Background Clinical trials are essential to advancing cancer treatments, yet fewer than 10% of adults with cancer enroll in trials, and many studies fail to meet accrual targets. Artificial intelligence (AI) could improve identification of appropriate trials for patients, but sharing AI models trained on protected health information remains difficult due to privacy restrictions. Methods We developed MatchMiner-AI, an open-source platform for clinical trial search and ranking trained entirely on synthetic electronic health record (EHR) data. The system extracts core clinical criteria from longitudinal EHR text and embeds patient summaries and trial "spaces" (target populations) in a shared vector space for rapid retrieval. It then applies custom text classifiers to assess whether each patient-trial pairing is a clinically reasonable consideration. The pipeline was evaluated on real clinical data. Results Across retrospective evaluations on real EHR data, the fine-tuned pipeline outperformed baseline text-embedding approaches. For trial-enrolled patients, 90% of the top 20 recommended trials were relevant matches (compared to 17% for the baseline model). Similar improvements were noted for patients who received standard-of-care treatments (88% of the top 20 matches were relevant, compared to 14% for baseline). Text classification modules demonstrated strong discrimination (AUROC 0.94-0.98) for evaluating candidate patient-trial space pair eligibility; incorporating these components consistently increased mean average precision to ~ 0.90 across patient- and trial-centric use cases. Synthetic training data, model weights, inference tools, and demonstration frontends are publicly available. Conclusions MatchMiner-AI demonstrates an openly accessible, privacy-preserving approach to distilling a clinical trial matching AI pipeline from LLM-generated synthetic EHR data.