Verbit raises $11 million Seed Round for its AI powered Transcription solution
HV Holtzbrinck Ventures, Vertex Ventures and Oryzn Capital led investment
Software company tackles billion Dollar transcription market
April 2018 - Verbit secures a $11million seed funding, led by HV Holtzbrinck Ventures, Vertex Ventures and Oryzn Capital. Verbit was founded only a year ago by entrepreneurs Tom Livne (CEO), Eric Shellef (CTO) and Kobi Ben-Tzvi (VP of Engineering). The startups operates in the professional transcription market, which is a multi billion dollar market for its various connections to clients at different verticals such as: Legal, education, media resources, medical and more.
“We already generated millions of dollars in revenue, but we've only just begun. With this funding the company set aggressive growth targets and significantly expanded product capabilities”, Livne says. As such, Verbit plans to double the number of employees in the coming year. As Livne explains, these steps must be taken in order to realize the company's vision: "To make the world’s verbal content accessible and searchable”.
Every day billions of new video and audio minutes are created. Traditional transcription companies rely on manual work, resulting in expensive service costs and long turnaround times. At the same time fully automatic transcription only reaches an average accuracy rate of 70%, missing customer demands for professional, quality and a 99.9% accurate transcription.
Verbit has developed a unique transcription solution which uses a combination of AI technologies to achieve accuracy and speed, at a scale large enough to make the world’s audio and video content accessible. It integrates automatic speech recognition algorithms with human augmented refinement. Any correction made by human transcribers contributes to and improves the Verbit algorithm through machine learning technologies.
“Many tech giants strive to master speech recognition and transcription and we are very happy and proud to offer the results of a head-to-head demonstration. This comparison can be located here and this is how we successfully differentiated ourselves from our competitors”, says Livne.
Because most of the transcription process takes place automatically, Verbit has a major competitive cost structure. As a direct result, the final price per customer is significantly lower than competitors, while ensuring the quality and delivery of the service at an exceptional speed. The company charges its customers according to the audio/video minutes it transcribes. Verbit has already connected with many customers including Ivy league universities and leading E-learning platforms such as Coursera, Udacity, and London Business School.
“The level of accuracy and automation Verbit has reached is way beyond anything I saw in the industry. Those capabilities will allow Verbit to build a category leading company in the fragmented transcription market,” say Jasper Masemann, from HV Holtzbrinck Ventures.
“Verbit’s offering to its customers is very simple – 100% accurate transcription service at a very competitive cost with the fastest turn around in the market,” explains Yanai Oron, GP at Vertex Ventures Investment Fund. “No wonder the company has reached such a great product market fit and was able to generate these kind of revenues in its first year of existence. The funding should allow the company to expand to a number of new verticals while scaling its business in the existing verticals.”
Verbit is a fast growing AI startup focused on building a cutting edge transcription technology. Its solution is designed from start to finish to handle large scale jobs and still deliver the speed and accuracy today’s customers expects. The company uses adaptive artificial intelligence, a dual human editing process and an enterprise-grade management platform to provide each customer with the unique solution that they need. The company was founded by three highly experienced entrepreneurs only one year ago. All three have years of experience in deep learning, speech recognition, VC funding strategy and in large scale software engineering.