The NASA Exoplanet Archive (NEA) is developing a suite of tools to speed up and streamline the process of getting data from published papers into ingestion-ready form. Manually locating relevant papers, extracting tabular and textual data, and reformatting it to archive standards is a major bottleneck in keeping the archive current. We describe three complementary efforts underway to address this in different parts of our pipeline, two of which utilize the capabilities of modern LLMs.

First, we have created a standardized template that authors can use to submit their published data directly in our required ingestion format, reducing the translation burden on archive staff and ensuring parameter accuracy. Second, we have built an AI tool that scans the weekly astronomical literature to identify papers most likely to contain data of interest for NEA, cutting down on manual literature triage. Third, we are developing an AI tool that parses the text and tabular content of identified papers and converts it into the structured format required for ingestion. Fourth, we are writing tools to facilitate bulk ingest of large sets of curated data, such as GAIA DR4.

Together, these tools aim to shorten the pipeline from publication to archive availability, reduce manual effort, and improve the completeness and timeliness of the NEA holdings. We will present the basic design of each tool, early results, and lessons learned in applying AI to a production data-curation workflow.