Technology for better conversations, not automatic decisions

A large number of applications had to be reviewed, interviews conducted and decision bases prepared for HR within a short period of time. More than 80 applications arrived within the first few days alone, with more being processed continuously.

A conventional process would have caused a high manual workload: evaluating application documents individually, transcribing interview notes afterwards, bringing together statements from different sources, rewriting candidate profiles for HR, manually recording and comparing salary expectations, and identifying open questions for further interviews. This work is necessary, but it ties up a lot of time without automatically producing 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 sort them out automatically, but to represent every person as completely as possible and in line with their actual experience, statements and abilities.

Every candidate is treated as a person, not as a data record. Interviews are conducted in person, and even after a rejection there is an opportunity for direct exchange, where questions can be clarified, decisions explained and feedback discussed. This dialogue is a deliberate part of the recruiting process – technology is not meant to create distance, but to improve the quality of the exchange.

A key part of the further-developed process is transcribing the interviews. This means the person conducting the interview no longer has to focus simultaneously on the conversation, handwritten notes and a later summary. Transcription enables complete capture of relevant statements, fewer memory gaps and less loss of detail, better traceability, more precise summaries, more substantial candidate profiles and more targeted preparation of further interviews. The conversation itself remains human-led – the technology then 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. Connecting application documents with transcribed interview content creates a more differentiated profile. The point is not to rate people using an automated score, but to reproduce their statements precisely and prepare the relevant information for HR in an understandable way – so candidates are not judged only by job titles or individual keywords, but their actual experience and explanations are given greater weight.

Quenaris is used as a supporting logic for structuring and formulating the content. The solution helps in particular to organise information from interviews and documents, bring together relevant statements, formulate profiles consistently, make open points visible, prepare results in an understandable way for HR, and create comparable structures across several candidates. Quenaris does not make selection decisions – the technology helps present information completely, consistently and clearly, while assessment and responsibility remain with the people involved.

Feedback from around 60 candidate profiles was used to read in the stated salary expectations in a structured way. Processing is automated, allowing real data from the ongoing recruiting process to be presented clearly and evaluated by relevant criteria – for example, which salary expectations are stated for certain functions, which ranges appear at different experience levels, where notable deviations lie, how the feedback fits with 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 current process builds on the experience of an earlier recruiting mandate involving around 400 applications, where AI was already used to structure large volumes of applications more effectively. In the current project, this approach was developed further – with more complete capture of interviews, automated processing of transcripts, more consistent formulation of candidate profiles, a stronger connection between documents, statements and feedback, automated processing of salary data, more direct preparation of information for HR, and a simultaneous expansion of personal exchange. This increased efficiency further: individual work steps that can take one to two working days manually can be completed within a few hours – depending on the task, an acceleration of roughly a factor of five to ten.

The higher speed does not mean less time is dedicated to candidates – the opposite is true. Because transcription, structuring and formulation are technically supported, more time remains for personal conversations, follow-up questions, in-depth assessments, individual feedback, conversations 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, statements are captured more completely, experience is presented in a more differentiated way and important information is less easily lost; exchange remains possible even after a rejection, and the person is not reduced to a CV or a score. For the company, large volumes of applications remain manageable, HR receives more substantial candidate profiles, interview content is documented traceably, salary expectations are evaluated in a structured way, manual follow-up work is reduced, and decisions can be made on a broader information basis.

The status so far: more than 80 applications within a few days, ongoing processing of further applications, experience from an earlier mandate with around 400 applications, around 60 profiles as the basis for the automated salary analysis, personal conversations with candidates, the option to stay in touch even after rejections, transcription and structured preparation of interviews, use of Quenaris for structure and formulation, and a reduction of individual work steps from days to hours – with higher information quality for both HR and candidates.