UPDATED 09:00 EDT / APRIL 26 2023

AI

AiXplain raises $8M for low-code/no-code AI app development platform

AiXplain Inc., the maker of a low-code/no-code artificial intelligence app development platform, said today that the company has raised $8 million in seed funding led by Transform VC and Calibrate Ventures to continue building out features.

Using aiXplain’s platform, users can quickly develop, manage, benchmark and deploy AI applications with one click using an integrated development environment for end-to-end delivery with a graphical interface. Customers have access to a growing number of AI assets including data, models, pipelines and benchmarks for even more advanced AI applications and a marketplace of third-party assets from OpenAI LP, Google LLC, Amazon Web Services Inc., Microsoft Corp.’s Azure and other technology providers.

Hassan Sawaf, founder and chief executive of aiXplain, founded the company in June 2020 with the mission to provide a powerful tool that democratizes the building of AI applications even for nontechnical users.

“A friend compared us to ‘Figma’ for the design world: Where Adobe’s products were the de-facto standard for designers, Figma enabled ‘the rest of us’ to do a good job for a first pass, sometimes more than just ‘good enough,’” Sawaf told SiliconANGLE. “AiXplain does the same for the AI world: tools to make everyone be able to develop an AI solution that knocks off one’s socks. Or better yet, helps reduce costs, improve decision making, or enable business that would otherwise not be possible.”

AiXplain’s marketplace already contains more than 35,000 ready-to-use AI assets from numerous suppliers that allow developers to construct many use cases easily. This includes applications that need advanced speech and language interfaces, such as a specialized “CoPilot” for insurance workers supporting customers with complex claim cases or providing large language models for healthcare to help handle speech recognition and language understanding for doctor-patient conversations.

The platform’s asset library opens up a large variety of use cases from speech recognition, question answering, translation and summarization, as well as object recognition in images and video. This gives it a wide variety of opportunities across numerous industries and aiXplain and its network are constantly adding more AI assets and use cases.

Sawaf explained that as AI continues to get “more and more ready for prime time” it’s being used for more general use cases such as web searches or applications used frequently such as web search. But developing specialized used cases still requires AI specialists to work with people with specific skills to achieve goals to make a difference.

“AiXplain puts the right tools in the hands of a domain specialist, and takes the first shot to develop their own solution, often with the results they strive for,” Sawaf said. “Should they need even more refinement and optimization, aiXplain can connect them to a network of the right specialists that will work with the domain specialist on this solution, that then is deployed and hosted on aiXplain.”

Over the past year, the use of aiXplain has grown among high-growth technology companies and Fortune 500 enterprises to provide them easy-to-use AI application development. One example is Translated, a company that uses human translators and machine intelligence for language localization and machine translation services for more than 200,000 customers worldwide.

“Thanks to our deep partnership with aiXplain, we continue to expand our leadership position in the field of advanced natural language processing,” said Marco Trombetti, founder and chief executive of Translated. “AiXplain’s enterprise-strength platform enables us to power faster and more profitable global expansions for companies such as Airbnb and Uber.”

Sawaf said that the funding will go to expanding the features of aiXplain’s platform, which currently offers more than 30 functions. This includes adding functionality to the “Benchmark” capability, which allows users to analyze their models to optimize them for quality, latency, cost and bias.

Images: Pixabay, aiXplain

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