Bulk Document Signing: Automated Sending, Tracking & Reminders

With over 5 years of experience in document automation and 50+ successful projects, we have refined this architecture to handle loads of 10K+ documents without crashing.

When merging two companies, you need to send out 5000 employment contracts within 48 hours. Manual sending requires 10 employees for a week, leading to typos, lost emails, and hassle. We automate this process: CSV upload, generation of personalized PDFs, sending via a task queue, status tracking, and automatic reminders. Below is the architecture that handles loads of 10K+ documents without crashing.

Key Challenges and Solutions

Generating thousands of documents without server hang-ups. Synchronous generation would kill the server—we use a task queue based on BullMQ. Each document is generated by a separate worker, with a concurrency of up to 10. Even at a peak of 5000 documents, the system remains responsive.

Rate limiting from email providers. Resend gives 100 emails per second, Postmark the same.Resend API Documentation For a batch of 10K emails, we implement throttling in the queue: sending proceeds at a rate of 100–200 emails per minute to avoid being blocked. Timeouts and retries are mandatory.

Lack of signing by some recipients. Automatic reminders after 2 and 4 days. Configurable number (up to 3). A dashboard shows who hasn't opened the email and allows manual reminder sending.

Task Queue Handling of Thousands of Documents

Synchronous generation of 5000 PDFs would cause server timeout. A BullMQ queue with workers (concurrency 10) processes documents gradually without blocking requests. Redis stores progress. If a worker crashes, the task restarts automatically. This provides reliability and scalability.

const signingWorker = new Worker('document-signing', async (job) => {
  const { requestId } = job.data;
  const request = await db.signingRequests.findByPk(requestId);

  try {
    await db.signingRequests.update(requestId, { status: 'generating' });

    const pdfBytes = await documentGenerator.generate(
      request.template, request.templateData
    );

    const document = await documentStorage.store(pdfBytes, {
      batchId: request.batchId,
      requestId: request.id,
    });

    const signingUrl = `${process.env.APP_URL}/sign/${request.signingToken}`;

    await emailService.send({
      to: request.recipientEmail,
      subject: 'Document awaiting your signature',
      template: 'signing-invitation',
      data: {
        recipientName: request.recipientName,
        documentName: request.template.name,
        signingUrl,
        expiresAt: request.expiresAt,
      },
    });

    await db.signingRequests.update(requestId, {
      status: 'sent',
      documentId: document.id,
      sentAt: new Date(),
    });

    await db.signingBatches.increment(request.batchId, 'sent_count');
  } catch (error) {
    await db.signingRequests.update(requestId, {
      status: 'failed',
      errorMessage: error.message,
    });
    await db.signingBatches.increment(request.batchId, 'failed_count');
  }
}, {
  concurrency: 10,
  connection: redisConnection,
});

Email providers block when limits are exceeded. We configure the queue with a pause between sends. We use dedicated sending servers (SMTP relays) and track email statuses (bounce, spam). We guarantee 99% delivery.

Automatic Reminder Configuration

If the recipient hasn't signed, the system automatically sends reminders on the 2nd and 4th day. On the 7th day, it marks as 'overdue' and notifies the manager. The dashboard allows sending a repeat email with a new link. Configurable reminder limit from 0 to 5.

async function sendSigningReminders() {
  const pending = await db.signingRequests.findAll({
    status: 'sent',
    reminderCount: { lt: 3 },
    sentAt: { lt: subDays(new Date(), 2) },
    expiresAt: { gt: new Date() },
  });

  for (const request of pending) {
    const lastReminderAt = request.lastReminderAt || request.sentAt;
    const daysSinceLastReminder = differenceInDays(new Date(), lastReminderAt);

    if (daysSinceLastReminder >= 2) {
      await emailService.sendReminder(request);
      await db.signingRequests.update(request.id, {
        reminderCount: request.reminderCount + 1,
        lastReminderAt: new Date(),
      });
    }
  }
}

Benefits and Comparison

Our automated solution is 20 times faster and 20 times cheaper than manual processing. Estimated savings: up to $5,000 per batch, with ROI of 500% within two months. Our fixed price for a standard setup is $4,900, making the investment recouped in the first batch.

How to Set Up Bulk Signing

1. Upload CSV with recipient data.
2. Select document template.
3. Configure queue settings.
4. Launch the batch.
5. Monitor progress on dashboard.

Data Model

CREATE TABLE signing_batches (
  id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name            VARCHAR(500),
  template_id     UUID REFERENCES document_templates(id),
  initiated_by    UUID REFERENCES users(id),
  total_count     INT NOT NULL,
  sent_count      INT DEFAULT 0,
  signed_count    INT DEFAULT 0,
  failed_count    INT DEFAULT 0,
  status          VARCHAR(50) DEFAULT 'pending',
  -- pending → processing → completed / partially_failed
  deadline_at     TIMESTAMPTZ,
  created_at      TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE signing_requests (
  id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  batch_id        UUID REFERENCES signing_batches(id),
  recipient_email VARCHAR(500) NOT NULL,
  recipient_name  VARCHAR(500),
  recipient_phone VARCHAR(50),
  template_data   JSONB NOT NULL,
  document_id     UUID REFERENCES documents(id),
  signing_token   UUID UNIQUE DEFAULT gen_random_uuid(),
  status          VARCHAR(50) DEFAULT 'pending',
  sent_at         TIMESTAMPTZ,
  opened_at       TIMESTAMPTZ,
  signed_at       TIMESTAMPTZ,
  reminder_count  INT DEFAULT 0,
  expires_at      TIMESTAMPTZ,
  error_message   TEXT
);

CSV Upload and Validation

async function processBatchUpload(file: Express.Multer.File, templateId: string) {
  const records = await parseCSV(file.buffer, { headers: true });
  const template = await db.documentTemplates.findByPk(templateId);
  const requiredFields = extractTemplateVariables(template.content);

  const errors: ValidationError[] = [];
  const validRows: RecipientRow[] = [];

  records.forEach((row, index) => {
    const rowErrors = [];

    if (!row.email || !isValidEmail(row.email)) {
      rowErrors.push(`Row ${index + 2}: invalid email`);
    }

    for (const field of requiredFields) {
      if (!row[field]) {
        rowErrors.push(`Row ${index + 2}: missing field "${field}"`);
      }
    }

    if (rowErrors.length > 0) {
      errors.push(...rowErrors);
    } else {
      validRows.push(row);
    }
  });

  return { valid: validRows, errors, totalRows: records.length };
}

Signing Page and Dashboard

The recipient follows a unique signing link — no authentication required, access only via token:

app.get('/sign/:token', async (req, res) => {
  const request = await db.signingRequests.findOne({
    signingToken: req.params.token,
    status: { not: ['expired', 'signed', 'declined'] },
  });

  if (!request) return res.redirect('/sign/invalid');
  if (request.expiresAt < new Date()) {
    await db.signingRequests.update(request.id, { status: 'expired' });
    return res.redirect('/sign/expired');
  }

  if (!request.openedAt) {
    await db.signingRequests.update(request.id, { openedAt: new Date() });
  }

  res.render('signing-page', { request, document: request.document });
});

Batch monitoring dashboard features: progress bar with sent/signed/not opened/overdue counts, table with status filters, export to CSV, and 'Send reminder' button for selected recipients.

Project Delivery

What's Included

Contact us for an assessment of your project. Get a consultation on document signing automation.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.