A placement record can teach more than a résumé—but only if its outcome and rights are clear.

Start here

Does any of this sound familiar?

• A job order, submission, employer decision, and confirmed placement can be linked.
• Recruiters record candidate fit, client feedback, and placement status.
• You want to distinguish historical records from AI evaluation tasks.

A recruiting episode connects role requirements, recruiter judgment, employer response, and outcome. It may help assess an AI recruiting task, but does not make identifiable candidate files ready to export.

Start with a no-export inventory. Historical records show what happened; an evaluation task asks an expert to solve a defined problem against a rubric. They require different rights and safeguards.

The problem

Why a placement result is not self-explanatory

An ATS status called “placed” might mean an accepted offer, start date, or outdated value after a withdrawal. Notes may contain subjective impressions, sensitive details, client feedback, or copied profile content. A positive result does not prove fair selection, permission for new use, or ownership.

Without the role version, decision date, field source, and outcome definition, a model may learn stale criteria. A synthetic task with an expert rubric can test evaluation more safely, but is not historical placement data.

A useful outcome is a traceable decision and result, not a status label detached from its history.

The solution

Build a rights-aware placement record before discussing use

Begin with a field inventory, not candidate records. Check whether examples are necessary, accurate, permitted, and interpretable.

DataSupply partners only with labs that meet its top 0.01% credibility standard. We help assess whether a qualified buyer may be a fit and negotiate terms that reflect the data's potential value, including exclusivity where relevant. We also help you work through diligence questions about rights, privacy, security, and compliance, then present a high-level inventory of permitted records, not the dataset. Fit is specific to each situation; no buyer or value is guaranteed.

What to inventory before any buyer conversation

• Define the recruiting episode List role criteria version, submission and interview dates, recruiter action, employer decision, placement confirmation, and follow-up status. Record the source and distinguish verified results from stale or unknown statuses.
• Trace each right and restriction Mark whether fields came from an employee, candidate, client, or third party. Review notices, client and vendor terms, retention, and applicable law for the proposed use. Keep raw records out of exploratory conversations.
• Separate corpus from evaluation work For historical records, document provenance, permitted population, exclusions, outcome definition, and known gaps. For expert tasks, define scenario, expected answer, rubric, evaluator, and rights. Ask a recipient which it wants and why.

Set the boundaries before discussing access.

Counsel and security should review candidate and client permissions, discrimination risk, notice, retention, de-identification, access, deletion, derivatives, and audit rights. Define purpose and prohibited reuse in a contract; an NDA does not grant data rights.

What could make a permitted example useful?

A clear trail may be easier to review than a large folder. Demand, rights, preparation cost, and terms are separate; none guarantees a transaction.

A practical first step.

Map one placement from requisition to verified result without exporting records. Identify data owners and missing permissions.

datasupply.ai can discuss possible fit and buyer questions without receiving your dataset. You decide whether to pursue any introduction. No buyer, license, or payment is guaranteed.

Documented example / what it proves

Public evidence supports scrutiny of selection systems, not a data sale

The U.S. Equal Employment Opportunity Commission’s resource “What is the EEOC’s role in AI?” identifies recruiting, screening, and hiring as uses of AI and notes that a seemingly neutral employment practice with unjustifiable disparate impact may be unlawful. It points readers to separate technical assistance on assessing adverse impact in selection procedures. Read U.S. Equal Employment Opportunity Commission.

For a staffing firm, preserve job criteria and employer decision context rather than treating a model score as self-validating. An evaluation task needs its own defined answer and review method; it differs from historical candidate and client records.

The important limit: The guidance addresses selection and discrimination, not a data license, buyer demand, a completed transaction, or permission to reuse records.