Question the unknowns
Prioritize consequential gaps, counter-evidence, and hard gates rather than generating a generic question bank.
AI interview evaluation
Turn interviews from impression sharing into role-based evidence review
Aimme prepares targeted questions from confirmed role requirements and unresolved resume evidence, then organizes interview facts, opinions, conflicts, and gaps. It helps teams compare evidence without replacing interviewer or hiring-lead judgment.
The recruiting problem
Interview feedback often exists as fragmented notes, overall impressions, and inconsistent interviewer standards. Without shared criteria, teams cannot tell whether a conclusion came from candidate evidence, interviewer inference, or an untested assumption.
How Aimme handles it
Break recruiting judgment into inspectable steps
Organize by requirement
Map interview material to responsibilities, capability, eligibility, and feasibility.
Separate fact from judgment
Distinguish candidate statements, locatable evidence, interviewer opinion, and model summary.
Support calibration
Give interviewers shared criteria, conflicting feedback, and remaining validation questions.
From question to reviewable result
Preserve context—and the opportunity to stop and review
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01
Prepare
Generate interview focus from the role profile, candidate evidence, and unknowns.
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02
Collect
Use notes, documents, or audio/video only with appropriate authorization and consent.
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03
Organize
Extract relevant evidence, counter-evidence, conflict, and missing information by criterion.
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04
Review
Interviewers inspect summaries and references, then record the human conclusion and next step.
Why the result is reviewable
Fluent model output is not treated as fact
- Evaluation conclusions map to an interview record or uploaded source.
- Requirements not discussed remain unknown rather than silently passing.
- Conflicting interviewer feedback stays visible and triggers calibration.
- Teams must obtain recording, transcription, and processing permission under applicable law and policy.
Operating boundaries
Be explicit about what the system does—and does not do
- Aimme does not recommend candidates based on protected traits such as name, gender, age, or ethnicity.
- Model output is recruiting support, not a hiring, rejection, or compensation decision.
- Missing evidence, conflicting sources, and unknown hard requirements are surfaced for human verification.
- Customers control candidate data. It is not used to train public models, and teams remain responsible for lawful, platform-compliant use.
Frequently asked questions
Answer the important questions first
Which interview materials can Aimme evaluate?
It can use interview notes, documents, and supported audio/video that the team is authorized to process. Formats and limits depend on the current plan.
Does AI decide whether a candidate passes?
No. It organizes job-related evidence and risk; the recruiting team makes progression decisions.
What happens to skills that were not discussed?
They remain unknown or insufficient evidence, with a proposed follow-up question instead of an assumed result.
Aimme
Start with one real role
Describe an active role. Aimme clarifies the requirements, searches and matches candidates, and organizes the evidence. Your recruiting team confirms every progression decision.