Case Study: Dialpad improves NLP and LLM signal quality by 20% with Labelbox

Dialpad improves signal quality by 20% and cuts cost per data point 48% with Labelbox

Dialpad, a provider of AI-driven customer engagement and communications, faced significant challenges with its previous data annotation provider. The vendor could not produce the high-quality training data required for Dialpad's NLP and LLM products, which handle tasks like transcription and sentiment analysis. This lack of quality slowed AI development and made data scientists reluctant to request the labeled data they needed. Dialpad turned to the Labelbox platform to address this problem.

By implementing Labelbox, Dialpad gained a software-first approach that delivered higher-quality training signal with less demand on its own data scientists. The results were substantial: after a year, Labelbox helped Dialpad achieve a 20% improvement in signal quality and a 41% reduction in costs per data point. This increase in accuracy and efficiency lowered expenses and boosted team productivity, enabling data scientists to proactively request the data they needed to scale AI development faster.

Dialpad