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.

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.

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

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

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.
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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.
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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:

https://docs.google.com/document/d/X/edit

Here, X represents the template ID, which will be needed in later steps.
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In this step, we'll establish two crucial directories on Google Drive, serving distinct purposes in our CV anonymization process:

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".
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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):

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

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.

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.
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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:

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

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.
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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.

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:

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):

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.