I'm excited to share a new platform called AskHumans (askhumans.io), which aims to transform the way organizations gather customer feedback by leveraging GPT. The goal is to provide a more insightful and personalized alternative to the widely used but often limited Net Promoter Score (NPS).

Having launched startups before, a consistent challenge has been gathering meaningful customer feedback. We believe that large language models (LLMs) can enable personalized feedback at scale, which can be turned into weighted sentiment scores that offer more informative insights than traditional NPS scores.

Moreover, we think that the future advantage of firms with AI is not solely about the algorithms but also about the unique data sets these algorithms can analyze. By incentivizing customers to give open-ended feedback, organizations can create unique data streams that will become increasingly more valuable to them over time.

AskHumans' innovative tech stack includes React Native, Rails, and GPT for sentiment analysis (replacing IBM Watson). The platform allows organizations to ask open-ended questions to their customers or stakeholders, and collect verbal feedback. This feedback is then analyzed by AI to better understand service quality, product satisfaction, and customer experience at scale.

One regional coffee brand's district manager experienced the potential of AskHumans firsthand. Upon witnessing the sentiment analysis, he realized how much more comprehensive understanding of customer experiences he could gain compared to the limited information from his location's NPS score of 7.8.

We believe that the application of AI in customer feedback, combined with unique data sets, will usher in a new era of insights, allowing organizations to make better-informed decisions and enhance their customer experiences.

We'd love to hear your thoughts on the potential of AI-driven customer feedback, the tech stack behind AskHumans, or your own experiences with NPS scores. If you have any questions or comments, please share them below.