How to Build AI Apps with Make.com and Softr

Using make.com together with Softr is a powerful way to build full AI applications without writing code. This how-to guide walks you step by step through designing prompts, connecting data, and automating AI workflows based on the official community challenge process.

The goal is to help you move from a basic idea to a working AI web app that users can access through a simple Softr interface, with make.com handling the automations in the background.

Overview of the Make.com and Softr AI Workflow

Before building, it helps to understand how Softr and make.com work together in an AI app:

Softr provides the web front-end where users submit data, upload files, and view results.
Make.com handles the automation, integrations, prompts, and AI calls.
Databases and APIs connect your content and external tools.

The flow usually looks like this:

User submits a form or action in Softr.
Softr sends data to a make.com webhook or integration.
Make.com processes the input, calls AI models, and enriches the data.
Results are stored, validated, and sent back to Softr or other tools.

Step 1: Define Your AI App Use Case

Start by clearly defining what problem your AI app should solve. The community challenge highlights several strong patterns that work well with Softr and make.com:

Content curation: Summarize, rank, or recommend resources based on user preferences.
Educational tools: Generate learning plans, flashcards, or quizzes from documents.
Business helpers: Draft emails, proposals, or product descriptions based on structured inputs.
Research assistants: Analyze public documents or knowledge bases and present clear answers.

Write a concise one-sentence value proposition, for example: “An AI assistant that turns long reports into simple learning paths.” This will guide your design and prompt writing later.

Step 2: Plan the Softr Front-End and Data Flow

Next, design how users will interact with your app in Softr and how this will trigger scenarios in make.com.

Key Softr Elements

In Softr, plan the following elements:

Input forms: Fields for text, URLs, files, or structured options.
Lists or tables: To show results generated by your AI.
Detail pages: For richer views of each AI-generated output.
Buttons or actions: To trigger updates or re-generation through make.com.

Data Connections for Make.com

Decide where your data lives and how make.com will access it:

Use Airtable, Google Sheets, or a database as your main store.
Connect Softr to this data source for display.
Connect the same source inside make.com so automations can read and write records.

Draw a simple diagram of how information moves from user → Softr → make.com → database → Softr.

Step 3: Create Your First Make.com Scenario

With the front-end planned, you can build your initial automation scenario in make.com.

Core Modules to Use in Make.com

Most AI apps in the challenge rely on a small set of essential modules:

Webhooks or app triggers: Receive data from Softr when a form is submitted or a record changes.
Database modules: Read and update records in Airtable or another data store.
AI modules: Send prompts to AI models and capture responses.
Routers and filters: Implement logic branches and conditions.

Building the Scenario Step by Step

Create a new scenario in make.com and add a trigger module (for example, a webhook receiving data from Softr).
Map incoming fields from Softr to variables inside the scenario.
Add database modules to fetch any reference data you need (for example, user preferences or stored content).
Add an AI module and construct a clear prompt using the mapped input values.
Store the AI response back to your database, linked to the original record.
Optionally, call webhooks or automations back to Softr if you need instant updates.

Step 4: Design Effective AI Prompts in Make.com

Strong prompt design is critical to reliable AI behavior. The challenge shows that good prompts usually share these traits:

Clear role: Tell the AI what it is (for example, “You are a senior product analyst…”).
Explicit structure: Ask for bullet lists, sections, or JSON when needed.
Detailed instructions: Specify tone, length, and what to avoid.
Use of user context: Insert user inputs and database values from make.com.

In a make.com AI module, build your prompt by combining static text with dynamic variables. For example:

Describe the user’s goal from a Softr form field.
Insert constraints from your database (budget, time, level).
Request a consistent, reusable output format.

Step 5: Validate and Post-Process AI Output

AI responses often need checking and cleaning. Make.com scenarios from the community challenge frequently include a validation layer to keep results usable.

Validation Strategies in Make.com

Use these techniques directly in your scenario:

Length checks: Ensure responses stay below a maximum length.
Field completeness: Confirm that all required sections are present.
Fallback logic: If the output is empty or malformed, re-run the AI module with a revised prompt.
Formatting: Convert AI responses into markdown, HTML, or structured fields before saving.

You can use conditional steps, routers, and text functions inside make.com to shape the output before sending it back to Softr or other tools.

Step 6: Connect Make.com Back to Softr

Once your scenario is generating good results, connect it tightly with Softr so users see updates in real time or near real time.

Common Integration Patterns

Database sync: Softr reads from a database that make.com updates. When a record is updated, Softr components reflect the change automatically.
Webhook-based updates: Make.com calls a Softr automation or API endpoint to signal that a result is ready.
Status fields: Store statuses such as “processing”, “ready”, or “failed” so the UI can show progress.

Test the flow end to end: submit from Softr, watch make.com process, and confirm that results appear on the correct pages and lists.

Step 7: Test, Iterate, and Optimize

Real-world usage will reveal edge cases. Use this feedback loop inspired by the challenge builders:

Collect test cases: Try unusual or difficult inputs that your users might submit.
Log important data: Store prompts, outputs, and error messages in your database.
Refine prompts: Adjust instructions in make.com based on recurring issues.
Improve reliability: Add retries, timeouts, and better filters where needed.

Iterate on both your Softr interface and your make.com scenarios until the experience feels smooth and predictable.

Resources and Next Steps

To dive deeper into examples of AI apps built with Softr and make.com, review the original community challenge article at this official Make blog post. It showcases real projects, patterns, and ideas you can adapt.

If you need strategic help designing scalable automations or AI workflows around make.com, you can also explore consulting resources such as Consultevo, which focuses on automation, integration, and AI system design.

By combining Softr’s flexible front-end with the automation power of make.com, you can deliver production-ready AI applications that are maintainable, testable, and easy for non-technical users to access.

Need Help With Make.com?

If you want expert help building, automating, or scaling your Make scenarios, work with ConsultEvo — certified workflow and automation specialists.

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