Automate your workflows
with Make
We build powerful automation workflows with Make (formerly Integromat), connecting your apps, automating repetitive tasks, and syncing data across your entire tech stack. Zero maintenance, infinite scale.
The best tool for
automation / ipaas
Make (formerly Integromat) is the most powerful visual automation platform. It connects 1,000+ apps and lets you build complex multi-step workflows, with filters, routers, iterators, and error handling.
What we build with Make
From MVPs to enterprise platforms, here's how we use Make to ship faster.
Data Sync
Keep your databases, CRMs, and spreadsheets in sync automatically, in real time.
Notifications & Alerts
Slack, email, and SMS notifications triggered by events in your app or database.
Lead & Onboarding Flows
Automate lead capture, welcome emails, CRM updates, and user onboarding sequences.
Reporting & Analytics
Scheduled data exports, automated reports, and dashboard updates, hands-free.
Certified Make experts
We don't just use Make, we master it. Our team is certified and has shipped dozens of projects with it.
Apps delivered
We've shipped over 50 production apps using Make and the broader no-code stack, from seed-stage MVPs to enterprise platforms.
Faster delivery
Make lets us build in weeks what traditional dev teams take months to deliver, giving you a decisive speed advantage.
Fixed pricing
Every project comes with a clear scope, fixed price, and weekly demos. No surprises, no scope creep, just results.
Tools we combine with Make
We integrate Make with the best tools in the no-code ecosystem for end-to-end solutions.
The Complete Guide to Make Development
Make (formerly Integromat) is the most powerful visual automation platform for no-code teams, enabling complex multi-step workflows that connect your entire app stack without writing server-side code.
Make Scenarios vs Zaps: The Key Differences
Make and Zapier both automate tasks by connecting apps, but their underlying models are fundamentally different. A Zap is a linear chain, trigger, then action, then optional action, designed for simplicity. A Make scenario is a flowchart: you can branch with routers, loop with iterators, aggregate data, handle errors in parallel paths, and process multiple records in a single execution. For simple two-step automations, Zapier is faster to set up. For anything with conditional logic, data transformation, or multiple output paths, Make is dramatically more capable. The visual builder is the most immediate difference. Zapier presents automations as a numbered list of steps. Make presents them as a canvas with modules connected by arrows, you can see the data flowing from one module to the next, inspect every field mapping, and understand the entire scenario at a glance. This visual model makes complex scenarios easier to build, debug, and hand off to another developer months later. When a scenario breaks at 2am on a Tuesday, the visual map tells you exactly where it failed and why. The other key difference is execution model. Zapier runs one Zap per trigger event, executing each step sequentially. Make can process arrays of items in parallel using its iterator and aggregator modules, one scenario execution can process a hundred records from a database query, transform each one, and insert them all into another system. This bulk processing capability makes Make the right tool for data migration scenarios, batch notification sends, and any workflow where you need to process collections of items rather than single events.
Data Mapping and Transformation in Make
Data mapping in Make works through a formula system embedded in each module's field configuration. When you connect a trigger to an action, you map output fields from the trigger to input fields in the action. Make's formula editor provides functions for string manipulation (concatenate, trim, replace, extract), date formatting, number rounding, array operations, and conditional logic. The formula system is similar to spreadsheet formulas, once you understand the syntax, it becomes second nature. The most powerful transformation tool in Make is the iterator. An iterator takes an array, a list of rows from a database query, a list of attachments from an email, a list of items from a webhook payload, and processes each item separately through the rest of the scenario. After an iterator, every subsequent module runs once per item in the array. This is how you send a personalised email to each subscriber in a list, process each row of a CSV file, or update each record returned by a database query. Aggregators are the counterpart to iterators: they collect the results of iterated operations back into a single bundle. An array aggregator takes the output of each iterator cycle and builds a new array, useful for constructing a formatted list of results, building a batch API request, or creating a structured data payload. The iterator-aggregator pattern is Make's most powerful feature and the one that most distinguishes it from simpler tools. Understanding when and how to use it is the skill that separates basic Make users from power users who can build sophisticated data pipelines.
