TL;DR:
- AirOps, an early-stage startup, has closed a $7 million seed round.
- The company helps businesses utilize large language models (LLMs) to build AI-enabled applications.
- AirOps assists with automating processes, extracting insights from data, generating personalized content, and performing natural language processing.
- Customers can leverage their own data and content alongside LLMs to create custom workflows and applications.
- AirOps aims to help customers use LLMs more efficiently and cost-effectively.
- The company plans to move away from a one-size-fits-all approach to a more nuanced understanding of LLM utilization.
- AirOps has 14 employees and aims to build a diverse team.
- The seed investment was led by Wing VC, with participation from other investors.
Main AI News:
AirOps, an early-stage startup, has recently announced the closure of a $7 million seed round, which took place at the beginning of last year. The timing of this funding couldn’t be more opportune, as there is a palpable shift in the air, with companies increasingly recognizing the need to harness the potential of large language models (LLMs). However, for many organizations with limited technical expertise, putting these advanced technologies to work can be easier said than done.
Enter AirOps. Positioned at the right place at the right time, this startup is poised to help companies seize the benefits offered by LLMs and build AI-enabled applications on top of them. Alex Halliday, the CEO, and co-founder of AirOps, acknowledges the challenge faced by businesses in adopting LLMs.
He states, “There is a really large gap to close between these amazing capabilities that folks can play with in things like ChatGPT, and then [applying that] to the kind of hardest challenges in the business. So we’re creating a platform that lets folks come in and create custom solutions on top of these algorithms that really move numbers in the business,” he shared with TechCrunch.
The core focus of AirOps is to assist its customers in constructing applications that leverage the power of three LLMs: GPT-4, GPT-3, and Claude. The goal is to enable users to automate processes, extract valuable insights from data, generate personalized content, and perform natural language processing techniques, among other capabilities.
Halliday further explains that current customers are keen on leveraging their own data and content in conjunction with LLMs to create new content or develop generative AI experiences on top of their existing software.
One of the main value propositions of AirOps lies in its ability to help customers utilize LLMs more efficiently and effectively, considering the potentially high costs involved. Halliday highlights an interesting approach where larger models, such as GPT-4, can be used initially to train smaller, fine-tuned models. This strategy allows organizations to benefit from the power of LLMs while optimizing their resources.
As Halliday puts it, “What’s kind of really interesting is that you can actually use the larger models to train smaller models. So maybe for the first couple of months, you would run using GPT-4, and that would create the training outputs to then use a smaller, open-source model that’s been fine-tuned.”
AirOps is actively involved in refining the recipes and architectures necessary to extract maximum value from LLMs. Halliday expects that, over time, a more nuanced understanding of how to leverage these models will replace the current one-size-fits-all approach. This evolution will enable businesses to make more informed choices about how they utilize LLMs based on their specific needs and objectives.
While AirOps initially focused on helping organizations derive value from their data, they soon realized the broader potential of blending LLMs with data to create custom workflows and applications. As a result, they shifted their focus last fall towards this approach, aligning with the growing interest and awareness surrounding LLMs.
With a current team of 14 employees and a few open positions, Halliday emphasizes the importance of diversity as they continue to build their company. Given the relative novelty of LLMs, AirOps has taken an open-minded approach to hiring individuals from different backgrounds and levels of experience.
The recent $7 million seed investment was led by Wing VC, with participation from Founder Collective, XFund, Village Global, Apollo Projects, and Lachy Groom, demonstrating confidence in AirOps’ vision and potential.
Conlcusion:
AirOps securing a $7 million seed round and positioning itself as a provider of AI-enabled applications built on large language models (LLMs) signifies a significant shift in the market. The increasing interest and investment in leveraging LLMs for business challenges highlight the growing recognition of their potential value. AirOps’ platform addresses the gap between LLM capabilities and their practical application in real-world business scenarios, offering customers the ability to automate processes, extract insights, and generate personalized content.
By enabling more efficient and cost-effective utilization of LLMs, AirOps is poised to capitalize on the market demand for custom workflows and applications that blend LLMs with organizational data. This trend suggests that businesses are actively seeking ways to harness LLMs to drive tangible results, indicating a promising market opportunity for companies operating in the space.
