Heartex

Russian software developers Malyuk, Maxim Tkachenko and Nikolai Lyubimov co-founded Heartex in 2019. Then the startup became one of the winners of the Sberbank accelerator and 500 Startups. The company positions itself as a developer of data labeling software designed to manage data in one place. The company's software helps add management and annotator capabilities to facilitate and optimize data labeling, quality assurance, and analytics, enabling enterprises to quickly annotate their datasets. Heartex aims to solve what Malyuk considers the main obstacle in the enterprise: extracting value from data using AI.

History

2022: Attracting $25 million investment

On May 18, 2022, Heartex, a startup developing an open-source platform for labeling data, announced it would raise $25 million as part of a Series A funding round led by Redpoint Ventures. Unusual Ventures, Bow Capital and Swift Ventures also participated, bringing Heartex's total raised capital to $30 million.

Co-founder and CEO Michael Malyuk said the new money will be used to improve Heartex products and expand the company's workforce from 28 people to 68 by the end of 2022.

Heartex Big Data Platform Founded by Russians Raises $25 Million

Being from the engineering environment and machine learning, the team of the founders of Heartex knew how machine learning and AI can bring organizations, "Malyuk said in a written interview. At that time, we all worked in different companies and in different industries, but we were united by the problem of model accuracy due to poor-quality technical data. We have come to the conclusion that the only acceptable solution is to entrust annotating and supervising training data to internal teams with knowledge in this field. Who can provide the best results except for your own experts?.

According to Malyuk, if data labeling attracts increased attention from AI development companies, this is because labeling is a major part of the AI development process. Many AI systems "learn" to understand the meaning of images, video, text and audio using examples that have been labeled by groups of human annotators.

The problem is that not all labels are the same. Labeling data such as legal contracts, medical images and scientific literature requires special knowledge that not every annotator has. And - being human - annotators make mistakes.

Malyuk does not claim that Heartex completely solves these problems. He explained that the platform is designed to support labeling workflows for different AI use cases, with features that affect data quality management, reporting, and analytics.

Since we created a truly horizontal solution, our customers represent a variety of industries. Our customers include both small start-ups and several Fortune 100 companies. [More than 100 thousand data processing specialists around the world have switched to our platform, "Malyuk said, refusing to report revenue figures. Our customers create internal data annotation teams and buy our product because their AI production models do not work well, and they understand that the main reason is the poor quality of the source data.^[1]

Notes