Metaview’s AI Sourcing Agent helps you find high-fit candidates faster by researching publicly available professional information and surfacing a ranked list of profiles based on your requirements. It reduces the time you spend on manual sourcing — but you remain in full control of who to contact, how to engage them, and every hiring decision that follows. This guide outlines best practices for using AI Sourcing responsibly and effectively.
Metaview AI Sourcing is designed to assist, not replace, your recruiting judgment.
- You define the role requirements — through job descriptions, verbal briefs, or calibration on existing candidates.
- The AI Sourcing Agent researches publicly available candidate information to identify people who may be a good fit.
- Results are returned as a ranked list of candidate profiles that you can re-sort, search, and filter.
- You independently review the results and manually decide who to contact and how to proceed.
Metaview AI Sourcing does not:
- Make hiring decisions or recommendations about who to hire.
- Automatically contact, progress, or reject any candidate.
- Suggest specific actions to take on any candidate.
- Use protected characteristics in ranking candidates.
- Replace your team’s independent evaluation of candidates.
Defining your search criteria
The quality of your sourcing results depends on the clarity of your inputs. Metaview uses the requirements you provide to research and rank candidates, so well-defined criteria lead to better, more relevant results.
Best practice guidelines for search criteria
- Be specific about what matters for the role. Include relevant skills, experience levels, and role-specific requirements rather than broad or vague descriptions.
- Focus on job-related qualifications. Keep your criteria centered on professional attributes like skills, experience, career history, and location relevance.
- Calibrate when possible. Use existing strong candidates or profiles as calibration examples to help the AI understand what “good” looks like for this specific role.
- Iterate and refine. If initial results aren’t quite right, adjust your criteria. The AI learns from your calibration to improve relevance over time.
- Avoid criteria that could introduce bias. Do not include references to protected characteristics such as age, gender, ethnicity, disability, or other non-job-related personal attributes in your search requirements.
If your initial results feel too broad, narrow your criteria by adding required skills, seniority bands, or industry context. If they feel too narrow, remove overly restrictive requirements and recalibrate with a few example profiles.
Reviewing and acting on results
Every list of candidates Metaview surfaces is a starting point for your review — not a final answer. The ranking reflects how closely each candidate’s publicly available profile matches the requirements you’ve set, but your judgment is what determines who moves forward.
When reviewing your results, you can:
- Evaluate candidates independently. Don’t rely solely on ranking position. Review individual profiles to assess fit based on the full context of the role.
- Use the results as one input among several. Combine AI Sourcing results with other sourcing channels, referrals, inbound applications, and your own research.
- Consider candidates the AI may have ranked lower. Rankings reflect pattern matching against your criteria — they may not capture every dimension of what makes a great hire.
- Document your reasoning. When you decide to progress or pass on a candidate, note why. This supports fair, consistent decision-making across your team.
Rankings indicate relevance to your stated criteria — not overall candidate quality. Always assess candidates against the full scope of role requirements and team needs.
- Link to your organization’s privacy policy. This lets candidates know how their data is being used and what their rights are.
- Include an unsubscribe or opt-out mechanism. Give candidates a simple way to tell you they don’t want to be contacted, and honor those requests promptly.
Following these practices helps ensure transparency and supports compliance with applicable data protection laws.
What data AI Sourcing uses
Metaview AI Sourcing researches publicly available professional information to identify and rank candidates. This includes information such as career history, skills, professional experience, and location.
Metaview processes this data on your behalf based on the requirements you define. The data is not retained by Metaview for its own independent purposes.
For the ranking of candidates, Metaview applies its proprietary analysis to evaluate and order profiles against your criteria. You define what you’re looking for; Metaview’s AI determines how to evaluate relevance. This means Metaview and your organization share responsibility for this part of the process — your organization as the entity directing the sourcing, and Metaview as the entity applying the ranking logic.
All activities after the ranked list is delivered — reviewing candidates, reaching out, and making hiring decisions — are entirely within your organization’s control.
Privacy and legal considerations
Using AI to source candidates is an established practice across the recruiting industry. The legal basis for sourcing and contacting passive candidates whose professional information is publicly available is typically legitimate interest under data protection frameworks like GDPR. This is consistent with how sourcing tools across the industry operate.
Privacy and AI regulations vary by jurisdiction. Your organization should seek independent legal advice for your specific jurisdictions, regulatory obligations, and use cases.
You’re always in control
Metaview AI Sourcing is designed to make your sourcing faster and more effective while keeping you firmly in control. The AI researches and ranks — you decide and act. No candidate is contacted, progressed, or rejected without a human making that choice.
By combining AI-powered research with your professional judgment and responsible outreach practices, you can build stronger pipelines while ensuring that every step of the process is fair, transparent, and human-led. 
