Enterprise

Lang.ai snags $2M to remove technical burden of implementing AI for businesses

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Image Credits: Lang.ai

Lang.ai, which has developed a no-code platform for businesses, closed on a $2 million seed funding round.

The company’s SaaS platform aims to allow business users to structure any free-text data with custom categories built through a drag & drop interface, based on AI-extracted concepts.

Village Global led the financing, which included participation from new and existing backers including Acceleprise, Oceans Ventures, Alumni Ventures Group, 2.12 Angels, GTMFund and Lorimer Ventures.

Spain-born Jorge Peñalva founded Lang.ai in 2018 with the goal of giving any business user the ability “to build enterprise-ready natural language processing models in just minutes.” It was built to give non-engineers a way to automate repetitive tasks in use cases such as customer service and claims processing.

“It can be installed in our cloud or theirs,” Peñalva said. 

Lang.ai saw its revenue double from the last quarter of 2020 to the first quarter of 2021 and the seed funding was motivated mainly to continue that momentum.

“We’re getting demand in the form of projects with our larger customers, so we needed the funding to be able to support that demand,” Peñalva told TechCrunch.

In his previous role of CEO of Séntisis, Peñalva realized that processes driven by free-text data remained a blind spot for many companies. 

“Today, millions of dollars and hours are invested by companies to manually read and process textual information captured from disparate areas of their business,” he said.

His mission with Lang.ai is to “empower businesses to put AI to work for them, without the technical complexities of building and training custom algorithms.” 

Specifically, Peñalva said that Lang.ai’s product analyzes a customer’s historical data “in minutes” and suggests AI-extracted concepts to build custom categories through a drag & drop interface. The custom categories are applied in real time to automate “tedious” tasks such as the manual tagging and routing of support tickets, the processing of insurance claims and the dispatching of field engineers to incoming work order requests.

Put simply, Lang.ai’s goal is to remove the technical burden of implementing AI for a business.

Lang.ai’s community of users (called “Citizen NLP Builders”) consists of mostly non-technical business roles, ranging from customer service operations to marketers, business analysts and UX designers.

Customers include Freshly, Userzoom, Playvox, Spain’s CaixaBank, Yalo Chat and Bancolombia, among others. 

Ben Segal, director of infrastructural efficiency at Freshly, described the platform as “so nimble.”

“Out of the box, it took us two days to make automated tagging 15% more reliable than a previous platform that we had had in production for two years, with the added benefit that now all of our teams can tap into and exploit our support data,” Segal said. “The marketing team has built workflows to understand key customer moments. Our data and analytics team is super excited about having all these new tags in Snowflake, and it’s crazy how easy it is to use.”

Peñalva is proud of the fact that Lang.ai’s engineering team is primarily based in Spain and that he has been able to grow the 10-person company outside of his native country.

“With very few resources, it took us a little over two years to build an enterprise-grade product and find the right set of early customers and investors who are aligned with our vision,” he said. “I moved to the U.S. from Spain to build a global company and this is just the beginning…Lang has always been powered by immigrant hustle, and it has been core to our values since day 1.”

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