Frequently Asked Questions

An AI interview is a structured interview run by software instead of a live recruiter at the first stage. With IntervAI, candidates answer role-specific questions asynchronously, and the platform captures the response, transcript, scoring signals, and evaluation notes for recruiter review. It is not a final hiring decision. The goal is to make early screening more consistent, reduce scheduling overhead, and give hiring teams comparable evidence before deciding who should move forward.

IntervAI scores interviews against the role criteria defined by the hiring team. The system evaluates candidate responses with a structured rubric, then presents score breakdowns, transcript evidence, and summary notes so recruiters can compare candidates consistently. Scores are decision support, not automatic hiring outcomes. Recruiters can review the evidence, calibrate expectations, and apply human judgment before shortlisting, rejecting, or inviting a candidate to the next step.

An AI screening interview is an early-stage interview that helps teams qualify many applicants with the same baseline questions and evaluation criteria. It is useful when resume volume is high or recruiter calendars are constrained. IntervAI can combine resume screening, async interview invitations, transcripts, and scoring into one workflow. The result is a clearer shortlist and a more documented basis for deciding which candidates deserve deeper human conversations.

AI screening usually refers to the broader first-stage qualification process, including resume matching, application review, and shortlisting. An AI interview is one screening method that collects spoken or written candidate answers through a structured interview flow. IntervAI supports both ideas: teams can evaluate fit from application data, invite candidates to async interviews, and review the combined evidence in a consistent candidate profile before moving to live interviews.

The human role remains central. IntervAI handles repetitive first-stage work such as asking consistent questions, collecting responses, producing transcripts, and organizing scoring evidence. Recruiters and hiring managers still define the role criteria, review candidate context, handle exceptions, and make final decisions. This separation matters: AI can standardize and document screening, but hiring judgment, team fit, candidate communication, and accountability stay with people.

IntervAI pricing depends on hiring volume, workflow needs, support expectations, and whether your team needs advanced features such as integrations, language evaluation, or custom onboarding. The public pricing page is designed to help teams estimate savings from reduced screening time and faster first-stage decisions. For larger or more complex recruiting operations, the best path is to discuss expected interview volume and integration requirements with the team.

The main return comes from reducing repetitive recruiter work: resume review, scheduling, first-screen calls, note taking, and manual candidate comparison. The exact ROI depends on applicant volume, role complexity, and how much of your first-stage process is currently manual. IntervAI is most useful when teams face high screening volume, slow shortlisting, or inconsistent interview documentation across recruiters and hiring managers.

Yes. Many teams start with a focused pilot on one hiring workflow, role family, or region before expanding. A good pilot defines success metrics up front: screening time saved, candidate completion rate, shortlist quality, recruiter adoption, and hiring manager satisfaction. IntervAI can support a practical rollout by helping configure job criteria, candidate invitations, evaluation rubrics, and reporting for the selected pilot roles.

IntervAI can connect with recruiting systems through API and webhook-based workflows, depending on the ATS and the data flow your team needs. Typical integrations cover candidate creation, interview invitation, status updates, evaluation results, and recruiter review links. For custom setups, the implementation team maps the current hiring workflow first, then defines which events should move automatically and which should remain under recruiter control.

The integration scope depends on your configuration, but common data includes candidate identifiers, role details, invitation status, interview completion status, transcripts, score summaries, and evaluation links. Teams should decide which fields are necessary for recruiting operations and avoid syncing unnecessary data. IntervAI is designed to support controlled workflows where recruiters can access structured evidence without turning the ATS into an unfiltered data dump.

Simple teams can create a role and begin screening candidates quickly. More complex rollouts take longer because they include workflow mapping, ATS configuration, scoring calibration, recruiter training, and candidate communication templates. The right timeline depends on how standardized your hiring process already is. A focused pilot is usually the fastest way to validate IntervAI while building the internal playbook for broader adoption.

AI interviewing can be used in the EU, but companies need appropriate governance. That means clear candidate communication, data minimization, documented evaluation criteria, human oversight, and a process for reviewing automated outputs. IntervAI is built for GDPR-aware and EU AI Act-ready recruitment workflows, but each employer remains responsible for its hiring policy, lawful basis, notices, and final employment decisions.

IntervAI is designed around GDPR-aware handling of candidate information, including controlled access, encrypted data handling, and privacy-conscious recruitment workflows. The practical compliance setup also depends on how each customer configures notices, retention periods, user access, and integrations. For hiring teams, the important point is to keep data use tied to the recruitment purpose, document decisions, and avoid collecting information that is not needed for the role.

Candidates should receive clear information when AI-supported tools are part of the hiring process. Good communication explains what the tool does, what data is collected, how results are reviewed, and who makes the final decision. This is both a trust issue and a governance issue. IntervAI supports structured documentation, but employers should align candidate notices with their legal obligations and internal hiring policies.

AI interviews are not automatically fair, but they can support fairer processes when designed carefully. The key safeguards are structured questions, job-relevant scoring criteria, consistent evaluation, human review, and monitoring for adverse impact. IntervAI helps standardize early-stage interviews and preserve evaluation evidence. Hiring teams still need to validate criteria, avoid irrelevant signals, and review outcomes before making decisions.

Yes. Any hiring tool can create or amplify bias if it uses poor criteria, inconsistent data, or weak human oversight. AI should not be treated as neutral just because it is automated. IntervAI is intended to support structured, reviewable screening: teams define job-relevant criteria, candidates answer comparable questions, and recruiters review the evidence. Governance, calibration, and ongoing monitoring remain necessary parts of a responsible hiring process.

Accuracy depends on the quality of the role criteria, the interview questions, and how well the scoring rubric matches the job. IntervAI is designed to provide consistent, interpretable evaluation signals rather than black-box final decisions. Recruiters should use the score breakdown, transcript, and evidence notes together. For best results, teams should calibrate scoring on real roles and review whether outputs align with hiring manager expectations.

Recruiters should set clear rules before the interview, not improvise after suspicion appears. Some AI use, such as preparation or note organization, may be acceptable. Live answer generation or misrepresentation may violate the process. The best approach is to define an AI-use policy, communicate it to candidates, design follow-up questions that test real understanding, and document any integrity concerns consistently.