Make
intermediateVisual automation that makes complex workflows intuitive.
Make (formerly Integromat) is a visual automation platform known for its intuitive drag-and-drop workflow builder and powerful data transformation capabilities. With Grid AI for intelligent orchestration, Make handles complex branching logic, iterators, aggregators, and data mapping that would require code in other platforms. It strikes a balance between Zapier's simplicity and n8n's power, offering a visual builder that can handle sophisticated workflows. PxlPeak deploys Make for businesses that need visual, maintainable automation with AI capabilities built in.
1,800+
App integrations
500K+
Organizations using Make
60%
Cost savings vs. manual processes
Key Features
Intuitive visual workflow builder with drag-and-drop design
Grid AI for intelligent workflow orchestration and optimization
Advanced data transformation with iterators, aggregators, and routers
1,800+ app integrations with deep data mapping
Scenario scheduling, error handling, and execution history
Custom app development with Make's app framework
Use Cases We Implement
Build data transformation pipelines across business systems
Create visual, maintainable marketing automation workflows
Orchestrate multi-step order processing and fulfillment
Automate reporting by pulling and transforming data from multiple sources
How We Implement Make
Assess
We analyze your business needs and how Make fits into your workflow.
Configure
Set up Make with custom settings, integrations, and data connections.
Integrate
Connect to your existing tools: CRM, helpdesk, email, and more.
Train & Launch
Train your team, document everything, and provide ongoing support.
When NOT to Use Make
Simple two-app integrations that Zapier handles natively
Teams without technical members: Make's power comes with a learning curve
High-volume webhook processing (Make's operation limits add up fast)
Workflows requiring custom code execution, n8n is better for that
Integration Patterns We Deploy
Data transformation hub
Make + Google Sheets + CRM + ERP
Pull data from multiple sources, transform with Make's iterators and aggregators, push clean data into your CRM or ERP. Make's data manipulation modules are its killer feature.
AI-enhanced visual workflows
Make + OpenAI + Airtable + Slack
Visual automation with AI modules for content generation, classification, or extraction, all manageable by non-developers through Make's drag-and-drop builder.
Multi-step order processing
Make + Shopify + shipping API + email
Complex order fulfillment with conditional logic (split shipments, custom handling, international routing), scenarios that need routers and iterators.
Cross-platform reporting
Make + Google Analytics + HubSpot + Data Studio
Aggregate reporting data from multiple platforms, transform and normalize it, then push to a unified dashboard or data warehouse.
Risks & How We Mitigate Them
Operation limit overruns spiking costs
Audit operation consumption per scenario. Use filters early in flows to skip unnecessary processing. Set up budget alerts and operation caps.
Complex scenarios becoming unmaintainable
Document every scenario with notes. Use folder organization by department. Break mega-scenarios into smaller linked ones with webhooks.
Data Store hitting storage limits
Implement data retention policies. Use Data Store for state tracking only, not as a database replacement. Purge stale records with scheduled cleanup scenarios.
Third-party API changes breaking scenarios
Monitor execution history daily. Set up error notification emails. Use Make's error handler routes for graceful degradation on API failures.
Implementation Checklist
Audit current manual workflows and prioritize by ROI potential
Set up Make Team plan with proper workspace organization
Design scenarios as flowcharts before building in the visual editor
Configure OAuth connections and API credentials for all target apps
Build core scenarios with routers for conditional logic paths
Add error handler routes on every critical module
Optimize operation usage with filters and aggregators
Set up execution monitoring and failure notification emails
Document scenarios and create runbooks for common issues
Review operation consumption weekly and adjust schedules
Implementation Guide: Make
1-3 weeksMake (formerly Integromat) is the visual automation platform that sits between Zapier's simplicity and n8n's power. The drag-and-drop scenario builder handles complex branching, loops, and error handling that Zapier struggles with, at roughly 60% of the cost. If your automations need conditional logic and you don't want to self-host, Make is the sweet spot.
Before You Start
Make Team plan or higher (free tier limits to 1,000 ops/month)
API credentials for all connected services
Workflow diagrams for complex multi-step processes
Understanding of your data formats (JSON, CSV, XML) across tools
Step-by-Step
Design scenarios visually
1-2 daysMap out your automations as flowcharts before building. Make's visual builder is powerful but complex scenarios need planning.
Make's visual approach means you can literally see where data flows break. Use this to debug before it becomes a problem.
Set up workspace and connections
1 dayCreate team workspace, add OAuth connections for each app, organize scenarios into folders by department or function.
Build core scenarios
3-5 daysStart with 3-5 priority automations. Use Make's modules, routers, and iterators to handle complex logic.
Add error handling routes
1-2 daysConfigure error handlers for each module. Make's error handling is much more granular than Zapier: use it.
Make lets you handle errors per-module. Set up resume routes for transient failures and break routes for permanent ones.
Optimize operations usage
1 dayMake charges per operation. Use aggregators to batch API calls and filters to skip unnecessary processing.
Test with real data
1-2 daysRun scenarios with production data in test mode. Verify data transformations, API responses, and error handling.
Deploy and schedule
1 daySet scenario schedules, enable monitoring, and configure notification emails for failures.
