How we built a scalable document processing system that converts PDFs to searchable text using modern web technologies

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The Problem: Turning Static PDFs into Actionable Data

Picture this: You have thousands of PDF documents containing valuable information, but they’re essentially digital paperweights. Users can’t search through them effectively, extract insights, or build applications on top of the content. This is exactly the challenge we faced when building Bom Condutor, a driving education platform for Cape Verde.

Our platform needed to ingest government traffic regulation PDFs and make them searchable and interactive for students. The documents contained crucial information about traffic signs, rules, and regulations, but in their static PDF format, they were practically unusable for modern web applications.

The requirements were clear:

• Accept PDF uploads through a web API
• Convert documents to searchable text while preserving structure
• Handle large documents (dozens of pages) without blocking the main application
• Provide real-time processing status and error handling
• Scale to handle multiple concurrent document uploads

After researching various solutions, we decided to build our own PDF data ingestion pipeline using TypeScript, the Wasp full-stack framework, and AI-powered OCR. Here’s the complete story of how we built it.

Why We Chose This Tech Stack

Wasp Framework: The Game Changer

Wasp is a declarative DSL that generates React + Node.js + Prisma applications. What makes it perfect for this use case:

• Built-in job queues with PgBoss for background processing
• Type-safe operations between frontend and backend
• Integrated database operations with Prisma ORM
• Zero-config deployment and development setup

The Supporting Cast

• TypeScript: Type safety across the entire pipeline
• pdf2pic: Reliable PDF-to-image conversion
• Mistral AI OCR: State-of-the-art text extraction
• PostgreSQL: Robust data persistence with JSONB support
• PgBoss: Production-ready job queue built on PostgreSQL

This combination gave us enterprise-grade reliability with startup-level development speed.

System Architecture: A Bird’s Eye View

Our PDF data ingestion pipeline follows a three-stage architecture designed for reliability, scalability, and maintainability:

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   PDF Upload    │───▶│  Background Jobs │───▶│   Database      │
│   API Endpoint  │    │    (PgBoss)      │    │  (PostgreSQL)   │
└─────────────────┘    └──────────────────┘    └─────────────────┘
│                       │                       │
▼                       ▼                       ▼
File Validation        Image Processing        Content Storage
& Initial Storage      & OCR Pipeline          (Structured Data)

The Three-Phase Processing Pipeline

Phase 1: Upload & Validation

• Immediate PDF upload via REST API
• File validation (type, size, format)
• Database record creation with PROCESSING status
• Background job submission for heavy processing
• Instant response to client with document tracking ID

Phase 2: Image Generation

• PDF pages converted to high-quality PNG images using pdf2pic
• Images stored in organized file system structure
• Document metadata updated with total page count
• Individual OCR jobs queued for each page

Phase 3: Content Extraction

• Mistral AI OCR processes each image independently
• Extracted markdown content saved to database
• Progress tracking across all pages
• Document status updated to COMPLETED when all pages finish

Database Schema Design

Our schema is optimized for both write performance during processing and read performance for content queries:

-- Document tracking table
CREATE TABLE "Document" (
"id" TEXT PRIMARY KEY,
"name" TEXT NOT NULL,
"path" TEXT NOT NULL,
"totalPages" INTEGER NOT NULL,
"status" "DocumentStatus" NOT NULL, -- PROCESSING/COMPLETED/FAILED
"createdAt" TIMESTAMP DEFAULT NOW(),
"updatedAt" TIMESTAMP NOT NULL
);

-- Individual page content storage
CREATE TABLE "DocumentPage" (
"id" TEXT PRIMARY KEY,
"documentId" TEXT REFERENCES "Document"("id"),
"number" INTEGER NOT NULL,
"path" TEXT, -- Image file path
"markdown" TEXT NOT NULL, -- OCR extracted content
"createdAt" TIMESTAMP DEFAULT NOW()
);

-- Performance indexes
CREATE INDEX "idx_doc_pages" ON "DocumentPage"("documentId", "number");
CREATE INDEX "idx_doc_status" ON "Document"("status");

This design enables us to:

• Track processing status in real-time
• Handle partial failures gracefully
• Query content efficiently
• Support document versioning (future enhancement)

Implementation Deep Dive

Let’s walk through the key components of our implementation, starting with the API endpoint and moving through the background processing pipeline.

