This approach ensures full alignment between your interfaces, internal databases, and HR intelligence tools (such as Recruiter Copilot), enabling better results in Searching and Scoring. By following this guide, you will lay the foundation for a scalable, data-driven architecture fully compatible with the HrFlow.ai ecosystem.

Step 1 – Prerequisite: Client Database Architecture (Form & Columns)
​Step 2 – Historical Data Management: Migration & Enrichment
​Step 3 – New Data Management: User Experience Form & Resume Dropzone

Step 4 – Advanced Integration: Recruiter Copilot (Completeness, Indexing, Archiving, Synchronisation Workflow)

To ensure full compatibility with HrFlow.ai, begin by updating and completing your database schema. Add new columns to support all fields available in the HrFlow.ai profile object.

This step is critical to avoid data loss when handling structured or parsed profiles. A well-aligned schema allows downstream tools and workflows to operate seamlessly.

Update your candidate intake form to reflect the complete schema. Every database column must have a corresponding field in the form. This ensures that data collected from new candidates is fully captured and correctly stored, enabling accurate enrichment and future automation.

Import existing profile data from your internal systems into structured_data_source (Source 1) using HrFlow.ai's Storing API.

Use HrFlow.ai's Parsing API to extract information from profiles resumes. Then Store the parsed output in parsed_data_source (Source 2).

Tip: Use a unique identifier (e.g., profile_id) from Source 1 to associate each structured profile with its corresponding parsed profile.

Enrich the candidate profiles by combining the structured data from Source 1 with the parsed data from Source 2. The structured data should complete and validate the parsed content.

Store the result in merged_data_source (Source 3) using HrFlow.ai's Storing API, which becomes your unified, enriched, and reliable source of truth.

To enhance the candidate experience and streamline data entry, we recommend dividing the submission process into two steps:

Step 1: Allow the user to upload their resume via a dropzone interface. This document is parsed using HrFlow.ai's Parsing API .

⚠️ Sync Parsing mode should be activated for the dropzone source.
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Step 2: Retrieve the parsed data from Step 1 and use it to pre-fill the candidate form. The user can then review, edit, and complete the information. This approach simplifies the process while improving accuracy and reducing input errors.

Once the candidate submits the completed form, the profile is stored according to your business logic. For example, logic may vary based on whether:

This step ensures that all new data is captured, enriched, and seamlessly integrated into your internal systems.

Before storing a profile in our Searching API, it's crucial to ensure its quality and completeness, and that it meets the searching and scoring criteria. The following criteria are used to determine whether a profile should be indexed in the Searching/Scoring engines:

You can read more about this here:

Define clear rules for:

Create production_source , a production-ready source to use with your Recruiter Copilot, this source should be continuously synced from your internal database.

production_source will include:

Set up a scheduled workflow (e.g., a cron job) to push all new or updated profiles from your database into Source 4. This ensures your Recruiter Copilot always works with up-to-date and complete data, and that your internal database remains the single source of truth.

With your data foundation in place, you can now focus on building seamless experiences powered by clean, enriched candidate profiles.

Your system should:

This setup transforms your candidate data into a reliable, scalable asset and unlock the full potential of HrFlow.ai’s Searching and Scoring capabilities.