Evidence comes back.
Judgement stays yours.

Your team sets the role’s requirements, checks the brief and publishes. aikho then prepares the interview and its assessment. You can read the plan at any time. Publishing is not approval of a set of questions shown in advance.

Once conversations are available, your team can read the evidence, explore gaps and choose whom to meet. aikho’s suggestions follow defined scoring rules and identify candidates it has enough evidence on. They do not make the final hiring decision.

aikho may decline an application when its CV or interview assessment establishes that one of the role’s hard requirements is not met. The rule is tied to requirements your team set for the role.

Missing or uncertain information is not proof that a requirement has failed. If a topic did not come up, the assessment reports that gap. Your team should read the evidence and distinguish an established failure from something that still needs asking.

Automatic closure of low-scoring applications is off by default. Employers can enable it after a quiet period. It needs a usable score, sufficient interview information and no recorded failure in assessment processing.

This is different from a requirement-based decline. One acts on an established failure of a hard requirement; the other uses the separate, optional low-score closure conditions. Neither means that aikho chooses whom to hire.

Your team can review the conversation and the evidence behind the ratings. The interview describes what the candidate said about their work. It does not independently verify claims or establish future performance.

Use your own follow-up conversations and reference checks as needed. Candidates are told before they start that aikho is an AI and that the team can review their conversation. Their employer remains the contact for questions about the process and their data.