Common Mistakes to Avoid
Not using routers for conditional logic
Make's Router module is incredibly powerful. Use it instead of building multiple scenarios for different conditions, it keeps everything in one place.
Ignoring operation limits
Each module execution is an operation. A scenario with 10 modules running on every webhook burns operations fast. Use filters early in the flow.
Building without error routes
Make's error handling is its secret weapon. Every production scenario should have error routes on critical modules. Silent failures are expensive.
Pro Tips
Make's Data Store is like a mini-database. Use it for tracking state between scenario runs, no external database needed.
Use the HTTP module for APIs without native integration. Make has fewer native integrations than Zapier, but the HTTP module covers the gap.
Schedule scenarios during off-peak hours when possible. Make's fair-use policy can throttle high-frequency scenarios.
Export scenarios as JSON blueprints and version-control them. Makes it easy to replicate setups across clients or environments.
Architecture
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Triggers │────▶│ Make │────▶│ Actions │
│ (Webhooks / │ │ Scenario │ │ (APIs / │
│ Schedules) │ │ Engine │ │ Services) │
└──────────────┘ └──────┬───────┘ └──────────────┘
│
┌────────────────┼────────────────┐
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Router │ │ Iterator │ │ Aggregator │
│ (Branching) │ │ (Loop) │ │ (Combine) │
└──────┬───────┘ └──────┬───────┘ └──────┬───────┘
│ │ │
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ CRM / ERP │ │ AI / LLM │ │ Data Store │
│ (HubSpot, │ │ (OpenAI, │ │ (State │
│ Shopify) │ │ Claude) │ │ Tracking) │
└──────────────┘ └──────────────┘ └──────────────┘Quick-Start Config
// Make Scenario Blueprint (JSON export format)
{
"name": "AI Lead Enrichment Pipeline",
"flow": [
{
"id": 1,
"module": "gateway:CustomWebHook",
"parameters": { "hook": "new-lead-webhook" }
},
{
"id": 2,
"module": "openai:CreateCompletion",
"parameters": {
"model": "gpt-5.6-sol",
"prompt": "Research this company and provide: industry, size, key products, and potential needs for AI services. Company: {{1.company_name}}, Website: {{1.website}}"
}
},
{
"id": 3,
"module": "builtin:BasicRouter",
"routes": [
{ "label": "High Value", "filter": "{{2.score}} >= 8" },
{ "label": "Medium Value", "filter": "{{2.score}} >= 5" },
{ "label": "Low Value" }
]
},
{
"id": 4,
"module": "hubspot:CreateContact",
"parameters": {
"email": "{{1.email}}",
"properties": {
"lead_score": "{{2.score}}",
"ai_enrichment": "{{2.analysis}}"
}
}
}
]
}Integration Recipes
AI-Powered Lead Enrichment
New lead webhook triggers Make scenario. OpenAI module researches the company and scores fit. Router branches by score: high-value leads get instant CRM entry with enrichment + Slack alert, medium leads enter nurture sequence, low leads get basic logging.
Multi-Source Reporting Dashboard
Scheduled weekly scenario pulls metrics from GA, HubSpot, and ad platforms. Iterator processes each data source, aggregator combines into unified format, then pushes to Google Sheets dashboard and posts summary to Slack #marketing channel.
// Make scenario scheduling config
{
"scheduling": {
"type": "interval",
"interval": 604800,
"start": "2026-01-06T09:00:00Z"
},
"sequential": true,
"maxErrors": 3,
"dataStore": "weekly_metrics_cache"
}E-Commerce Order Orchestration
New Shopify order triggers complex fulfillment logic: router checks item type (digital vs physical, domestic vs international), iterator handles multi-item orders, aggregator creates shipping labels in batch. Error handlers alert ops team in Slack for manual intervention on failures.
Want us to handle the implementation?
Our team handles Make setup, integration, training, and ongoing support.
Get Make ImplementedFrequently Asked Questions
How does Make compare to Zapier?
Make's visual builder handles complex branching and data transformation more elegantly than Zapier. Zapier has 4x more integrations and is simpler for basic automations. PxlPeak recommends Make for data-heavy workflows and Zapier for broad integration needs.
What is Grid AI?
Grid AI is Make's intelligent orchestration layer that helps build, optimize, and troubleshoot workflows. It suggests improvements, identifies bottlenecks, and can generate workflow logic from natural language descriptions.
Can Make handle complex data transformations?
Yes. Make excels at data manipulation with built-in functions for JSON, XML, arrays, dates, and mathematical operations. Its iterator and aggregator modules handle complex data structures that other platforms struggle with.
How long does implementation take?
PxlPeak designs and deploys Make automation in 1-3 weeks depending on complexity. Simple integrations take 1 week, complex multi-scenario systems with AI take 2-3 weeks.
Related Reading
Make in Action
Composite Model: How an Ecommerce Ops Team Reclaims 87 Hours/Month With n8n
Composite Model: Cutting Med-Spa Missed-Appointment Revenue Loss With an AI Voice Receptionist
How PxlPeak Scaled Operations with AI Automation
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