Interview intelligence captures what happens in every interview as comparable signal: scorecards against one rubric, AI-screened answers, and evidence that flows straight into your hiring decision.
An interview is the most expensive data-gathering exercise in hiring, and most teams throw the data away. Notes live in five different docs, ratings use five different scales, and by the decision meeting the actual signal has decayed into half-remembered impressions. Interview intelligence is about capturing that signal cleanly and making it count.
Lehire treats every interview as structured evidence. Interviewers rate against one shared rubric per role, an optional AI Interviewer handles turn-based screening and scores the answers, and all of it rolls up into a candidate fit score the panel can trust. The interview stops being a memory test and becomes a measurement.
This matters because the quality of a hiring decision is capped by the quality of its inputs. Better interview signal in means better decisions out, and interview intelligence is how you raise the floor on what every interviewer contributes.
Interview intelligence is the capture, structuring, and scoring of interview signal so that it becomes comparable evidence for a hiring decision. It standardizes interviews against a shared rubric, records ratings as structured scorecards, and can use an AI Interviewer to screen and score candidates consistently. The goal is to replace decaying notes and inconsistent ratings with clean, comparable data that feeds directly into candidate fit scores.
Most interview processes are intelligence-poor by accident. Interviewers improvise questions, score on gut, and write up notes hours later when the details have faded. Two candidates for the same role get asked entirely different questions, then get compared as if the assessments were equivalent. They are not.
Interview intelligence imposes just enough structure to fix this without turning interviews into interrogations. The rubric defines what you are actually assessing. The scorecard captures ratings against it while the interview is fresh. The result is signal you can compare across candidates and across interviewers, which is the entire point of interviewing in the first place.
A Lehire scorecard is tied to the role rubric, so every interviewer rates the same criteria on the same scale. There is no more averaging a "strong yes" from one person against a "7 out of 10" from another and pretending the math means something. Everyone is answering the same questions about the same competencies.
When scores diverge, that is useful information rather than noise. If two interviewers rate the same candidate very differently on a single criterion, the disagreement is visible and specific, which is exactly the kind of thing a decision meeting should be discussing instead of re-litigating the whole candidate.
Lehire includes a turn-based AI Interviewer that conducts a screening conversation using text-to-speech and the candidate's microphone. It asks role-relevant questions, listens to the answers, and produces a score that is saved against the candidate. Because every candidate gets the same structured screen, the comparison is fair by construction.
This is not a replacement for human interviews. It is a consistent, scalable first pass that frees your team to spend live interview time on the candidates who clear the bar. The AI Interviewer feeds its scores into the same fit model as human scorecards, so the signal is unified.
Interview intelligence is only valuable if it changes the decision. In Lehire, scorecards and AI Interviewer results roll up into the candidate's 0 to 100 fit score and feed the Decision Engine ranking. The path from "what happened in the room" to "who we should hire" is direct and traceable.
Because the signal is structured, you can also audit it. If a candidate ranked highly, you can see which interviews and which criteria drove it. That traceability is what lets a hiring committee trust the process instead of arguing about it.
Every interviewer rates the same criteria on the same scale, so ratings are genuinely comparable.
Turn-based screening interviews that ask role-relevant questions and save scored results per candidate.
Human scorecards and AI screens roll into one 0 to 100 fit score for the candidate.
See exactly where interviewers diverge on a criterion so the panel debates the right thing.
Capture evidence against criteria while the interview is fresh, not hours later from memory.
Tie questions to the rubric so interviews actually assess what the role requires.
Most interviews generate impressions, not data. Here is what changes when the signal is structured.
Keep multi-interviewer panels consistent by anchoring everyone to the same rubric and scale.
Use the AI Interviewer to give every applicant a fair, consistent first-round screen.
Compare a new interviewer's ratings against the panel to spot and correct drift early.
Walk into the debrief with disagreements already surfaced, so discussion targets what matters.
A notes tool stores what was said. Interview intelligence structures and scores it against a rubric so the output is comparable evidence, not free text. That structure is what lets it feed directly into a fit score and ranking.
No. It is a consistent screening first pass that scores candidates against role-relevant questions. Your team still runs the live interviews; the AI Interviewer just makes sure everyone clears the same bar before getting there.
It runs a turn-based conversation using text-to-speech and the candidate's browser microphone. It asks questions, captures answers, scores them, and saves the result against the candidate so it feeds the overall fit signal.
Absolutely. The rubric and scorecard structure how judgment is captured, not whether it is used. Interviewers rate what they observed; Lehire just makes those ratings comparable across the panel.
They roll up into the candidate's 0 to 100 fit score and into the Decision Engine ranking, with full traceability back to which interview and criterion drove each part of the score.
Yes. Lehire sits on top of your ATS. Candidates can come from your ATS or Lehire's public application links, and evaluations export back to your ATS or to CSV.
See how interview intelligence turns conversations into comparable, scored evidence.