1. The Upload API Endpoint

Our upload endpoint is built with Express middleware and Wasp’s type-safe operations:

// main.wasp - API definition
api ingestPdf {
httpRoute: (POST, "/api/v1/data-ingestion/pdf"),
fn: import { ingestPdf } from "@src/server/data_ingestion/apis/ingest-pdf",
entities: [User]
}

// ingest-pdf.ts - Implementation
export const ingestPdf: IngestPdf = async (req, res, _context) => {
try {
// File validation
if (!req.file || req.file.mimetype !== 'application/pdf') {
throw new HttpError(400, "Valid PDF file required");
}

// Extract metadata
const fileMetadata = {
name: req.file.originalname,
type: req.file.mimetype,
size: req.file.size,
};

// Create database record immediately
const documentId = await createDocument({
name: fileMetadata.name,
path: fileMetadata.name,
totalPages: 0 // Updated after processing
});

// Return success immediately
res.json({
success: true,
message: "PDF uploaded successfully. Processing started.",
documentId: documentId
});

// Submit background job (non-blocking)
await processPdfToImages.submit({
fileBufferString: req.file.buffer.toString('base64'),
fileMetadata: fileMetadata,
documentId: documentId
});

} catch (error) {
console.error("Upload failed:", error);
throw new HttpError(500, "PDF processing failed");
}
};

Key Design Decisions:

• Immediate response: Client gets instant feedback with tracking ID
• Base64 encoding: File buffer serialized for job queue persistence
• Comprehensive validation: Multiple layers of file checking
• Error isolation: Upload failures don’t affect background processing

2. Stage 1: PDF-to-Image Conversion

The first background job converts PDF pages to high-quality images:

// pdf-to-image.job.ts
export const processPdfToImages: ProcessPdfToImages<ProcessPdfArgs, void> =
async (args, context) => {
const { fileBufferString, fileMetadata, documentId } = args;
const fileBuffer = Buffer.from(fileBufferString, 'base64');

try {
// Configure pdf2pic for optimal quality/performance balance
const convertOptions = {
density: 150,           // DPI - good quality without huge files
saveFilename: `${baseName}_page`,
savePath: imagesDir,
format: "png" as const,
width: 800,            // Max width for web display
height: 1200           // Preserve aspect ratio
};

const convert = fromBuffer(fileBuffer, convertOptions);
const results = await convert.bulk(-1, { responseType: "image" });

// Update document with actual page count
await context.entities.Document.update({
where: { id: documentId },
data: {
totalPages: results.length,
status: DocumentStatus.PROCESSING
}
});

// Submit OCR job for each page independently
for (const [index, result] of results.entries()) {
await extractAndProcessPageContent.delay(index).submit({
pageNumber: index + 1,
imagePath: path.basename(result.path),
documentId: documentId
});
}

console.log(`✅ Generated ${results.length} images for ${fileMetadata.name}`);

} catch (error) {
// Mark document as failed and log for monitoring
await updateDocumentStatus(documentId, DocumentStatus.FAILED);
throw error;
}
};

Performance Optimizations:

• Delayed job submission: Pages processed with staggered delays to prevent OCR API rate limiting
• Optimal image settings: Balanced quality/file size for web applications
• Atomic operations: Database updates happen only after successful image generation

3. Stage 2: OCR Content Extraction

Each page gets processed independently by our OCR pipeline:

// extract-and-process-page-content.job.ts
export const extractAndProcessPageContent: ExtractAndProcessPageContent =
async (args, context) => {
const { pageNumber, imagePath, documentId } = args;
const fullImagePath = path.join(BASE_PATH, imagePath);

try {
// Perform OCR using Mistral AI
const markdownContent = await performOcrOnImage(fullImagePath);

// Save extracted content to database
await saveDocumentPage({
documentId: documentId,
number: pageNumber,
path: imagePath,
markdown: markdownContent
});

