Encord Secures $30M in Series C Funding to Enhance Data Annotation for AI
Encord, a data development platform specializing in preparing and managing data for AI models, has raised $30 million in a Series C funding round led by Next47. This brings Encord’s total funding to $50 million, positioning the company to expand its operations significantly.
Founded by Eric Landau and Ulrik Hansen, Encord addresses the crucial but often overlooked task of data annotation—an essential step in training AI models. Properly labelled data allows AI systems to interpret and learn from the information. With the growing complexity of AI models, the demand for efficient data labelling has surged, making platforms like Encord increasingly vital.
The new capital will be used to double Encord’s product, engineering, and AI research teams over the next six months, as well as to expand the company’s presence in San Francisco. Encord, which now operates dual headquarters in London and San Francisco, plans to grow its team from 70 to 100 employees by the end of the year.
Encord’s platform stands out in the crowded data annotation market by offering a versatile solution that allows teams to explore, visualize, and compare datasets, including image, video, and voice data. The platform also detects model accuracy issues and suggests additional training data to improve model performance, providing a consolidated workflow that offers transparency into AI model decision-making processes.
Since its launch during Y Combinator in 2021, Encord has gained traction with 120 customers, including high-profile names like Philips, AI startup Synthesia, and healthcare providers such as Cedars-Sinai and Northwell Health. Encord has also secured contracts with various military and government agencies. The company reports a 4x increase in revenue over the past year and anticipates becoming cash-flow positive by 2025, despite its ongoing growth.
Other participants in the funding round included Y Combinator, CRV, and Crane Venture Partners.
Encord’s success highlights the growing importance of robust data development tools in the AI ecosystem, particularly as the demand for more sophisticated and accurate AI models continues to rise.
