A Seattle-based startup recently raised $6.7 million in its seed funding round. The funds are earmarked for helping businesses cut costs on Large Language Models (LLMs) usage. Costanoa Ventures led the round, with participation from Y Combinator and private investors. The technology developed by the startup allows businesses to use LLMs more efficiently, lowering costs.
The founders of the startup, David and Kyle Corbitt, are determined to decrease costs and simplify processes related to LLM deployment and training. They aim to provide developers with tools to create customized LLM models. They believe that smaller, specialized models can achieve higher efficiency and lower costs compared to larger, general models.
The company suggests that using more simplified AI systems that deeply understand a business’s policies and product line, rather than complex AI systems, can improve customer service and enhance other business functions. By doing so, customer retention and satisfaction rates can potentially increase.
Seattle startup’s funding boosts AI efficiency
The enterprise is aiming to implement its system across different business operations for improved effectiveness and productivity.
For example, a financial firm can use AI to examine call transcripts and extract essential data, resulting in cost reductions and fewer errors. These processes demonstrate how AI can optimise various business operations and inspire further adoption of the technology.
Users don’t need extensive machine learning or data science knowledge to use the system, according to Kyle Corbitt. Many of their users, primarily full-stack app developers, have successfully trained robust models without needing external knowledge. This simplifies the model training process and reduces the steep learning curve typically associated with the field.
Before the current startup, Kyle spent his time working at a family history startup, Emberall, and had an engineering role at Y Combinator, where he organised the Startup School for its community of founders. His diverse career experience undoubtedly enriches the projects he is involved in.
Even though the startup has not revealed its income statistics, it has experienced considerable growth since its establishment a year ago. Its primary revenue comes from clients who pay for the fine-tuning of models and their application in real-life situations. The startup is always eager to discover new ways to satisfy diverse market demands.
