Technical Architecture

interface RetrievalFirstArchitecture {
  // Prioritize finding existing solutions over creating new ones
  pattern_recognition: {
    similarity_search: VectorSimilarity;
    template_matching: TemplateEngine;
    composition_engine: ComponentAssembler;
  };
 
  // Minimize AI generation, maximize reuse
  generation_minimization: {
    component_reuse: ComponentLibrary;
    pattern_application: PatternMatcher;
    incremental_building: IncrementalAssembler;
  };
 
  // Continuous learning from successful implementations
  learning_loop: {
    success_pattern_storage: PatternStore;
    feedback_integration: FeedbackEngine;
    model_refinement: ModelUpdater;
  };
}

class AgentOrchestrator {
  private agents = {
    business_analyst: new BusinessAnalystAgent(),
    technical_architect: new TechnicalArchitectAgent(),
    data_scientist: new DataScientistAgent(),
    partnership_specialist: new PartnershipSpecialistAgent(),
    risk_assessor: new RiskAssessmentAgent()
  };
 
  async processBusinessQuery(query: BusinessQuery): Promise<BusinessResponse> {
    // Intelligent agent routing based on query characteristics
    const primaryAgent = await this.routeQueryToAgent(query);
    const supportingAgents = await this.identifySupportingAgents(query);
 
    // Collaborative processing
    const primaryResult = await primaryAgent.process(query);
    const supportingInsights = await Promise.all(
      supportingAgents.map(agent => agent.provideInsights(query, primaryResult))
    );
 
    return await this.synthesizeResponse(primaryResult, supportingInsights);
  }
}

class DataIngestionPipeline {
  private sources = {
    game_events: new GameEventIngestion(),
    social_media: new SocialMediaIngestion(),
    market_data: new MarketDataIngestion(),
    partnership_activity: new PartnershipIngestion()
  };
 
  async ingestDataStream(streamType: StreamType): Promise<void> {
    const ingestor = this.sources[streamType];
 
    const stream = await ingestor.connect();
    stream.on('data', async (data) => {
      await this.processAndStore(data);
    });
  }
 
  private async processAndStore(data: RawData): Promise<void> {
    const normalized = await this.normalizeData(data);
 
    if (this.isOperationalData(data)) {
      await this.storeInSupabase(normalized);
    } else {
      await this.storeInFirebase(normalized);
    }
 
    await this.triggerIntelligencePipeline(normalized);
  }
}

class VectorEmbeddingEngine {
  private embeddings = new OpenAIEmbeddings({
    modelName: 'text-embedding-3-large',
    dimensions: 3072
  });
 
  async embedBusinessData(data: BusinessData): Promise<VectorEmbeddings> {
    const textEmbeddings = await this.embedText(data.description);
    const numericalEmbeddings = await this.embedMetrics(data.metrics);
    const categoricalEmbeddings = await this.embedCategories(data.categories);
 
    return {
      text: textEmbeddings,
      numerical: numericalEmbeddings,
      categorical: categoricalEmbeddings,
      combined: this.combineEmbeddings([
        textEmbeddings,
        numericalEmbeddings,
        categoricalEmbeddings
      ])
    };
  }
 
  async semanticSearch(query: string, context: BusinessContext): Promise<SearchResults> {
    const enhancedQuery = await this.enhanceQueryWithContext(query, context);
    const queryEmbeddings = await this.embeddings.embedQuery(enhancedQuery);
 
    return await this.vectorStore.similaritySearch(queryEmbeddings, {
      limit: 10,
      includeMetadata: true,
      filter: this.buildContextFilter(context)
    });
  }
}

// Next.js API Routes with intelligent processing
export default async function handler(
  req: NextApiRequest,
  res: NextApiResponse
) {
  const { query, context } = req.body;
 
  try {
    const intelligence = await intelligenceEngine.search(query, context);
    const formatted = await formatResponseForContext(intelligence, context);
 
    res.status(200).json({
      success: true,
      data: formatted,
      intelligence: intelligence.metadata
    });
  } catch (error) {
    const errorResponse = await handleIntelligentError(error, context);
    res.status(errorResponse.status).json(errorResponse);
  }
}

const typeDefs = gql`
  type League {
    id: ID!
    name: String!
    intelligence: Intelligence!
    partnerships: [Partnership!]!
    analytics: Analytics!
  }
 
  type Intelligence {
    insights: [Insight!]!
    predictions: [Prediction!]!
    recommendations: [Recommendation!]!
    confidence: Float!
  }
 
  type Query {
    league(id: ID!): League
    intelligentSearch(query: String!, context: ContextInput): SearchResult!
    partnershipOpportunities(leagueId: ID!): [Opportunity!]!
  }
`;

interface SecurityLayers {
  authentication: {
    supabase_auth: SupabaseAuth;
    firebase_auth: FirebaseAuth;
    jwt_tokens: JWTManager;
    multi_factor: MFAManager;
  };
 
  authorization: {
    role_based_access: RBACSystem;
    context_based_permissions: ContextPermissions;
    data_level_security: DataSecurity;
  };
 
  data_protection: {
    encryption_at_rest: EncryptionManager;
    encryption_in_transit: TLSManager;
    data_masking: DataMaskingEngine;
  };
}

interface CachingStrategy {
  multi_layer_cache: {
    browser_cache: BrowserCache;
    cdn_cache: CDNCache;
    application_cache: AppCache;
    database_cache: DBCache;
  };
 
  intelligent_invalidation: {
    context_aware_invalidation: ContextInvalidator;
    predictive_invalidation: PredictiveInvalidator;
    usage_pattern_analysis: UsageAnalyzer;
  };
}

# Kubernetes deployment with intelligent scaling
apiVersion: apps/v1
kind: Deployment
metadata:
  name: altsportsleagues-intelligence
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: intelligence-api
        image: altsportsleagues/intelligence:latest
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "2Gi"
            cpu: "1000m"
        ports:
        - containerPort: 3000
        livenessProbe:
          httpGet:
            path: /api/health
            port: 3000
        readinessProbe:
          httpGet:
            path: /api/ready
            port: 3000

frontend/
├── app/                          # Next.js App Router
│   ├── (dashboard)/              # Protected dashboard routes
│   │   └── dashboard/            # Main dashboard pages (100+ routes)
│   ├── (marketing)/              # Public marketing pages
│   └── api/                      # API routes
├── components/                   # React components (193+ files)
│   ├── ui/                       # Shadcn/UI components
│   ├── dashboard/                # Dashboard-specific
│   ├── chat/                     # Chat interface
│   └── mcp/                      # MCP tool integration
├── contexts/                     # React Context Providers
├── hooks/                        # Custom React Hooks (12+)
├── lib/                          # Utility libraries (50+ files)
└── store/                        # Zustand stores