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CV Anonymization with Make.com and HrFlow.ai
CV Anonymization with Make.com and HrFlow.ai

Explore the transformative synergy between HrFlow.ai's CV parsing API and Make's automation scenarios in revolutionizing recruitment.

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Written by Wafaa Kahoui
Updated over a week ago

Introduction

In the ever-evolving landscape of recruitment, precision and efficiency are paramount. Leveraging the capabilities of HrFlow.ai's CV parsing API with the seamless automation prowess of Make's scenarios, a new frontier of possibilities has emerged. This article unravels the synergy between HrFlow.ai and Make, illuminating the transformative journey of CV parsing automation and anonymization. Join us as we explore the innovative alliance reshaping the recruitment process, where powerful parsing meets the simplicity of no-code automation.

Prerequisites

Before delving into the intricacies of harnessing the synergy between HrFlow.ai and Make for automated CV parsing and anonymization, readers need to familiarize themselves with the foundational aspects of this collaborative endeavor. We highly recommend reviewing the documentation titled Introduction to Make.com ETL before proceeding further.

Now, let's walk through how to create an anonymized version of a resume using the HrFlow.ai Parsing module and Make automation tool.

General Idea

A simple way to anonymize resumes is by using Google Docs. To do so, we will start with the following steps:

  1. Create a Template: Submit a resume template on Google Docs.

  2. Prepare designated folders in Google Drive: Set up two folders: one for incoming CVs and another for anonymized ones.

Then, we will implement a Make scenario with the following key steps:

  1. Monitor Google Drive: Observe the designated Google Drive folder for new resume uploads.

  2. Download Resume: Automatically download each new resume from the folder.

  3. Parse with HrFlow.ai: Use HrFlow.ai's Parse a Resume into a Source module to extract data from the resume.

  4. Anonymize Using Google Docs: Fill in the Google Docs template with the parsed data while excluding the information you want to hide (like contact information, age, sec, etc.). This will effectively anonymize the resume.

  5. Save the anonymized resume: Store the anonymized document in the target Google Drive folder.

This method integrates Google Docs, Google Drive, and HrFlow.ai using Make automation tool. It offers an efficient way to anonymize resumes and doesn't require specific directory names.


Setting Up the CV Anonymization Make Scenario

Now that we understand the core components and steps involved in CV anonymization, let's start a step-by-step guide to configuring the Make scenario. This guide will help you to seamlessly implement the described process, bringing together Google Docs, Google Drive, HrFlow.ai, and Make for a robust and efficient CV anonymization workflow. The following steps describe in detail how to set up the Make scenario for resume anonymization.

Step 1: Create an Anonymization Template with Google Docs

Initiate the CV anonymization process using Google Docs. Refer to Google's support answer for essential guidance, and follow these concise steps.

Note:

To effectively use the template, ensure to save its unique ID. When inspecting the template URL, look for the following structure:

Here, X represents the template ID, which will be needed in later steps.

Step 2: Set Up Google Drive Directories

In this step, we'll establish two crucial directories on Google Drive, serving distinct purposes in our CV anonymization process:

  1. Incoming Resumes Directory

    1. Create a directory dedicated to the entry of new resume files.

    2. This folder will be monitored for new uploads during the CV anonymization process.

  2. Anonymized Resumes Storage Directory

    1. Create a separate directory designated for storing the anonymized versions of the resumes.

    2. The final, formatted CVs will be saved in this folder after processing.

Ensuring the proper setup of these directories is pivotal for the seamless operation of the CV anonymization Make scenario.

Example:

Incoming Resumes Directory - "source," Anonymized Resumes Storage Directory - "target".

Step 3: Create the Make Scenario

In this step, we will craft the Make scenario to orchestrate the CV anonymization process. To guide you through this, refer to the detailed tutorial available in our developers' guide Introduction to Make.com ETL. Follow the instructions to set up the Make scenario, incorporating the key elements discussed in the CV anonymization process.

Remember, the Make scenario is the backbone of this workflow, seamlessly integrating Google Docs, Google Drive, HrFlow.ai, and Make for a streamlined CV anonymization approach.

Scenario Steps (Make module names):

  1. Google Drive - Watch files in a Folder

  2. Google Drive - Download a File

  3. HrFlow.ai - Parse a Resume into a Source

  4. Google Docs - Create a Document from a Template

These steps, as visualized in the General Idea section, play a pivotal role in automating the CV anonymization workflow.

Step 4: Fill Module Parameters in the Make Scenario

In this step, we will dive into the intricate details of the Make scenario by populating the necessary parameters for each module. This crucial phase ensures a seamless flow of data and actions, aligning with the CV anonymization process. Follow along as we guide you through the configuration of each module, shaping the scenario to meet the specific requirements of your recruitment workflow.

Disclaimer:

Before delving into the configuration of each module, it's essential to acknowledge that the parameters, especially those in the trigger module, are often subjective and dependent on individual preferences. As you proceed, consider adapting these parameters to align with the specific requirements and nuances of your unique recruitment workflow. This flexibility ensures optimal performance tailored to your preferences.

Now, let's proceed to populate the parameters for each module in the Make scenario.

Step 4.1: Google Drive - Watch files in a Folder

Step 4.2: Google Drive - Download a File

Note:

For the File ID parameter, it is essential to map the File ID obtained from the execution result of Step 4.1. This mapping ensures the seamless flow of data between steps, maintaining accuracy and continuity in the CV anonymization process. Refer to the screenshot for visual guidance.

