# How to document hiring interviews with AI and make them comparable > Guide to documented interviews with AI: transcription with Fireflies, a structured script, fair candidate comparison and the rules of recording. - Canonical: https://serchai.com/en/guides/ai-interview-notes/ - Site: Serchai (https://serchai.com) — AI tools comparator - Language: en - Updated: 2026-07-26 --- ## Tools you will use - [Fireflies](https://serchai.com/en/reviews/fireflies/) — Records, transcribes and summarizes your meetings and interviews in 100+ languages. - [Claude](https://serchai.com/en/reviews/claude/) — Anthropic's AI assistant for writing, analysing and thinking through documents. - [Workable](https://serchai.com/en/reviews/workable/) — The hiring platform with an AI agent that sources, screens and qualifies candidates. ## The steps, in short 1. **Prepare the structured interview script** — The same core questions for every candidate: the base of fair comparison. 2. **Record and transcribe with disclosure and Fireflies** — The assistant joins the call, transcribes and summarizes, and the interviewer actually listens. 3. **Turn each interview into a criteria-based evaluation** — From summary to per-criterion scores with literal quotes as evidence. 4. **Compare candidates on what was said, not what is remembered** — The final decision happens with the evidence on the table and the differences visible. > **TLDR:** The undocumented interview gets decided by memory and likability, which is the recipe for bias. The documented system: a structured script with the same core questions, Fireflies recording and transcribing (always disclosed), each candidate evaluated against criteria with literal quotes, and the final decision comparing evidence. It comes out fairer, more defensible and, surprise, faster than the classic round of impressions. This guide is for anyone who interviews and decides: managers hiring for their team, HR coordinating processes, founders making their first hires. The problem it attacks is familiar: after four interviews, what remains is impressions, and impressions systematically favor the candidate who resembles the interviewer. It continues the process from the [screening candidates](https://serchai.com/en/guides/ai-candidate-screening/) guide: the criteria defined there are the same ones evaluated here in person. ## 1. Prepare the structured interview script The comparable interview starts before the call: a script with the same core questions for every candidate in the process. The evidence has been clear on this for decades: the structured interview predicts better and discriminates less than free-flowing conversation. The practical script: four or five core questions linked to the role's criteria (real situations lived, not hypotheticals), room to dig where each conversation calls for it, and the same final ten minutes for the candidate's questions. [Claude](https://serchai.com/en/reviews/claude/) helps turn each criterion into a well-phrased situational question, from $17 a month and with a permanent free tier. An interview question is won or lost on how it is written, since the badly phrased one leads the answer, so the assistant that handles text best beats the one that compares models. Structure does not steal naturalness: the script is the skeleton and the conversation remains a conversation. What it guarantees is that something comparable exists at the end. ## 2. Record and transcribe with disclosure and Fireflies [Fireflies](https://serchai.com/en/reviews/fireflies/) joins the video call, records, transcribes with separated speakers and delivers the summary within minutes. The immediate effect on the interview: the interviewer without a notebook actually listens, asks better and keeps the eye contact note-taking used to break. From $10 per seat per month annually. The recording rules are not optional: candidate disclosure beforehand (in the invitation and at the start), a basis for processing, and interview data treated as what it is, personal data from a hiring process, with its retention period and restricted access. Transcription is not a shortcut around data protection: it is part of it. The quality nuance: check the summary against the transcript on each process's first interview. Automatic summaries are good and not perfect, and knowing where they weaken (nuance, irony, long answers) calibrates how much to trust. ## 3. Turn each interview into a criteria-based evaluation The transcript is raw material: the useful piece is the evaluation. After each interview, the short flow: with the transcript in front of you, score each role criterion and paste the literal quote supporting each score. Fifteen minutes with the summary's help, and the candidate is evaluated with evidence instead of adjectives. The literal quote is the system's key piece: "managed the migration of an eight-person team" is evidence. "Good leadership" is an impression. When the decision gets questioned (by the team or a candidate), the quotes are the difference between explaining and improvising. In processes on a full platform, [Workable](https://serchai.com/en/reviews/workable/) integrates these evaluations into the candidate's file alongside the rest of the team's, keeping the whole process in one place. ## 4. Compare candidates on what was said, not what is remembered The final decision changes nature with this system: instead of the round of impressions ("I got a good feeling"), the table holds every candidate's per-criterion evaluations with their quotes. Real differences jump out, and ties get broken by returning to the evidence, not by raising voices. The complete system's accumulated benefits: better decisions (decided on what was said), fairer ones (everyone faced the same questions and criteria), defensible ones (everything has evidence) and a faster process than it sounds, because discussions with evidence on the table take half as long. And a side benefit teams discover later: interview transcripts are the best interviewer school there is, because they let you review your own questions. The whole sector lives in [AI for human resources](https://serchai.com/en/ai-for/human-resources/). ## Common mistakes Recording without disclosure. Beyond the legal breach, it destroys the trust the interview needs. The double notice (invitation and opening) is the standard. Interviewing without a shared script. Without common core questions, transcripts document incomparable conversations: plenty of record, zero comparison. Scoring without quotes. The adjective evaluation ("good fit") is the old impression in a new format. The literal quote is what turns the score into evidence. Keeping transcripts indefinitely. Candidate data has a life cycle: limited retention, restricted access and deletion when the process ends, per your data protection policy. ## Frequently asked questions ### Do candidates accept being recorded? With clear disclosure and the why explained (fairness and comparability), the vast majority do. And the candidate who prefers not to be recorded must be able to continue with manual notes: recording is a tool, not a filter. ### How much does this system cost? Fireflies from $10 per seat per month, and the script costs one afternoon per role type. The full platform with Workable enters at process volume, from $299 a month. ### Can AI evaluate the interview for me? It can summarize and help you locate answers per criterion: the scoring and the decision belong to the interviewer. Automatic candidate evaluation in interviews sits squarely in regulatory high-risk territory, and it also loses exactly the nuances you interview in person for. ### Does it work for in-person interviews? Yes, with the mobile app recording the room (same disclosure). Transcription quality drops somewhat against a clean-audio video call, and the whole system works identically.