There’s some new technology shift in the air and it seems like companies need to think about how to use large language models at work, but as with any new cutting-edge technology, this is often easier said than done, especially for the less technical. savvy organizations.
AirOps, an early-stage startup, is in the right place at the right time to help companies take advantage of these new opportunities to build AI-enabled applications based on large language models. The company today announced that it has received $7 million in a seed round that was effectively closed at the start of last year.
The company’s CEO and co-founder, Alex Halliday, says that with the recent interest in LLMs, there is a challenge for businesses trying to get into the new technology race. “There’s a really big gap between these amazing features that people can play with in things like ChatGPT and then [using those features] to solve some of the toughest business problems. So we’re building a platform that allows people to come in and create custom solutions based on these algorithms that really change the business,” Holliday said .
The company currently helps clients build applications based on three LLM algorithms: GPT-4, GPT-3, and Claude. The idea is to help users with things like automating processes, extracting information from data, creating personalized content, and performing natural language processing techniques.
Halliday says current clients are looking for ways to use their own data and content in conjunction with LLM to create new content from an existing corpus or build generative AI on top of existing software.
One of the company’s main offerings is to help customers use these models more efficiently and effectively, as this can be costly. “What’s really interesting is that you can use large models to train smaller models. So maybe for the first couple of months you will use GPT-4 and it will give you learning results to then use a smaller open source model that has been fine-tuned,” he said.
And AirOps can help you get through these stages. “We’re really learning the right recipes and architectures, but we expect that over time the impracticable ‘let’s boil the ocean’ approach will give way to a finer and better understanding of how to take advantage of the choices that people have,” he said.
The company started last year with the goal of helping people get value from their organizational data, but as LLMs went public, the company shifted focus. “When we started looking at applying LLM to the data space, we realized that there was actually a much broader opportunity to help people combine LLM with their data to create custom workflows and applications,” he said. Last fall, they really shifted their focus to this approach.
The company has 14 employees and there are several vacancies. The $7 million seed investment was led by Wing VC with contributions from Founder Collective, XFund, Village Global, Apollo Projects and Lachy Groom.
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