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
-
Network Security
- HTTPS only
- CORS configuration
- Rate limiting
- DDoS protection
-
Application Security
- Input validation
- SQL injection prevention
- XSS protection
- CSRF tokens
-
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
-
Response Caching
- Redis for frequently accessed data
- 5-minute TTL for chat responses
- 1-hour TTL for credit reports
-
Database Caching
- Prisma query caching
- Connection pooling
- Read replicas for scaling
-
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
-
Event-Driven Architecture
- Message queue integration
- Event sourcing for audit trail
- CQRS pattern implementation
-
Microservices Expansion
- Separate scoring service
- Independent document service
- Dedicated notification service
-
Machine Learning Pipeline
- Model training infrastructure
- Feature engineering pipeline
- A/B testing framework
-
Multi-Region Deployment
- Geographic distribution
- Data residency compliance
- Edge computing capabilities
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