Retorio is a video recruiter that fuses artificial intelligence and machine learning with scientific findings from psychology and organizational research. Even from short application videos Retorio reliably detects the communication behavior and important personality traits of the applicant. Talents can be recognized more effectively and reliably and compared with different job profiles.
The scientific research behind Retorio has been done at the faculties for psychology and computer sciences at TUM university in Munich. Retorio’s analysis is based on the universally accepted, advanced psychological model of personality traits, the BIG 5. This model describes an individual’s personality in terms of 5 dimensions:
Though personality traits cannot specifically predict behavior, the Big 5 gives insight into how individuals may react, behave, and interpret things differently from others in the same situation. Personality thus constitutes a crucial predictor for job relevant behavior which measures such as task fit and cultural fit can be inferred from.
Based on the research of the Big 5 of Personality, our AI analyzes each video within seconds for thousands of visual cues to predict personality and behavioral patterns, presenting you with the equivalent of dozens of recruiters taking the time to interview each candidate.
Our system has a very good prediction accuracy of around 92%. Imagine asking thousands of people what they think about the candidate. We achieve the same results to 92%.
Although resume screening constitutes one of the most frequently used pre-selection methods for applicants, it is nonetheless a rather unreasonable way of screening a candidate’s personality - assessing only one out of the five personality traits of a candidate correctly. In fact, recent studies have shown that not only do recruiters diverge strongly in their assessment of character. But also does assessment through resume merely relate slightly to the candidate’s actual personality (through self-assessment). Consequently, with resume screening being the “initial employment gatekeeper”, applicants failing this rather arbitrary selection process will not further be considered for a job position, though in fact: they might be eligible candidates!
Pre-employment assessments are used to evaluate job applicants. The assessment includes plenty of methods and tools employers can use to assess whether or not a job applicant is a right fit for their organization. Pre-employment assessments introduce an element of objectivity into the hiring process by providing concrete results that can be standardized across all applicants. Employers can then use these data to make better informed, more defensible hiring decisions before proceeding to more resource-demanding procedures like personal interviews.
Fully customizable. We can help you with best practices.
Yes, you can tailor questions individually for your job profile -task requirements, team formation and organizational culture
The opposite is the case, Retorio enhances your exisiting team. Retorio's software gives a listing recommendation of your candidates displaying assessed fit based on your previously selected job profile and organizational culture. Those fit parameters are then to be considered by your recruitment team in order to make elaborate preselection decisions (whom to invite to a personal interview). Thus, the HR department is both indispensable in defining the job profile, preselection and final selection decisions.
Absolutely; you can invite candidates and view all their results without the need for an additional tool like an ATS.
You can request a demo version by leaving your email address with us under the following link.
Feel free to write us anytime to firstname.lastname@example.org
The maximum amount of time that is needed usually does not exceed 5 minutes in total, with one minute scheduled for each question. However, the length of the assessment depends on the number of questions posed by the recruiter.
You will need a recording camera, functioning voice recording and quiet environment, preferably a white the background and sufficient light.
Just like there's no right or wrong personality in a partner or friends -they either fit your character or they don't-, there is no right or wrong personality for a job in general. Most importantly, Retorio will assess a fit between the candidate's personality and job tasks, as well as the company culture and existing team. Performing well therefore means attaining a high fit. But here it gets very individual and candidates should at all costs avoid faking responses, gestures or mimic. It is most important to try and behave naturally, lean back and relax. As a fit that's not based on your actual personality will neither be helpful for candidate nor company.
No, it will only be used for the selected job position. We don't store the results for further positions.
We make it our objective to assess candidates' personality as objectively as possible. We guarantee high objectivity and reliability by removing single observer-based bias from the data and using multiple observer ratings. We also set value on removing any biases resulting from gender, age, ethnicity or skin color.
The combined rating of the AI makes use of visual input from facial expression and gesture, as well as auditive information like speaking speed, language sentiment and engaging language.
The Retorio observer based assessment can be expected to show higher external validity than self-assessment of personality traits (which rather reflects the self-concept). Since personality traits are crucial predictors of job behavior, it is essential taking these into account when making hiring decisions. For more background on the validity of assessment, click here.
For candidates to cheat, there would have to be an ideal state the cheating aims at. In personality assessment, there can be multiple ways to reach a goal. In general, we do have a high test-retest reliability. That is, if the same person takes the test in the morning or evening, only a deviation of around 9% occurs. However, if the candidate decides to intentionally act in a different role from their own personality, reliability can be hypothesized to be somewhat lower. It must be said, that this can also occur in a face-to-face interview or on the job itself. That is why perceived personality is important.
Facial expression detection uses biometric markers to detect emotions in human faces. It analyzes emotional sentiment and detects the six basic or universal expressions: happiness, sadness, anger, surprise, fear, and disgust.
Facial expressions are among other gestures that are part of nonverbal communication. Computer-based facial expression detection’s aim is to correctly mimic how humans interpret these subtle cues. These cues are essential to interpersonal interactions as they give additional information to spoken words.
Facial expression detection extracts and analyzes information from an image or video. With ample amounts of unbiased emotional responses as data, technology can detect the six universal expressions.
While FED detects human behavior and emotions, facial recognition uses biometric software to map out an individual’s facial features, storing it as a faceprint.
With facial expression, AI is solely looking for emotion and behavior---not the individual’s faceprint. Bias has long plagued facial recognition algorithms. Neural networks train on different numbers of faces from different groups of people.