// Check if all pages are complete
const document = await context.entities.Document.findFirst({
where: { id: documentId },
include: { pages: { where: { deletedAt: null } } }
});

// Mark document complete when all pages processed
if (document && document.pages.length === document.totalPages) {
await updateDocumentStatus(documentId, DocumentStatus.COMPLETED);
console.log(`🎉 Document ${documentId} fully processed`);
}

} catch (error) {
console.error(`OCR failed for page ${pageNumber}:`, error);
await updateDocumentStatus(documentId, DocumentStatus.FAILED);
throw error;
}
};

4. OCR Integration with Mistral AI

Our OCR service provides high-quality text extraction:

// mistral-ai.ts
export async function performOcrOnImage(imagePath: string): Promise<string> {
const mistral = new Mistral({
apiKey: process.env.MISTRAL_API_KEY ?? "",
});

const base64Image = await loadImageAsBase64(imagePath);

const ocrResponse = await mistral.ocr.process({
model: "mistral-ocr-latest",
document: {
imageUrl: base64Image,
type: "image_url",
},
});

if (ocrResponse.pages && ocrResponse.pages.length > 0) {
return ocrResponse.pages[0].markdown;
}

throw new Error("No content extracted from OCR");
}

Why Mistral AI OCR?

• High accuracy: Superior text recognition compared to traditional OCR
• Markdown output: Structured format preserving document formatting
• Multi-language support: Handles Portuguese content excellently
• API reliability: Enterprise-grade uptime and rate limiting

Challenges We Encountered (And How We Solved Them)

Building a production-ready document processing pipeline isn’t just about happy path scenarios. Here are the real-world challenges we faced and our solutions:

Challenge 1: Memory Management with Large PDFs

Problem: Large PDF files (>5MB) were causing memory issues when converting to images, especially with high DPI settings.

Solution: Implemented streaming buffer processing and optimized pdf2pic configuration:

// Before: Memory spikes with large files
const convert = fromBuffer(fileBuffer, { density: 300, ... });

// After: Balanced approach for web applications
const convertOptions = {
density: 150,        // Reduced from 300 DPI
width: 800,          // Max width constraint
height: 1200,        // Preserve aspect ratio
quality: 85          // Slight compression for file size
};

Result: 60% reduction in memory usage while maintaining text readability.

Challenge 2: OCR Rate Limiting and Failures

Problem: Mistral AI has rate limits, and simultaneous OCR requests for multi-page documents were hitting API limits.

Solution: Implemented intelligent job delays and retry logic:

// Stagger OCR jobs to respect rate limits
for (const [index, result] of results.entries()) {
await extractAndProcessPageContent
.delay(index * 2) // 2-second delays between pages
.submit({
pageNumber: index + 1,
imagePath: path.basename(result.path),
documentId: documentId
});
}

Result: Zero rate limit errors and improved overall processing reliability.

Challenge 3: Partial Processing Failures

Problem: If one page failed OCR, the entire document would be marked as failed, even if other pages processed successfully.

Solution: Implemented granular error handling and recovery:

// Allow partial success with detailed error tracking
try {
const markdownContent = await performOcrOnImage(fullImagePath);
await saveDocumentPage({ /* ... */ });
} catch (error) {
// Log error but don't fail entire document
console.error(`Page ${pageNumber} failed, continuing with other pages`);

// Save page with error status for manual review
await saveDocumentPage({
documentId,
number: pageNumber,
path: imagePath,
markdown: `[OCR_ERROR]: ${error.message}`,
status: 'FAILED'
});
}

Result: Documents with partial failures can still be useful, with clear indication of problematic pages.

Challenge 4: File System Organization and Cleanup

Problem: Generated images were accumulating without cleanup, and file paths were hardcoded.

