Skip to Content

Architecture

Comprehensive overview of the Credit Engine’s system architecture, design patterns, and technical implementation.

System Overview

The Credit Engine is built as a microservice architecture using Next.js 15.4 in API-only mode, designed for scalability, security, and maintainability.

Architecture Principles

1. Microservice Design

  • Single Responsibility: Each service handles one domain
  • Loose Coupling: Services communicate via well-defined APIs
  • High Cohesion: Related functionality grouped together
  • Independent Deployment: Services can be updated independently

2. API-First Approach

  • RESTful Design: Standard HTTP methods and status codes
  • Consistent Interfaces: Predictable request/response formats
  • Versioning Strategy: Backward compatibility maintained
  • OpenAPI Documentation: Auto-generated API docs

3. Security by Design

  • Zero Trust Model: Every request authenticated and authorized
  • Defense in Depth: Multiple security layers
  • Data Encryption: At rest and in transit
  • Audit Logging: Comprehensive activity tracking

Component Architecture

API Layer

// app/api/chat/route.ts export async function POST(request: Request) { // Authentication const session = await auth(); if (!session) { return NextResponse.json({ error: "Unauthorized" }, { status: 401 }); } // Rate limiting const rateLimitResult = await checkRateLimit(session.user.id); if (!rateLimitResult.allowed) { return NextResponse.json({ error: "Rate limit exceeded" }, { status: 429 }); } // Request processing const body = await request.json(); const validated = creditChatSchema.parse(body); // Service invocation const response = await creditService.processChat(validated, session.user); return NextResponse.json(response); }

Service Layer

// lib/services/credit-service.ts export class CreditService { constructor( private aiProvider: AIProvider, private database: PrismaClient, private cache: RedisClient ) {} async processChat(input: ChatInput, user: User) { // Check cache const cached = await this.cache.get(cacheKey); if (cached) return cached; // Process with AI const response = await this.aiProvider.generateResponse({ messages: input.messages, context: await this.buildContext(user), systemPrompt: CREDIT_COACH_PROMPT }); // Store in database await this.database.chatHistory.create({ data: { userId: user.id, messages: input.messages, response: response } }); // Cache result await this.cache.set(cacheKey, response, { ttl: 300 }); return response; } }

Data Layer

// prisma/schema.prisma model User { id String @id @default(cuid()) email String @unique creditScore Int? creditGoal Int? createdAt DateTime @default(now()) updatedAt DateTime @updatedAt chatHistory ChatHistory[] creditReports CreditReport[] documents Document[] } model ChatHistory { id String @id @default(cuid()) userId String messages Json response Json createdAt DateTime @default(now()) user User @relation(fields: [userId], references: [id]) @@index([userId, createdAt]) } model CreditReport { id String @id @default(cuid()) userId String bureau String // EXPERIAN | EQUIFAX | TRANSUNION score Int reportData Json createdAt DateTime @default(now()) user User @relation(fields: [userId], references: [id]) @@index([userId, bureau, createdAt]) }

AI Integration Architecture

Claude Provider Implementation

// lib/ai/claude-provider.ts import Anthropic from '@anthropic-ai/sdk'; export class ClaudeProvider implements AIProvider { private client: Anthropic; constructor(apiKey: string) { this.client = new Anthropic({ apiKey }); } async generateResponse(params: GenerateParams): Promise<AIResponse> { const response = await this.client.messages.create({ model: 'claude-3-5-sonnet-20241022', max_tokens: 4096, system: params.systemPrompt, messages: params.messages, temperature: 0.7, }); return { content: response.content[0].text, usage: { promptTokens: response.usage.input_tokens, completionTokens: response.usage.output_tokens, } }; } }

Prompt Engineering

// lib/ai/credit-prompts.ts export const CREDIT_COACH_PROMPT = ` You are a certified credit counselor with expertise in Canadian credit systems. Your role is to provide actionable, personalized credit improvement advice. Key responsibilities: 1. Analyze credit situations comprehensively 2. Provide specific, actionable recommendations 3. Create realistic timelines for credit improvement 4. Explain complex credit concepts in simple terms 5. Consider Canadian-specific credit regulations Always: - Be encouraging but realistic - Provide concrete next steps - Reference specific credit factors - Consider individual circumstances - Maintain professional boundaries `;

