RAIN Raises $11M for Custom Industrial Voice Assistants

Voice AI startup RAIN Technology has raised $11 million in a Series B funding round led by Valor Capital. The new investment will accelerate the company’s work on devising custom voice assistants for commercial and industrial fields, starting with vehicle service.

RAIN Sounds

RAIN began working on developing conversational AI for enterprise purposes at the end of 2020 when it raised $3 million from Stanley Ventures, the investor branch of Stanley Black & Decker. The idea is to compile voice assistants specifically for what RAIN calls the “deskless workforce.” The voice assistant might answer a question about measurements for a construction worker, explain the steps to repair a car, or list ingredients for the special of the day for a fast-food line cook. RAIN aims to increase productivity by making all that information available by asking a voice assistant instead of requiring paging through a digital or paper manual for answers.

“We made a strategic decision a couple of years ago to focus on custom assistants and worker productivity solutions,” RAIN CEO Nithya Thadani told Voicebot in an interview. “Often, when doing their work, people are pulled out of their workflow [to look up information]. We’re building an in-house voice search tool.”

The Stanley Ventures support makes for an obvious connection to the construction industry, as well as car repair. A voice request skips a lot of tedious steps hunting through a manual to get the right answer. Thadani also hinted at plans for quick-service restaurants and healthcare providers, including the U.S. Department of Veterans Affairs. The voice assistant RAIN has designed will live in a “ruggedized” tablet, meaning one that can survive an industrial environment and one with a magnetized back to stick to any nearby girders or pillars. A mobile app is also under consideration by RAIN, but Thadani described the tough tablet as the primary form. Hard devices and hard answers are central to RAIN’s approach.

“There are two things you need. One is a single source of truth on the data. There has to be a right answer. We want to make sure there’s no dispute. In construction, measurement or conversion questions have a [definite] answer versus asking about a subjective deadline for a project,” Thadani said. “The second piece needed is a stationary asset with constrained context, meaning that in automobiles, you’ve got a vehicle that’s being worked on [with unique repair specs]. Having tight context around that vehicle makes it possible to ask questions of it in an efficient natural way.”

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