Solution: Implemented organized file structure and cleanup jobs:

// Organized file naming with document context
const baseName = path.parse(fileMetadata.originalName).name.replace(/[^a-zA-Z0-9_-]/g, '_');
const imagesDir = path.join(process.env.STORAGE_PATH, 'documents', documentId);

// Future cleanup job (in backlog)
job cleanupProcessedImages {
executor: PgBoss,
perform: { fn: import { cleanupOldImages } from "@src/jobs/cleanup" },
schedule: { cron: "0 2 * * *" } // Daily at 2 AM
}

Result: Better file organization and foundation for automated cleanup.

Performance and Scalability Considerations

Our pipeline handles real-world production loads with these performance characteristics:

Current Performance Metrics

Processing Speed:

• Small PDFs (1-5 pages): ~30-60 seconds end-to-end
• Medium PDFs (10-20 pages): ~2-4 minutes
• Large PDFs (30+ pages): ~5-10 minutes
• Concurrent documents: Up to 10 simultaneous processing jobs

Resource Usage:

• Memory: ~200MB peak per document during image generation
• Storage: ~1.5MB per page (PNG images + database)
• CPU: Moderate usage, mostly I/O bound waiting on OCR API

Scalability Design Decisions

Horizontal Scaling Ready:

// Job queue handles distribution across multiple workers
job processPdfToImages {
executor: PgBoss,
perform: { fn: import { processPdfToImages } from "@src/jobs/pdf-processing" },
// PgBoss automatically distributes across multiple Node.js instances
}

Database Optimization:

-- Indexes for common query patterns
CREATE INDEX "idx_document_status_created" ON "Document"("status", "createdAt");
CREATE INDEX "idx_page_content_search" ON "DocumentPage" USING gin(to_tsvector('english', "markdown"));

-- Partitioning strategy for large deployments
CREATE TABLE "DocumentPage_2024" PARTITION OF "DocumentPage"
FOR VALUES FROM ('2024-01-01') TO ('2025-01-01');

Rate Limiting and Circuit Breakers:

// OCR service protection
const ocrWithRetry = async (imagePath: string, retries = 3): Promise<string> => {
for (let attempt = 1; attempt <= retries; attempt++) {
try {
return await performOcrOnImage(imagePath);
} catch (error) {
if (attempt === retries) throw error;

// Exponential backoff: 2s, 4s, 8s
await new Promise(resolve => setTimeout(resolve, 2000 * attempt));
console.warn(`OCR retry attempt ${attempt} for ${imagePath}`);
}
}
};

Monitoring and Observability

Key Metrics We Track:

• Job queue length and processing times
• OCR API success/failure rates
• Storage usage growth
• Database query performance
• Memory usage patterns during processing

Health Checks:

// API endpoint for system health
api systemHealth {
httpRoute: (GET, "/api/v1/health"),
fn: import { getSystemHealth } from "@src/monitoring/health"
}

export const getSystemHealth = async () => ({
database: await checkDatabaseConnection(),
jobQueue: await checkJobQueueHealth(),
ocrService: await checkOcrServiceStatus(),
storage: await checkStorageAvailability(),
timestamp: new Date().toISOString()
});

Scaling Bottlenecks and Solutions

Current Bottleneck: OCR API Rate Limits

• Problem: Mistral AI limits concurrent requests
• Solution: Intelligent request queuing with backoff
• Future: Multi-provider OCR with automatic failover

Future Bottleneck: Database Writes

• Anticipated: High-volume concurrent page saves
• Solution: Write-optimized database configuration and potential read replicas

Storage Considerations:

• Current: Local file system for development
• Production: S3-compatible storage with CDN for image serving
• Cleanup: Automated deletion of processed images after content extraction

Lessons Learned and Key Takeaways

Building this PDF processing pipeline taught us valuable lessons about modern web application architecture:

1. Choose the Right Level of Abstraction

Lesson: Wasp’s declarative approach eliminated huge amounts of boilerplate while maintaining flexibility.

// This simple Wasp declaration...
job processPdfToImages {
executor: PgBoss,
perform: { fn: import { processPdfToImages } from "@src/jobs/pdf-processing" },
entities: [Document, DocumentPage]
}

// ...generates type-safe job submission, queue management, and error handling
await processPdfToImages.submit({ documentId, fileBuffer, metadata });

Impact: Reduced development time by ~40% compared to setting up raw Express + PgBoss + Prisma.

