How to Build an Affiliate-Qualifying AI Agent with Make.com

In this guide, you will learn step by step how to build an affiliate-qualifying AI agent using make.com, combining AI prompts, webhooks, and data enrichment to automatically assess and score affiliate leads.

The process is based on the official tutorial and walks through everything from webhook setup to LLM configuration and final lead output formatting.

Overview of the Make.com Affiliate AI Agent

The goal of this scenario is to qualify affiliate applications automatically. The AI agent analyzes a potential partner’s business, audience, and goals, then produces a concise qualification summary and score you can send to your CRM or internal tools.

At a high level, the automation in make.com will:

• Receive affiliate application data through a webhook.
• Enrich company and contact data via external APIs.
• Call an LLM to analyze and qualify the lead.
• Return structured, CRM-ready results.

This architecture lets you scale affiliate review without losing quality or context.

Prerequisites for Using Make.com in This Scenario

Before you start building the AI agent in make.com, prepare the following components:

• A make.com account with access to scenario building and AI/HTTP modules.
• Access to an LLM provider (such as OpenAI, Google Gemini, or a similar API).
• API access to any enrichment tools you plan to use (for example, company lookup or social data providers).
• A basic understanding of HTTP webhooks and JSON data.

Once these are ready, you can construct a robust, repeatable qualification workflow.

Step 1: Design the Affiliate Data Flow in Make.com

Begin by mapping out how affiliate data will move through your make.com scenario.

1. Identify inputs: Determine what fields you receive from your affiliate application form, such as name, email, website, company name, traffic channels, and main markets.
2. Decide on enrichments: Choose what extra information you want to collect, like estimated traffic volume, industry, or social presence.
3. Define outputs: Plan the final fields your CRM or spreadsheet should receive, such as qualification score, tier, recommended follow-up, and key risks.

Having this flow defined before building saves time later when testing and adjusting the logic in make.com.

Step 2: Create a Webhook Trigger in Make.com

The first functional step is to set up a webhook in make.com that captures new affiliate applications.

1. In your make.com dashboard, create a new scenario.
2. Add the Webhook module as the trigger.
3. Generate a custom webhook URL.
4. Connect your form tool (for example, a landing page or form builder) so that each submission is sent as a POST request to the make.com webhook.

After you send a test submission, let the webhook module in make.com detect the data structure so you can map fields in the next modules.

Step 3: Enrich Affiliate Leads with External Data

To improve qualification quality, enrich the incoming data using HTTP or dedicated enrichment modules in make.com.

1. Add an HTTP or relevant third-party module after the webhook.
2. Pass key identifiers, such as domain or company name, to the enrichment API.
3. Configure authentication and query parameters according to the provider’s documentation.
4. Map the returned fields (for example, industry, employee count, traffic rank) into the scenario variables.

With this additional context, your AI agent in make.com can make far more accurate qualification decisions.

Step 4: Build the AI Prompt and LLM Call in Make.com

Now connect the enriched data to a large language model using a suitable AI or HTTP module in make.com.

1. Add an AI or HTTP module that calls your LLM provider.
2. Construct a clear, role-based prompt that explains the job of the AI agent, for example:

• Describe the ideal affiliate profile.
• Specify what data the model receives.
• Instruct the model to output standardized JSON with fields like score, tier, summary, and recommended action.

3. Map all relevant input fields from the webhook and enrichment modules into the prompt.
4. Configure model parameters (temperature, max tokens, system instructions) for consistent evaluations.

Ensure the prompt requests predictable fields so you can parse the response reliably inside make.com.

Step 5: Parse and Validate the LLM Response

Once the LLM returns a response to make.com, you must validate and structure the data.

1. Use a JSON Parse or similar module to convert the LLM output into discrete fields.
2. Set fallback values for missing or malformed fields, such as a default score or generic summary.
3. Add Router or Filter modules if you want different paths for strong, medium, and low-quality affiliates.

This step ensures your downstream tools always receive clean, reliable data from make.com.

Step 6: Send Qualified Leads to Your Destination

Next, configure where the qualified affiliate data should go from make.com.

• CRM or sales tool: Use native modules or HTTP to send fields like score, rationale, and actions.
• Spreadsheet or database: Store a history of all affiliate evaluations for reporting.
• Notification channels: Notify your partnership team via email or chat when a high-priority affiliate appears.

Be sure to map the LLM’s rating fields consistently, so your internal team quickly understands the meaning of each score.

Step 7: Test, Iterate, and Improve the Make.com Agent

After the basic flow works, refine the AI agent’s performance inside make.com.

1. Test with real applications: Run past affiliate submissions through the scenario and compare results to human decisions.
2. Adjust prompts: Clarify instructions where the model is too strict or too lenient.
3. Refine filters: Tweak score thresholds that trigger manual review or automatic rejection.
4. Optimize enrichment: Add or remove data sources based on their impact on decision quality.

Small iterations in both prompts and filters can significantly increase the overall precision of your make.com affiliate-qualifying system.

Best Practices for AI Affiliate Qualification in Make.com

Prompt Design Tips for Make.com AI Workflows

• Keep role and goals explicit, for example, “You are an affiliate partnership analyst.”
• Provide multiple example profiles of good and bad affiliates.
• Ask for bullet-point reasoning, not just a numeric score.
• Require machine-readable JSON output with strict field names.

Data Handling and Governance in Make.com

• Minimize personal data sent to LLMs where possible.
• Use proper API key management and secure connections.
• Log only what you need for audit and optimization.

These practices maintain security while ensuring the AI in make.com remains transparent and explainable.

Resources and Next Steps

To go deeper into this pattern, review the original tutorial on the official site: How to build an affiliate-qualifying AI agent. It provides a reference blueprint you can adapt for your own partner programs.

If you want strategic help designing scalable automations and AI workflows beyond make.com, you can explore consulting resources such as Consultevo, which focuses on automation architecture and AI-driven operations.

By combining structured data, enrichment APIs, and LLM analysis inside make.com, you can transform affiliate qualification from a tedious manual review into a reliable, scalable, and data-rich AI process.

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.

Get Help