The startup has also released computer vision as a service, enabling organisations to use its platform for image detection, classification, and segmentation
Abacus.AI has developed an end-to-end AI platform. (Credit: Gerd Altmann from Pixabay)
Abacus.AI, a US-based end-to-end artificial intelligence (AI) platform, has raised $50m in a Series C funding round led by investment firm Tiger Global.
The financing round also saw the participation of Coatue, Alkeon Capital, and Index Ventures.
Headquartered in San Francisco, Abacus.AI has designed a cloud AI platform to handle all aspects of machine learning (ML) and deep learning at an enterprise scale.
The company’s end-to-end autonomous AI service, which can be customizable, imparts training to machine and deep learning models for common enterprise AI use cases.
Abacus.AI has secured $90.3m in total funding in 30 months. This includes $22m raised by the company in a Series B round in November 2020 that was led by Coatue, a technology-focused investment manager.
The company intends to use the proceeds from the Series C round to further improve its end-to-end AI platform.
Its platform is said to be used by organisations of different sizes, ranging from small startups to Fortune 500 firms to expedite their AI adoption.
Alkeon Capital principal Josh Shirazi said: “Abacus.AI is democratising the use of AI by offering an end-to-end platform with the greatest breadth of domain-specific use cases.
“With the launch of computer vision use-cases, Abacus.AI is unlocking AI on unstructured data sets, amplifying the value customers already realise by discovering incremental revenue opportunities, saving costs, gaining productivity, and increasing NPS.
“We’re thrilled to collaborate with one of the strongest AI/ML teams, who has built systems at scale across their experiences at Google, Amazon, and Uber.”
Apart from the Series C round, the Abacus.AI released computer vision as a service. The company said that organisations can now use its platform for common computer vision use-cases such as image detection, classification, and segmentation.
The intuitive user experience (UX) of the company is said to help enterprises easily develop customised, deep-learning-based computer vision models by applying transfer learning.
Additionally, the company’s platform now supports language and images on all of its tabular data use-cases by synthesising insights from all types of data.