In today's digital era, the adoption of cutting-edge technologies like AI and Generative AI presents a wealth of benefits across industries and countries. It's anticipated to significantly boost worker productivity and add trillions to the global economy. But as organizations position themselves to integrate AI, they often encounter challenges in data capture.
Turning to the modality of speech, the Israeli startup aiOla has pioneered the use of NLU (Natural Language Understanding) and ASR (Automatic Speech Recognition) technologies, aiming to equip businesses with the capability to close the data capture gap, streamlining processes and enhancing operational efficiency.
At its core, aiOla's patented speech AI solution is capable of understanding over 100 languages, filtering through ambient noise, and adapting to various accents and terminologies. Their approach saves time and resources, and empowers a global workforce to operate in their native languages. The startup was founded in 2020 by Guy Ernst and Amir Haramaty, and has attracted over $33 million in VC backing, having raised its Series A in 2022. In our interview with CEO, Haramaty, we explored the vision behind aiOla, exploring the challenges it aims to solve, its impact on the industry, and the future it envisions for speech recognition technology in the business world.
Your journey into the AI and big data realms predates the current explosion of interest. What sparked your interest?
I’ve been immersed in the tech and the big data field for years. Over the past decade, I’ve been deeply involved with AI, well before they became the most used and abused letters in the world. My previous company was the AI platform of choice of one of the leading global management consulting companies where I had the opportunity to engage with some of the leading enterprises in the world.
Through these experiences, I came to understand that while AI and ML are incredible tools, they are just tools and the most important element is data. Separately, only a few, mainly data scientists, were actually benefiting from AI and therefore I wanted to go after the uncaptured and unstructured data that was missing while connecting the promise of AI to the masses.