2. Design for Observability from Day One

Lesson: Comprehensive logging and status tracking saved countless debugging hours.

// Every major operation logs structured data
console.log(`📤 OCR job submitted - Doc: ${documentId}, Page: ${pageNumber}`, {
documentId,
pageNumber,
jobId: result.id,
timestamp: new Date().toISOString()
});

Impact: Issues can be traced through the entire pipeline with searchable, structured logs.

3. Embrace Async Processing for Better UX

Lesson: Immediate API responses with background processing creates much better user experience than blocking operations.

Before: 60-second API timeouts for large documents

After: Sub-second API responses with real-time status updates

4. Error Handling Should Be Granular and Recoverable

Lesson: Failing fast on individual pages while continuing document processing provides better user value.

// Don't let one bad page kill the entire document
try {
await processPage(pageInfo);
} catch (error) {
await logPageError(pageInfo, error);
// Continue processing other pages
}

Impact: 85% of documents with partial page failures still provide valuable content.

5. AI APIs Are Powerful But Require Defensive Programming

Lesson: External AI services need circuit breakers, retries, and graceful degradation.

Key Strategies:

• Exponential backoff for rate limits
• Structured error responses for debugging
• Fallback mechanisms for service outages
• Cost monitoring for API usage

Future Enhancements and Roadmap

Our current implementation handles the core use case well, but there’s always room for improvement:

Short Term (Next Month)

• Progress Tracking API: Real-time status updates for frontend integration
• Batch Processing: Handle multiple PDFs in a single upload
• Content Validation: Quality checks on extracted text
• File Cleanup Jobs: Automated removal of temporary images

Medium Term (3-6 Months)

• Multi-Provider OCR: Automatic failover between OCR services
• Content Search: Full-text search across all processed documents
• Document Versioning: Handle updates to existing documents
• Advanced Error Recovery: Automatic retry of failed pages

Long Term (6+ Months)

• Real-time Processing: WebSocket updates for live status
• AI Content Enhancement: Automatic tagging and categorization
• Multi-format Support: Word documents, PowerPoint, images
• Enterprise Features: User permissions, audit trails, API keys

Code Quality Improvements

// Current backlog items from our TODO list:
const backlogItems = [
"Add progress tracking query endpoint",
"Implement file cleanup operations",
"Enhanced error recovery with exponential backoff",
"Remove hardcoded file paths",
"Add structured logging with proper levels",
"Implement job queue monitoring and dead letter handling"
];

The Bottom Line

Building a production-ready PDF data ingestion pipeline taught us that the architecture matters more than the individual technologies. By choosing tools that work well together (Wasp + TypeScript + PgBoss + Mistral AI), we built something that’s both powerful and maintainable.

Key Success Factors:

1. Async-first design for better user experience
2. Comprehensive error handling for production reliability
3. Structured logging for operational visibility
4. Type safety across the entire stack
5. Gradual optimization based on real usage patterns

The entire pipeline processes documents reliably in production, handling everything from single-page forms to 50-page government manuals. More importantly, it’s maintainable and extensible for future requirements.

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Want to Build Something Similar?

If you’re working on document processing or considering a similar architecture:

1. Start with the Wasp framework - The productivity boost is real
2. Design your job queue strategy early - Async processing is crucial for good UX
3. Choose your OCR provider carefully - Quality varies dramatically between services
4. Plan for partial failures - Documents are messy, and your system should handle that gracefully

Questions or want to dive deeper into any part of this architecture? Drop a comment below - I love discussing technical architecture and lessons learned from real-world implementations.

Building something similar? I’d love to hear about your approach and any challenges you’re facing. The developer community thrives on sharing these kinds of implementation stories!

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This post is part of our ongoing series on building modern SaaS applications with Wasp. Follow for more deep dives into full-stack TypeScript development and production architecture patterns.

Tags: #WebDev #TypeScript #PDF #OCR #Architecture #Wasp #FullStack #BackgroundJobs #AI