AI Recruiting Agent with Make.com

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How to Build an AI Recruiting Agent in Make.com

Using make.com, you can build a powerful AI recruiting agent that automates candidate sourcing, CV analysis, and personalized outreach, all without writing a single line of code. This guide walks you through the exact steps to recreate the automation shown in the official how-to, so you can streamline your hiring process end-to-end.

The automation uses tools like LinkedIn, Notion, and OpenAI to evaluate candidates, summarize experience, and contact people that match your ideal profile. You will learn how to connect these tools inside make.com to create a repeatable recruiting workflow.

What You Will Build with Make.com

The AI recruiting agent you will create in make.com follows a clear hiring journey from job description to qualified candidate list.

At a high level, your scenario will:

  • Capture a job description and desired profile.
  • Fetch or receive candidate CVs and profiles.
  • Use AI to evaluate skills and experience.
  • Score and filter the best candidates.
  • Generate summaries and outreach messages.
  • Store structured results for the recruiter.

All these steps are automated with modules inside make.com, letting you run the workflow on demand or on a schedule.

Prerequisites for the Make.com AI Recruiting Flow

Before you start, prepare the following accounts and resources to plug into make.com:

  • A make.com account with access to the scenario editor.
  • An OpenAI (or compatible) account and API key for language and reasoning tasks.
  • A Notion workspace or similar database tool to store candidate data.
  • LinkedIn or other talent source access (profiles, exported lists, or webhook feed).
  • Your job description and ideal candidate profile in text format.

Once these are ready, you can assemble the modules in make.com to create a full recruiting agent.

Step 1: Define the Recruiting Workflow in Make.com

Begin by mapping the core recruiting flow you want to automate inside make.com. Think through each stage from input to output.

  1. Collect job description and role requirements
    Decide where the recruiter will enter the job description. Options include:

    • A Notion database entry.
    • A Google Sheet row.
    • A web form connected to make.com.

  2. Identify your candidate sources
    Choose how candidate data will enter make.com:

    • CSV export of LinkedIn profiles.
    • Webhook from an ATS.
    • Manual uploads or entries into a database.

  3. Define outputs
    Determine how you want the results from make.com delivered:

    • Ranked table of candidates in Notion.
    • Email or Slack digest to the recruiter.
    • Ready-to-send outreach messages.

Having this blueprint will help you structure your modules correctly.

Step 2: Create a New Scenario in Make.com

Now it is time to configure the scenario in make.com that will act as your AI recruiting agent.

  1. Create a scenario
    In your make.com dashboard, create a new scenario and choose a trigger module that suits your process, such as:

    • “Watch database” for Notion when a new job record is created.
    • “Watch rows” for Google Sheets when a new row is added.
    • A custom webhook if you have an external form.

  2. Add data sources
    Connect modules that will supply candidate data to make.com, for example:

    • Import from Google Sheets or CSV.
    • Read from a Notion database of candidates.
    • Receive data via webhook from your ATS.

  3. Organize the scenario flow
    Place the job description input modules at the start of the scenario and connect them to the candidate source modules. This ensures every candidate is evaluated in the context of the same role description.

Step 3: Add AI Evaluation with OpenAI in Make.com

The core of the AI recruiting agent in make.com is the evaluation and reasoning powered by OpenAI or a similar provider.

  1. Connect OpenAI
    In make.com, add the OpenAI (or generic HTTP) module. Configure it with your API key and select an appropriate large language model.

  2. Design your prompt
    Use the job description and candidate data fields as input. A typical prompt structure includes:

    • Job title and responsibilities.
    • Required skills and experience level.
    • Candidate CV or profile text.
    • Clear instructions to return a structured JSON response.

    For example, instruct the model to output:

    • A suitability score from 0 to 100.
    • A short explanation of strengths and gaps.
    • Key extracted skills and seniority level.

  3. Parse the AI response
    Use the JSON or text parser in make.com to map the AI output to fields like:

    • score
    • summary
    • recommended_level
    • top_skills

This structured output allows you to sort, filter, and route candidates automatically.

Step 4: Store and Rank Candidates with Make.com

Next, configure make.com to keep all candidate evaluations organized in a central, searchable space.

  1. Create or connect a database
    Most users follow the reference implementation by using Notion. In make.com, connect a Notion module and map fields such as:

    • Candidate name.
    • Contact info.
    • Source (LinkedIn, ATS, manual).
    • AI score and summary.
    • Skills and seniority.

  2. Write entries per candidate
    For each candidate processed in the scenario, add or update a record in your Notion database (or your chosen tool). This builds a live ranking table the recruiter can filter and sort.

  3. Filter strong candidates
    Use routers and filters in make.com to:

    • Flag candidates with scores above a threshold.
    • Send only top candidates to the outreach branch.
    • Route weak matches to a different follow-up list if desired.

Step 5: Generate Outreach Messages in Make.com

Once you have identified qualified candidates, make.com can help you draft tailored outreach messages using AI.

  1. Use AI for personalized messaging
    Add another OpenAI (or similar) module. Provide:

    • The candidate name and role.
    • The job description.
    • Key strengths from the previous AI summary.

    Ask the model to generate a concise, friendly outreach message suitable for LinkedIn or email.

  2. Store or send messages
    Depending on your process, configure make.com to:

    • Write the message into your Notion or sheet as a draft.
    • Send it via email using an email module.
    • Prepare it for manual copy-paste into LinkedIn.

  3. Track outreach status
    Add fields such as outreach_status and date_contacted in your database. Update these fields from the scenario to keep your pipeline visible.

Step 6: Run, Test, and Iterate in Make.com

With all parts assembled, you can refine your AI recruiting agent in make.com to improve accuracy and efficiency.

  1. Test with a small batch
    Run the scenario in manual mode on a limited list of candidates. Review:

    • Scoring consistency.
    • Quality of AI summaries.
    • Relevance of outreach messages.

  2. Tune prompts and thresholds
    If needed, adjust:

    • Scoring criteria in your AI prompts.
    • Minimum score for outreach.
    • Message tone, length, or language.

  3. Automate scheduling
    Once satisfied, schedule the scenario in make.com to run:

    • On a specific interval.
    • Whenever new candidates appear in your source.
    • On demand when a new job is added.

Using the Official Make.com AI Recruiting Guide

The official reference implementation shows concrete module configurations, prompt examples, and screenshots. You can follow it step by step to ensure your setup matches a proven pattern.

Access the original how-to guide at this make.com AI recruiting agent tutorial. Compare each stage with your own scenario and adapt it to your tech stack and hiring volume.

Next Steps and Optimization Resources

Once your AI recruiting agent in make.com is working, you can expand it with additional automations, such as:

  • Sending candidate summaries to hiring managers via Slack.
  • Creating interview tasks in a project management tool.
  • Adding follow-up reminders for unanswered outreach.

If you need help designing complex automations, integrating additional tools, or optimizing prompts, you can get expert assistance from automation and AI workflow specialists experienced with scalable make.com solutions.

By combining structured data, AI reasoning, and clear workflows, make.com lets you turn a manual recruiting process into a repeatable, data-driven system that saves time and surfaces the best candidates faster.

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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