Syadal Magos and Shahriar Tajbakhsh were working at Uber and Palantir, respectively, when they realized that recruiting — especially the interviewing process — had become unwieldy for many companies' HR departments.
“It was clear to us that the most important part of the hiring process is interviews, but also the most ambiguous and unreliable part,” Magos told TechCrunch. “On top of that, there's a host of hassles associated with taking notes and writing comments, which many interviewers and hiring managers do everything they can to avoid.
Magos and Tajbakhsh thought the hiring process was ripe for disruption, but they wanted to avoid extracting too much from the human element. So they launched Metaview, an AI-powered note-taking app for recruiters and hiring managers that scores, analyzes and summarizes job interviews.
“Metaview is an AI-powered note-taking tool designed specifically for the hiring process,” Magos said. “It helps recruiters and hiring managers focus more on getting to know candidates and less on extracting data from conversations. As a result, recruiters and hiring managers save a lot of time writing notes and are more present during interviews because they don't have to do tasks.” Multiple.
Metaview integrates with apps, phone systems, and video conferencing platforms and tools like Calendly and GoodTime to automatically capture interview content. Magos says the platform “takes into account the nuances of recruiting conversations” and “enriches itself with data from other sources,” such as applicant tracking systems, to highlight the most relevant moments.
“Zoom, Microsoft Teams, and Google Meet all have a transcription feature, which is a potential alternative to Metaview,” Magos said. “But the information Metaview’s AI extracts from interviews is more relevant to the hiring use case than generic alternatives, and we also help users with the next steps in their hiring workflow in and around these conversations.”
Image credits: MetaView
There's certainly a lot that goes wrong with traditional job interviews, and a note-taking and conversation analysis app like Metaview can help, at least in theory. As an article in Psychology Today points out, the human brain is full of biases that cloud our judgment and decision-making, for example the tendency to rely too heavily on the first piece of information presented and to interpret the information in a way that confirms our pre-existing beliefs.
The question is, does Metaview work, and more importantly, does it work equally well for all users?
Even the best AI-powered speech dictation systems suffer from their own biases. A Stanford University study showed that black speakers' error rates on speech-to-text services from Amazon, Apple, Google, IBM, and Microsoft are twice the error rates of white speakers. Another recent study published in the journal Computer Speech and Language found statistically significant differences in the way two leading speech recognition models handled speakers of different genders, ages, and dialects.
There are also hallucinations to consider. AI makes summarization errors, including in meeting summaries. In a recent story, the Wall Street Journal cited an example where, for one early adopter who used Microsoft's AI tool Copilot to summarize meetings, Copilot invented attendance and implied calls were about topics that were never discussed.
When asked what steps, if any, Metaview has taken to mitigate bias and other algorithmic issues, Magus claimed that Metaview's training data is diverse enough to produce models that “exceed human performance” in recruiting workflows and perform well under common bias criteria.
I'm also a bit skeptical and wary of Metaview's approach in how it handles speech data. Magos says Metaview stores conversation data for two years by default unless users request that the data be deleted. That seems like an exceptionally long time, and maybe the candidates will do it.
But none of this appears to have affected Metaview's ability to obtain financing or customers.
Metaview this month raised $7 million from investors including Plural, Coelius Capital and Vertex Ventures, bringing the total raised for the London-based startup to $14 million. Magus says Metaview has 500 corporate clients, including Brex, Quora, Pleo and Improbable – and has grown 2,000% year-on-year.
“The funds will be used to primarily grow the production and engineering team, providing more fuel for our sales and marketing efforts,” Magos said. “We will triple our production and engineering teams, improve our conversation synthesis engine so our AI automatically extracts exactly the right information our clients need and develop systems to proactively detect issues like inconsistencies in the interview process and candidates who appear to be losing interest.”
