Hyro (from LinkedIn)
Hyro, an Israeli startup that is developing a no-code platform for AI-powered call center, web, and mobile solutions, raised $20 million in a $20 million Series B funding round led by Macquarie Capital. The company has now raised $35 million to date.
Everyone is talking about AI – artificial intelligence – these days. OpenAI ChatGPT, which is now as controversial as it is popular. How will teachers ever know if their students are writing their own papers, or if they used an AI program? Hollywood screenwriters are currently on strike because they fear being replaced by programs such as ChatGPT. How do you know that an actual human being wrote this article or if it was generated by a banana?
(Disclaimer: No this was not written by either a banana or an AI program, but you get the idea.)
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Founded in 2018 by CEO Israel Krush and Rom Cohen, Hyro says it is connecting the world, thousands of AI-powered conversations at a time. The firm boasts that it turns complex data into simple dialogue with natural language automation and computational linguistics. Enterprises across the U.S., including Baptist Health, Mercy Health and Rent.com, trust Hyro to replace their rigid, intent-based chatbots and IVR systems with adaptive conversational experiences that are simple, fast and scalable.
The company promises to help its clients to reduce their workloads while “saving” their workforces. But what do they mean by “save.” Will firms like Hyro make even more people redundant by replacing them with machines leaving former skilled professionals to work as baristas at a coffee bar?
Hyro declares that it offers AI assistants customers need to resolve over 85% of repetitive tasks and tells its potential clients, “meet your digital workforce.”
“At Hyro we employed a wholly unique approach to natural language interfaces from day one,” said Israel Krush. “While many of our competitors and peers chose to tackle this challenge by building elaborate data pipelines, recruiting armies of data scientists, and constructing their own in-house natural language architectures (that are now being supplanted by an influx of new LLMS), we chose a much more adaptive approach.”
