Siadhal Magos and Shahriar Tajbakhsh were working at Uber and Palantir, respectively, when they both realized that hiring—especially the interview process—was becoming cumbersome for many corporate HR departments.
“It was clear to us that the most important part of the hiring process is the interviews, but also the most opaque and unreliable part,” Magos told TechCrunch. “Plus, there’s a ton of drudgery associated with note-taking and feedback that many interviewers and hiring managers do their best to avoid.”
Magos and Tajbachs felt the hiring process was ripe for disruption, but wanted to avoid removing too much of the human element. So they started Metaviewan AI-powered note-taking app for recruiters and hiring managers that records, analyzes and summarizes job interviews.
“Metaview is an artificial intelligence notebook built specifically for the recruitment process,” said the Wizard. “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 taking notes and are more present during interviews because they don’t have to multitask.”
Metaview integrates with apps, phone systems, video conferencing platforms and tools like Calendly and GoodTime to automatically download interview content. Magos says the platform “calculates the nuances of recruiting conversations” and is “enriched with data from other sources,” such as applicant tracking systems, to highlight the most relevant moments.
“Zoom, Microsoft Teams, and Google Meet all have built-in transcription, which is a potential alternative to Metaview,” Magos said. “But the insights that Metaview’s AI gleans from interviews are much more relevant to the recruiting use case than generic alternatives, and we’re also helping users with next steps in their recruiting workflows in and around those conversations.”
Image Credits: Metaview
Sure, there’s a lot wrong with the traditional job interview, and a note-taking and conversation analysis app like Metaview could help, at least in theory. As a piece in Psychology Today notes, the human brain is full of biases that hinder our judgment and decision-making, for example a tendency to rely too much on the first information offered and to interpret 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 study found that error rates for black speakers on speech-to-text services from Amazon, Apple, Google, IBM and Microsoft are almost double that for the white speakers. Another, more recent study published in the journal Computer Speech and Language found it statistically significant differences in how two leading speech recognition models treated speakers of different genders, ages and accents.
There is also hallucination to consider. AI makes mistakes summarizingincluding in meeting summaries. In a recent story, the Wall Street Journal cited an example where, for an early adopter using Microsoft’s AI Copilot tool to summarize meetings, Copilot invented bystanders and implied calls were about topics that were never discussed.
When asked what measures, if any, Metaview has taken to mitigate bias and other algorithmic issues, Magos claimed that Metaview’s training data is diverse enough to yield models that “exceed human performance” in recruitment workflows and they perform well on popular bias benchmarks.
I’m skeptical and a bit wary, too, of Metaview’s approach to how it handles speech data. Magos says Metaview stores chat data for two years by default, unless users request that the data be deleted. That seems like an extremely long time.
But none of that seems 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 raises to $14 million. Metaview’s customer base numbers 500 companies, Magos says, including Brex, Quora, Pleo and Improbable — and has grown 2,000% year over year.
“The money will be used to grow the product and engineering team primarily and to fuel our sales and marketing efforts,” said Magos. “We will triple our product and engineering team, further improve our conversation synthesis engine so that our AI automatically extracts the right information our customers need, and develop systems to proactively identify issues such as inconsistencies in the interview process and candidates who seems to be losing interest.”
