Technology for better conversations, not automatic decisions

Within a short time, many applications had to be reviewed, interviews held and decision bases prepared for HR. More than 80 applications arrived in the first few days alone. More than 160 have been processed so far.

A conventional process would have meant a lot of manual work. Each application would be evaluated individually and interview notes transcribed afterwards. Statements from different sources would be merged and candidate profiles rewritten for HR. Salary expectations would be recorded and compared by hand, and open questions for further interviews identified. This work is necessary. But it takes a lot of time without automatically leading to better conversations or better decisions.

The central question was therefore how technology could increase quality in recruiting without replacing personal exchange. The goal was not to classify candidates as quickly as possible or filter them out automatically. Instead, every person should be shown as completely as possible, in line with their actual experience, statements and abilities.

Every candidate is treated as a person, not as a data record. Interviews are held in person. Even after a rejection, a direct exchange is possible to clarify questions, explain decisions and discuss feedback. This dialogue is a deliberate part of the recruiting process. Technology should not create distance. It should improve the quality of the exchange.

A key part of the refined process is transcribing the interviews. With the candidates' consent, the interviews are transcribed and prepared for structured evaluation. So the interviewer no longer has to focus on the conversation, handwritten notes and a later summary at the same time. Transcription captures relevant statements completely. It means fewer memory gaps, less lost detail and better traceability. Summaries become more precise and candidate profiles more substantial, and further interviews can be prepared in a more targeted way. The conversation itself is still led by people. Afterwards, technology helps process what was discussed completely and in a structured way.

A CV only partially reflects a person. Interviews often reveal competencies, experience, motivation and context that do not emerge from written documents alone. Combining application documents with the interview transcripts creates a more nuanced profile. The point is not to rate people with an automated score. It is to reproduce their statements precisely and prepare the relevant information clearly for HR. This way, candidates are not judged only by job titles or individual keywords. Their actual experience and explanations carry more weight.

QUENARIS is used as supporting logic to structure and formulate the content. In particular, it helps to organise information from interviews and documents and to bring together relevant statements. It also helps to formulate profiles consistently, make open points visible, prepare results clearly for HR and create comparable structures across several candidates. QUENARIS does not make selection decisions. The technology helps present information completely, consistently and clearly. Assessment and responsibility remain with the people involved.

Salary expectations stated in candidates' feedback are imported in a structured way. Processing is automated. Real data from the ongoing recruiting process can thus be presented clearly and evaluated by relevant criteria. For example, it shows which salary expectations are stated for certain functions and which ranges appear at different experience levels. It also shows notable deviations, how the feedback fits the intended pay bands, and where clarification or discussion is needed. The analysis does not set salaries. It improves the basis for a traceable classification.

The approach builds on experience from earlier recruiting mandates. In those, AI was already used to structure large volumes of applications more effectively. In the current mandate, it was developed further. Interviews are captured more completely, and transcripts and salary data are processed automatically. Candidate profiles are formulated more consistently, and documents, statements and feedback are more closely connected. Information is prepared more directly for HR. At the same time, personal exchange has been expanded.

Technical support does not mean less time for candidates. The opposite is true. Technology supports transcription, structuring and formulation. That leaves more time for personal conversations, follow-up questions, in-depth assessments, individual feedback, talks after a rejection and careful coordination with HR. The technology reduces administrative work. It replaces neither the conversation nor the responsibility.

The process was designed so that both sides benefit. For candidates, personal conversations remain central. Their statements are captured more completely and their experience is presented in more detail. Important information is less easily lost. Exchange remains possible even after a rejection, and no one is reduced to a CV or a score. For the company, large volumes of applications remain manageable. HR receives more substantial candidate profiles, and interview content is documented traceably. Salary expectations are evaluated in a structured way and manual follow-up work is reduced. Decisions can be made on a broader information basis.

The status so far in the ongoing mandate: more than 160 applications processed. Missing information in CVs can be pointed out concretely, so candidates can prepare their documents more effectively. Advice is possible even after a rejection. Some candidates have since booked separate advice to improve their applications. This extra benefit adds no significant time to the recruiting process. Technology structures and supports. People make the decisions.