Error Handling and Retry Logic
Make provides three tools for managing errors in production scenarios: error handlers, ignore directives, and retry configurations. An error handler is a separate module path that activates when a specific module fails, similar to a try-catch block in code. You connect an error handler to any module and define what happens on failure: send a Slack alert, log the error to a database, try a fallback API, or simply break the scenario and notify you. Without error handlers, a failed module stops the entire scenario silently. The ignore directive tells Make to continue executing the scenario even if a specific module fails. This is appropriate for non-critical steps, if a Slack notification fails, you probably want the rest of the workflow to continue. If a database insert fails, you almost certainly want to stop and investigate. Use ignore directives sparingly and only for genuinely non-critical operations. Every ignored error should still be logged somewhere so you can detect patterns. Make's retry configuration lets you specify how many times a module should retry on failure and the interval between retries. This is particularly important for HTTP modules calling external APIs that occasionally return 429 (rate limited) or 503 (service unavailable) responses. Configure two to three retries with exponential backoff for any module calling a third-party API. Also configure Make's scenario-level incomplete execution handling: when a scenario fails partway through, Make can store the incomplete execution for manual review and reprocessing rather than dropping the data entirely. Enable this for any scenario processing user-submitted data or financial transactions.
Webhooks as Triggers and Actions
Webhooks are the fastest and most reliable way to trigger Make scenarios. Instead of polling an API on a schedule, a webhook fires your scenario instantly when an event occurs, a new form submission, a Stripe payment, a new row in Supabase, a status change in your custom app. Make provides a unique webhook URL for each scenario; you paste this URL into the source application's webhook configuration, and Make begins receiving events immediately. Make also supports outbound webhooks, sending HTTP POST requests to external services as a scenario action. This is how you trigger events in systems that do not have a native Make module: your custom backend, a legacy API, an internal microservice, or a third-party tool with a webhook-receiving endpoint. The HTTP module in Make lets you configure the full request: URL, method, headers, body format (JSON, form data, raw), and authentication. It returns the response body and status code as variables available to subsequent modules. The instant webhook trigger versus the scheduled trigger is one of the most important architectural decisions when building Make scenarios for a SaaS product. Scheduled triggers (every 15 minutes, every hour) introduce latency, a user signs up and waits up to 15 minutes for their welcome email. Instant webhook triggers fire the moment the event occurs, delivering a near-real-time experience. Always prefer webhook triggers for user-facing automations. Use scheduled triggers for batch operations where timing does not matter: nightly data exports, weekly digest emails, monthly invoice generation.
Connecting Make to Supabase for SaaS Automation
Make connects to Supabase via two mechanisms: the native Supabase module (for basic table operations) and the HTTP module with Supabase's REST API (for complex queries and RLS-aware operations). The native Supabase module covers the most common operations, select rows, insert a row, update a row, delete a row, and requires only your Supabase URL and API key to configure. For most automation use cases, the native module is sufficient. For more complex scenarios, the HTTP module with Supabase's PostgREST API unlocks the full power of the connection. You can pass filter parameters, use range headers for pagination, leverage Supabase's nested resource syntax for joined queries, and send the service role key for admin operations that bypass RLS. This is the approach for scenarios that need to query across multiple tables, process large result sets, or perform operations that the native module does not support. A common SaaS automation stack uses Supabase as the source of truth and Make as the orchestration layer. When a new user signs up (Supabase webhook trigger), Make sends a welcome email via SendGrid, creates a Stripe customer, adds the user to a HubSpot contact list, and posts a notification to a Slack channel, all in one scenario. When a subscription is cancelled (Stripe webhook trigger), Make updates the user's status in Supabase, sends a cancellation confirmation email, and triggers an offboarding sequence in your email platform. This pattern keeps business logic in Make's visual environment, where non-technical founders can read and modify it, rather than buried in backend code.
Make Pricing and How to Minimise Operations Count
Make charges based on the number of operations executed per month. An operation is one module execution, one trigger, one action, one transformer. A scenario with a trigger and three actions costs four operations per execution. A scenario that processes a hundred items through an iterator with three modules per item costs 300 operations plus the trigger. Understanding this model upfront prevents surprise bills when your automation volume scales. The most effective way to minimise operations is to reduce scenario complexity for high-volume automations. Combine multiple transformations into a single Tools module rather than chaining separate formatter modules. Use Make's built-in data store for lookups instead of querying an external database repeatedly. Merge low-volume scenarios that share a trigger into a single scenario with a router, reducing the total trigger operations. For batch workflows, prefer one scenario that processes an array over multiple scenarios each handling a single item. Make's free plan includes 1,000 operations per month and 1,000 webhook events, enough for testing and low-volume personal projects. The Core plan at $9/month provides 10,000 operations and is appropriate for early-stage SaaS automations. Professional and Teams plans scale to millions of operations per month with faster minimum intervals and priority execution. Most SaaS products stay comfortably within the Core or Professional tier for the first year. Audit your operation count regularly in the Make dashboard, a runaway scenario iterating over a large dataset can consume your monthly budget in hours if the scenario is misconfigured.
How Make compares
See how Make stacks up against other popular tools.
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