Step 4.3: HrFlow.ai - Parse a Resume into a Source

Note:

Before configuring the third step of the Make scenario, ensure that an appropriate HrFlow.ai Source has been created. For guidance on creating a HrFlow.ai Source, refer to the developers’ guide Create, Configure a Source.

Disclaimer:

All keys and sensitive information visually present in the provided screenshots are for illustrative purposes only and are mock representations.

Step 4.4: Google Docs - Create a Document from a Template

Note:

In the context of template variables, each variable is expressed as {{variable_name}}. It's crucial to be aware that when parsing a CV, the number of experiences or educations varies depending on the CV itself. The template, however, is static, meaning that the number of variables defined in the template is fixed. While there are potential workarounds, for simplicity, let's keep the template static. This ensures a straightforward implementation while accommodating variations in CV structures.

Advanced Note:

While the template is inherently static, there is an advanced workaround idea that involves creating a template page for each experience or education. By iterating through the experiences (or educations) array using Make, you can dynamically fill these individual templates. Subsequently, all these pages can be concatenated to generate the final version of the anonymization. This method introduces a level of dynamism to the process, allowing for a more nuanced representation of varied CV structures. However, keep in mind that this approach may add complexity to the implementation, and it's crucial to carefully assess its suitability for your specific use case.

The Google Docs: Create a Document from a Template module plays a pivotal role in the CV anonymization process. Here are the essential details for its configuration:

  1. Document ID (Template ID)

    1. This corresponds to the unique ID of the template created in the initial steps.

  2. Values (Variable Names and Specified Values)

    1. Fill in the values corresponding to the variable names in the template. These variable names are expressed as {{variable_name}} in the template. Ensure accurate mapping and alignment between the template variables and the specified values for effective CV anonymization.

  3. Dynamic Title for Anonymized CV

    1. Assign a dynamic title for the anonymized version of the CV. Consider incorporating variables or specific data to ensure a unique and identifiable title for each processed CV.

Configuring this module meticulously ensures the seamless generation of anonymized CVs with dynamic titles, incorporating the specified values from the parsing process.

Here's a breakdown of the module parameters:

  1. Document ID

    1. The Document ID corresponds to the actual unique ID of the template. In the template URL, it's represented as https://docs.google.com/document/d/X/edit, where X is the ID.

  2. Values Section

    1. The Values section has intentionally been hidden due to the extensive data it contains. For detailed insights into the Values section, refer to the subsequent screenshots, providing a granular view of this critical component.

  3. Title

    1. The Title is dynamically composed using the key of the parsed profile. This simple and effective approach ensures a unique file name for each anonymized CV.

Here are some examples for the Values section:

  1. Direct mapping

  2. Direct mapping with formatting

  3. Extraction of specific fields from a list of objects

  4. Extraction of field at specific index

The provided examples offer a comprehensive view of potential configurations for the Values section in the Google Docs: Create a Document from a Template module. These examples cover direct mapping, formatting, extraction of specific fields from a list of objects, and extraction of a field at a specific index. It's important to note that these examples are sufficient for most cases.

However, it's worth highlighting that Make provides a diverse range of functions and customization options. For a deeper understanding and exploration of these functionalities, we encourage you to refer to the respective section in the developers' guide Introduction to Make.com ETL. This guide offers comprehensive insights into leveraging the full potential of Make for your specific use cases.

Step 5: Test the Scenario

In typical scenarios, automated workflows are set to run at predefined time intervals. The trigger component checks periodically for new files, executing the scenario accordingly. However, for testing purposes, it's valuable to understand how to manually trigger the scenario on a specific file. This manual testing approach allows for a more controlled and immediate assessment of the scenario's functionality. Let's explore how to effectively test your CV anonymization scenario in a controlled environment.

To manually trigger the scenario on a specific file in the designated entry point directory (where resumes are placed), follow these steps:

  1. Right-click on the trigger module.

  2. Select Choose where to start.

  3. Select Choose manually.

  4. Select the desired file from the directory.

  5. Click the Run Once bottom left button.

This process allows for on-demand testing of the scenario, enabling a more controlled assessment of its performance with specific files.

For this test, the NICO DURANT, publicly available, CV was used.

This is the first page of the template (before the anonymization):

This is the first page of the anonymized CV (after the anonymization):

Conclusion

In conclusion, the integration of Google Docs, Google Drive, HrFlow.ai, and Make presents a powerful solution for CV anonymization. This article has provided a step-by-step guide, from setting up directories to creating a Make scenario and populating template variables. By embracing the dynamic functionalities of Make, users can automate and streamline the CV anonymization process effectively.

Remember, while the provided examples offer a robust foundation, the versatility of Make allows for further customization. For advanced configurations and a deeper dive into Make's capabilities, refer to the comprehensive developers' guide Introduction to Make.com ETL. The manual testing approach shared ensures a thorough evaluation of the scenario's functionality.

By following this guide, users can not only enhance their recruitment workflows but also prioritize privacy and efficiency in handling sensitive candidate information. Elevate your CV anonymization process with the seamless integration of Make and associated tools.

References

  1. Introduction to Make.com ETL. Available at: https://developers.hrflow.ai/docs/etl-make

  2. Parse a Resume into a Source. Available at: https://developers.hrflow.ai/docs/resume-parsing

  3. Google's support answer. Available at: https://support.google.com/docs/answer/148833

  4. Create, Configure a Source. Available at: https://developers.hrflow.ai/docs/connectors-source

  5. Google Docs: Create a Document from a Template. Available at: https://www.make.com/en/help/app/google-docs#create-a-document-from-a-template-968237

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