Siadhal Magos and Shahriar Tajbakhsh were working at Uber and Palantir, respectively, once they each got here to the belief that hiring — particularly the technique of interviewing — was becoming unwieldy for a lot of corporate HR departments.
“It was clear to us that a very powerful a part of the hiring process is the interviews, but in addition probably the most opaque and unreliable part,” Magos told TechCrunch. “On top of this, there’s a bunch of toil related to taking notes and writing up feedback that many interviewers and hiring managers do all the pieces they will to avoid.”
Magos and Tajbakhsh thought that the hiring process was ripe for disruption, but they desired to avoid abstracting away an excessive amount of of the human element. So that they launched Metaview, an AI-powered note-taking app for recruiters and hiring managers that records, analyzes and summarizes job interviews.
“Metaview is an AI note-taker built specifically for the hiring process,” Magos said. “It helps recruiters and hiring managers focus more on attending to know candidates and fewer on extracting data from the conversations. As a consequence, recruiters and hiring managers save a ton of time writing up notes and are more present during interviews because they’re not having to multitask.”
Metaview integrates with apps, phone systems, videoconferencing platforms and tools like Calendly and GoodTime to routinely capture the content of interviews. Magos says the platform “accounts for the nuances of recruiting conversations” and “enriches itself with data from other sources,” resembling applicant tracking systems, to focus on probably the most relevant moments.
“Zoom, Microsoft Teams and Google Meet all have transcription in-built, which is a possible alternative to Metaview,” Magos said. “But the knowledge that Metaview’s AI pulls out from interviews is much more relevant to the recruiting use case than generic alternatives, and we also assist users with the subsequent steps of their recruiting workflows in and around these conversations.”
Image Credits: Metaview
Actually, there’s plenty mistaken with traditional job interviewing, and a note-taking and conversation-analyzing app like Metaview could help, no less than in theory. As a chunk in Psychology Today notes, the human brain is rife with biases that hinder our judgement and decision making, for instance an inclination to rely too heavily on the primary piece of knowledge offered and to interpret information in a way that confirms our preexisting beliefs.
The query is, does Metaview work — and, more importantly, work equally well for all users?
Even one of the best AI-powered speech dictation systems suffer from their very own biases. A Stanford study showed that error rates for Black speakers on speech-to-text services from Amazon, Apple, Google, IBM and Microsoft are nearly double those for white speakers. One other, newer study published within the journal Computer Speech and Language found statistically significant differences in the way in which two leading speech recognition models treated speakers of various genders, ages and accents.
There’s also hallucination to think about. AI makes mistakes summarizing, including in meeting summaries. In a recent story, The Wall Street Journal cited an instance where, for one early adopter using Microsoft’s AI Copilot tool for summarizing meetings, Copilot invented attendees and implied calls were about subjects that were never discussed.
When asked what steps Metaview has taken, if any, to mitigate bias and other algorithmic issues, Magos claimed that Metaview’s training data is diverse enough to yield models that “surpass human performance” on recruitment workflows and perform well on popular benchmarks for bias.
I’m skeptical and a bit wary, too, of Metaview’s approach to the way it handles speech data. Magos says that Metaview stores conversation data for 2 years by default unless users request that the information be deleted. That looks like an exceptionally very long time, and candidates would probably.
But none of this appears to have affected Metaview’s ability to get funding or customers.
Metaview this month raised $7 million from investors including Plural, Coelius Capital and Vertex Ventures, bringing the London-based startup’s total raised to $14 million. Metaview’s client count stands at 500 corporations, Magos says, including Brex, Quora, Pleo and Improbable — and it’s grown 2,000% year-over-year.
“The cash will likely be used to grow the product and engineering team primarily, and provides more fuel to our sales and marketing efforts,” Magos said. “We’ll triple the product and engineering team, further fine-tune our conversation synthesis engine so our AI is routinely extracting exactly the precise information our customers need and develop systems to proactively detect issues like inconsistencies within the interview process and candidates that look like losing interest.”
