A recruiter workspace built around evidence and review.
See how a candidate CSV becomes a focused review workspace, with a role brief, supporting evidence, questions to validate, and an outreach queue the recruiter controls.
Start with records you already have.
A recruitment database can contain useful records from earlier searches. The difficult work is often revisiting those records against a new role, finding the supporting detail, and keeping track of what still needs to be checked.
This build explores that workflow in a local application. It brings the role brief, candidate records, review evidence, and outreach preparation into one screen. The video demonstrates the workspace and explains how to build it with React and Next.js.
What this example establishes
This is a prototype walkthrough. It does not include a measured client result, a live agency rollout, or a demonstrated ATS integration. The tutorial recommends synthetic records for development and testing.
A small workflow with explicit decisions.
Define the role brief
The recruiter enters the role title and requirements before generating the review list. Keeping that brief visible makes it possible to inspect what the system is looking for and change it when the role changes.
Import structured records
The tutorial uses a CSV template with consistent candidate fields, including experience, skills, achievements, and contact history. Import validation should identify incomplete rows so missing information is visible before it affects review.
Show evidence and unanswered questions
A local rule-based matcher compares explicit requirements with the structured records. Supporting evidence and missing evidence are kept separately. A missing detail becomes a question to validate rather than an invented qualification.
Let the recruiter review
The workspace puts the role brief on the left, a review list in the middle, and evidence and candidate details on the right. The recruiter can inspect the source detail and approve or remove an entry from the outreach queue.
Prepare an outreach draft
The review step leads to a draft based on the role and a relevant achievement. Copy, open-email, and contact-status actions support the recruiter’s next step. The demonstrated workflow does not send email automatically.
The tutorial calls its comparison an evidence-coverage score. It is not a hiring decision. Missing, stale, or ambiguous records still need human verification, and the person using the tool remains responsible for reviewing the evidence.
Keep a useful workflow when AI is unavailable.
The local matcher provides the initial workflow without an API dependency. The video then describes an optional AI layer for more flexible comparisons and clearer summaries of an already selected set of records.
That layer requests structured output, validates returned candidate identifiers, and falls back to the local matcher when the API is unavailable. A clear notice tells the operator which mode produced the result. The API key belongs on the server, and only the information needed for the task should be sent.
The tutorial explicitly excludes inferring protected characteristics or making hire/reject recommendations. Its useful design lesson is narrower: organize job-related evidence, expose gaps, and preserve a reviewable path through the work.
What changes before an agency rollout?
The single-device prototype uses browser storage for the brief, records, approvals, and contact states. A shared agency application introduces additional requirements. The video identifies these as future production work; they are not features established by the local demo.
| Area | Prototype approach | Production work to scope |
|---|---|---|
| Records | CSV import and browser storage | Encrypted database storage and a controlled migration path |
| Access | One local workspace | Secure login, user permissions, and tenant separation |
| Record lifecycle | Local saved state | Retention controls and audit logs |
| Source systems | Manual record export | Assess ATS API access, field ownership, and synchronization |
| Outreach | Reviewed drafts and manual actions | Define approval, delivery, contact-state, and failure-handling rules |
The first scoping conversation should establish who uses the workspace, where records come from, which actions require approval, and who owns corrections. That determines whether a small internal tool or a deeper system integration is appropriate.
Test the whole path, including mistakes.
The video’s validation checklist covers more than a successful model response. These are checks to run when implementing the workflow:
- A missing requirement produces a question to validate.
- Negative wording does not become positive evidence.
- The CSV template imports, and incomplete records are handled clearly.
- Approval and contact states survive a reload.
- An unavailable AI service produces a visible fallback.
- A draft stays unsent until the recruiter takes the relevant action.
- Exports, the production build, and the mobile interface work as expected.
For a real rollout, agree on a baseline using reviewed examples and observed work. Review time, correction rate, unresolved evidence gaps, and successful completion of approved actions are useful measures to define. This walkthrough does not report measured improvements in those outcomes.
Go to the relevant part of the build.
Each chapter opens the original video at the indicated point.