Security Architecture

Authentication Flow

Security Layers

  1. Network Security

    • HTTPS only
    • CORS configuration
    • Rate limiting
    • DDoS protection
  2. Application Security

    • Input validation
    • SQL injection prevention
    • XSS protection
    • CSRF tokens
  3. Data Security

    • Encryption at rest
    • Encryption in transit
    • PII anonymization
    • Secure key management

Scalability Architecture

Horizontal Scaling

# vercel.json { "functions": { "app/api/chat/route.ts": { "maxDuration": 60, "memory": 1024 }, "app/api/analyze/route.ts": { "maxDuration": 30, "memory": 512 } }, "regions": ["iad1", "sfo1"] }

Caching Strategy

  1. Response Caching

    • Redis for frequently accessed data
    • 5-minute TTL for chat responses
    • 1-hour TTL for credit reports
  2. Database Caching

    • Prisma query caching
    • Connection pooling
    • Read replicas for scaling
  3. CDN Caching

    • Static assets on Vercel Edge
    • API responses with cache headers

Performance Optimization

Database Optimization

-- Indexes for common queries CREATE INDEX idx_chat_history_user_created ON chat_history(user_id, created_at DESC); CREATE INDEX idx_credit_reports_user_bureau ON credit_reports(user_id, bureau, created_at DESC); -- Partitioning for large tables CREATE TABLE chat_history_2025 PARTITION OF chat_history FOR VALUES FROM ('2025-01-01') TO ('2026-01-01');

API Response Optimization

// Streaming responses for large data export async function GET(request: Request) { const stream = new ReadableStream({ async start(controller) { const data = await fetchLargeDataset(); for (const chunk of data) { controller.enqueue(JSON.stringify(chunk)); } controller.close(); } }); return new Response(stream, { headers: { 'Content-Type': 'application/json', 'Transfer-Encoding': 'chunked' } }); }

Monitoring & Observability

Logging Architecture

// lib/logging/logger.ts import winston from 'winston'; export const logger = winston.createLogger({ level: process.env.LOG_LEVEL || 'info', format: winston.format.json(), transports: [ new winston.transports.Console(), new winston.transports.File({ filename: 'error.log', level: 'error' }), new winston.transports.File({ filename: 'combined.log' }) ] }); // Usage logger.info('Credit analysis started', { userId: user.id, requestId: req.id });

Metrics Collection

// lib/metrics/collector.ts export class MetricsCollector { async recordApiCall(endpoint: string, duration: number, status: number) { await this.prometheus.histogram('api_duration', duration, { endpoint, status }); } async recordCreditAnalysis(userId: string, score: number) { await this.prometheus.gauge('credit_score', score, { userId }); } }

Error Handling

Global Error Handler

// lib/errors/handler.ts export class ErrorHandler { static handle(error: unknown): Response { if (error instanceof ValidationError) { return NextResponse.json( { error: 'Invalid input', details: error.errors }, { status: 400 } ); } if (error instanceof AuthenticationError) { return NextResponse.json( { error: 'Authentication required' }, { status: 401 } ); } // Log unexpected errors logger.error('Unexpected error', { error }); return NextResponse.json( { error: 'Internal server error' }, { status: 500 } ); } }

Future Architecture Considerations

Planned Enhancements

  1. Event-Driven Architecture

    • Message queue integration
    • Event sourcing for audit trail
    • CQRS pattern implementation
  2. Microservices Expansion

    • Separate scoring service
    • Independent document service
    • Dedicated notification service
  3. Machine Learning Pipeline

    • Model training infrastructure
    • Feature engineering pipeline
    • A/B testing framework
  4. Multi-Region Deployment

    • Geographic distribution
    • Data residency compliance
    • Edge computing capabilities
Last updated on