Unlocking Large Language Models with LaunchDarkly & Vercel's AI Gateway

Building a Production LLM Data Extraction Pipeline As machine learning (ML) models become increasingly pervasive in our industry, we're faced with an intriguing challenge: extracting meaningful data from unstructured text. In this article, we'll explore how to build a production-ready data extraction pipeline using LaunchDarkly and Vercel AI Gateway. The Problem of Unstructured Text Every conversation your organization has contains signals that your ML models need: Customer calls reveal buying intent Support tickets expose product friction Interview transcripts capture technical depth However, these signals are buried in thousands of words of unstructured text. This is where traditional tools like Gong and Chorus shine, but they're not designed for extracting specific features with a custom schema. Requirements for a Production-Ready Pipeline To extract meaningful data from unstructured text, our pipeline should meet the following requirements: Scalability : Handle large volumes of tex