BackSearch – Letting agents search the web (www.gr.inc)

BackSearch has been launched as a specialized tool for letting agents, enabling them to search a stable version of the web as it existed at specific past dates. This innovative approach addresses a significant challenge within the AI and machine learning community: the need for reliable historical data while avoiding the pitfalls of live search APIs that provide updated content, which can lead to errors in predictive modeling. By offering two endpoints—one for searching and another for fetching archived articles—BackSearch ensures that users retrieve only documents that existed on or before a designated date, thereby eliminating potential data leakage associated with real-time content. The significance of BackSearch lies in its ability to facilitate backtesting for language models and agents, allowing researchers to assess model performance against known historical outcomes. This could enhance applications in quantitative finance, enabling robust evaluations of strategies based on concrete historical data. The initial release focuses on news domains, spanning from December 2025 to July 2026, with plans for broader coverage in response to user feedback. The tool also features a cost-effective pay-as-you-go pricing model, expanding its accessibility for researchers and developers looking to utilize archived web data for training and evaluation purposes.