Non-profit industry need a system for accurately matching fund-seekers (e.g., AMERICAN NATIONAL RED CROSS) with fund-givers (e.g., BILL AND MELINDA GATES FOUNDATION) aligned in cause (e.g., cancer) and target beneficiary group (e.g., children). In this paper, we create an enterprise search system “ANGEL” for the non-profit industry that takes a fund-giver’s mission description as input and returns a ranked list of fund-seekers as output, and vice-versa. ANGEL employs ColBERT, a neural information retrieval model, which we enhance by exploiting the two techniques of (a) Syntax-aware local attention (SLA) to combine syntactic information in the mission description with multi-head self-attention and (b) Dense Pseudo Relevance Feedback (DPRF) for augmentation of short mission descriptions. We create a mapping dictionary “non-profit-dict” to curate a “non-profit-search database” containing information on 594K fund-givers and 194K fund-seekers from IRS-990 filings for the non-profit industry search engines . We also curate a “non-profit-evaluation” dataset containing scored matching between 463 fund-givers and 100 fund-seekers. The research is in collaboration with a philanthropic startup that identifies itself as an “AI matching platform, fundraising assistant, and philanthropy search base.” Domain experts at the philanthropic startup annotate the non-profit evaluation dataset and continuously evaluate the performance of ANGEL. ANGEL achieves an improvement of 0.14 MAP@10 and 0.16 MRR@10 over the state-of-the-art baseline on the non-profit evaluation dataset. To the best of our knowledge, ours is the first effort at building an enterprise search engine based on neural information retrieval for the non-profit industry.