Automate Business Processes with Make (Integromat): Save Hours Daily

How Automating with Make Eliminates Manual Data Transfer

Every day, businesses repeat the same operations: moving orders from an online store to CRM, updating stock levels in 1C, sending invoices to clients. On average, these tasks consume 2–4 hours daily. As volumes grow, N+1 database queries only worsen the situation. Manual entry errors can lead to losses of up to 5% of revenue. We configure Make (formerly Integromat) scenarios that handle all the routine, freeing employees' time and reducing operational costs.

On one project, we automated synchronization between Shopify, Freshdesk, and Telegram. Previously, a manager manually copied orders from Shopify to Freshdesk and sent notifications in Telegram. After implementing Make, the processing time per order dropped from 180 seconds to 2–3 seconds — 60–90 times faster. Error rate fell to zero.

Which Business Processes Can You Automate with Make?

Make fits any task that involves data transfer between services: CRM, email, messengers, databases, and APIs. Unlike Zapier, Make supports branching (Router), iterators (Iterator), and aggregators (Aggregator). This allows processing arrays of thousands of records with individual conditions. For example, when syncing a product catalog with marketplaces: one scenario can break down an array, check prices, update stock, and compile a report — all without human intervention. Make also offers more flexible pricing: up to 1,000 operations per month on the free plan — 10 times more than Zapier (only 100). The HTTP module lets you integrate any service's API, even legacy systems.

How Do the HTTP Module, Iterators, and Aggregators Work?

The HTTP module sends requests to any REST API. Example POST request configuration:

{
  "url": "https://api.example.com/orders",
  "method": "POST",
  "headers": [
    { "name": "Authorization", "value": "Bearer {{1.api_token}}" },
    { "name": "Content-Type", "value": "application/json" }
  ],
  "body": {
    "orderId": "{{1.id}}",
    "customer": {
      "email": "{{1.customer.email}}",
      "name": "{{1.customer.first_name}} {{1.customer.last_name}}"
    },
    "amount": "{{1.total_price}}",
    "currency": "RUB"
  }
}

An Iterator splits an array into individual bundles — each element goes through a chain of modules. An Aggregator collects results into a single response. This is essential for handling shopping carts or product lists. Example processing an items array from an order:

[Webhook: new order with items[]]
           │
[Iterator: split items[] into bundles]
    │ (one by one items[i])
[Lookup: find product in DB by SKU]
    │
[HTTP: decrease stock quantity]
    │
[Aggregator: collect results]
    │
[Email: final report]

Step-by-step scenario setup with a webhook and iterator:

1. Create a new scenario in Make and select the Webhook trigger.
2. Copy the webhook URL and configure the external service to send POST requests.
3. Add an Iterator module, pointing to the array from the request body.
4. Inside the iterator, configure an HTTP request to your API for each array element.
5. Add an Aggregator module to bundle results, choosing source data and structure.
6. Set up a notification module (e.g., Email) to send the final report.

How to Handle Errors in Make Scenarios?

Make provides an Error Handler Module — a special branch executed on failure. According to Make documentation, the Error Handler supports four strategies:

• Ignore — skip the error, continue processing next bundles.
• Break — stop the current bundle, move to next.
• Retry — retry the module up to 5 times with configurable delay.
• Rollback — undo changes in supporting modules.

This variety gives 4 times more flexibility compared to Zapier, which only offers Retry. We always configure error notifications to Telegram or Slack so you know about issues instantly. This reduces downtime and minimizes financial losses.

Automation Setup Process and Timelines

Pricing is tailored to scenario complexity. A simple scenario with 3–5 modules and one condition takes 1–2 days; a scenario with iterator and aggregator takes 3–5 days; a turnkey solution with training and documentation takes up to 7 days.

Comparison: Make vs. Zapier

What Is Included in the Work

• Scenario documentation: description of each module, logic, and variables.
• Access configuration: connecting to service APIs, creating webhooks.
• Testing: verification with test data, edge case handling.
• Employee training: demonstration of scenario operation, instructions for adding new data.
• Warranty support: 2 weeks after launch to fix any issues.

Contact us to assess automation for your process. Get a consultation — we'll analyze your routine and propose a scenario that saves hours of your team's time. Order setup now — the investment pays off through reduced manual labor.

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.