedItems);

    return {
      processed_count: processedItems.length,
      knowledge_updates: processedItems.map(item => ({
        id: item.id,
        categories: item.kb_categories,
        impact_areas: item.kb_impact_areas
      }))
    };
  }

  async processForKnowledgeBase(item) {
    // Generate embeddings for semantic search
    const embeddings = await this.embeddingService.generateEmbeddings([
      item.title,
      item.content,
      item.analysis.entities.supply_chain_terms.join(' ')
    ]);

    // Create knowledge base entry
    const kbEntry = {
      id: item.id,
      title: item.title,
      content: item.content,
      summary: await this.generateExecutiveSummary(item),
      
      // Structured data for RAG
      entities: item.analysis.entities,
      industries: item.analysis.industries.primary_industry,
      geographic_regions: item.analysis.geography.regions,
      risk_factors: Object.keys(item.analysis.risk_patterns.detected_risks),
      business_impact_summary: this.summarizeBusinessImpact(item.business_impact),
      
      // Search and retrieval data
      embeddings: embeddings,
      keywords: this.extractKeywords(item),
      categories: this.categorizeForKB(item),
      
      // Metadata
      source: item.source,
      published_at: item.published_at,
      processed_at: new Date(),
      relevance_score: item.relevance_score.overall_score,
      
      // S&OP specific tags
      s_and_op_tags: this.generateSandOPTags(item)
    };

    return kbEntry;
  }

  generateSandOPTags(item) {
    const tags = [];
    
    // Supply-side tags
    if (item.business_impact.supply_chain.supplier_risk > 0.6) {
      tags.push('supplier_risk');
    }
    if (item.business_impact.supply_chain.logistics_risk > 0.6) {
      tags.push('logistics_risk');
    }
    
    // Demand-side tags
    if (item.business_impact.supply_chain.demand_risk > 0.6) {
      tags.push('demand_risk');
    }
    
    // Cost tags
    if (item.business_impact.financial.cost_impact > 10000000) {
      tags.push('significant_cost_impact');
    }
    
    // Timeline tags
    if (item.business_impact.timeline.urgency_score > 0.8) {
      tags.push('immediate_action_required');
    }
    
    // Industry-specific tags
    const primaryIndustry = item.analysis.industries.primary_industry;
    if (primaryIndustry) {
      tags.push(`${primaryIndustry}_industry`);
    }
    
    return tags;
  }
}

2. Integration with ChainAlign's RAG System

RAG Enhancement Service

class RAGEnhancementService {
  constructor() {
    this.knowledgeGraph = new KnowledgeGraphService();
    this.contextualRetrieval = new ContextualRetrievalService();
  }

  async enhanceRAGWithMarketIntelligence(kbEntries) {
    // Update knowledge graph with new relationships
    await this.updateKnowledgeGraph(kbEntries);
    
    // Create contextual retrieval patterns
    await this.createRetrievalPatterns(kbEntries);
    
    // Generate proactive insights
    const proactiveInsights = await this.generateProactiveInsights(kbEntries);
    
    return {
      knowledge_graph_updates: this.knowledgeGraph.getUpdateSummary(),
      new_retrieval_patterns: proactiveInsights.length,
      proactive_insights: proactiveInsights
    };
  }

  async updateKnowledgeGraph(kbEntries) {
    for (const entry of kbEntries) {
      // Create entity relationships
      const relationships = this.extractRelationships(entry);
      await this.knowledgeGraph.addRelationships(relationships);
      
      // Update industry-specific knowledge
      await this.knowledgeGraph.updateIndustryKnowledge(
        entry.industries,
        entry.business_impact_summary
      );
      
      // Update geographic knowledge
      await this.knowledgeGraph.updateGeographicKnowledge(
        entry.geographic_regions,
        entry.risk_factors
      );
    }
  }
}

This AI processing pipeline provides ChainAlign with intelligent market intelligence that directly feeds into S&OP decision-making, automatically scoring relevance, assessing business impact, and integrating with your RAG system for contextual insights.