ASAP (Assemble Species by Automatic Partitioning) is a new method for species delimitation from single-locus sequence alignments, or barcode data sets. It is designed to be efficient, using only pairwise genetic distances and avoiding the computational burden of phylogenetic reconstruction. ASAP employs a hierarchical clustering algorithm that ranks partitions based on a new scoring system, which combines the probability of groups being panmictic species and the width of the barcode gap. The method is implemented as a stand-alone program, available through a graphical web interface or downloadable for local use. ASAP was evaluated on 10 real COI barcode data sets and compared with three other methods (ABGD, PTP, and GMYC). Through these analyses, ASAP demonstrated its potential as a tool for taxonomists, providing rapid and relevant species hypotheses as a first step in integrative taxonomy. The method is particularly useful for large data sets, with the ability to process up to 10^4 sequences in minutes. ASAP overcomes the limitations of ABGD by eliminating the need for an a priori defined $P$ value and providing a scoring system for partitions. The graphical output of ASAP includes a list of ranked partitions, a plot of the asap-score, an ultrametric clustering tree, and a "boxed-species" graph. The authors recommend using ASAP as a first step in species delimitation, followed by evaluation with other methods and lines of evidence to ensure robustness.ASAP (Assemble Species by Automatic Partitioning) is a new method for species delimitation from single-locus sequence alignments, or barcode data sets. It is designed to be efficient, using only pairwise genetic distances and avoiding the computational burden of phylogenetic reconstruction. ASAP employs a hierarchical clustering algorithm that ranks partitions based on a new scoring system, which combines the probability of groups being panmictic species and the width of the barcode gap. The method is implemented as a stand-alone program, available through a graphical web interface or downloadable for local use. ASAP was evaluated on 10 real COI barcode data sets and compared with three other methods (ABGD, PTP, and GMYC). Through these analyses, ASAP demonstrated its potential as a tool for taxonomists, providing rapid and relevant species hypotheses as a first step in integrative taxonomy. The method is particularly useful for large data sets, with the ability to process up to 10^4 sequences in minutes. ASAP overcomes the limitations of ABGD by eliminating the need for an a priori defined $P$ value and providing a scoring system for partitions. The graphical output of ASAP includes a list of ranked partitions, a plot of the asap-score, an ultrametric clustering tree, and a "boxed-species" graph. The authors recommend using ASAP as a first step in species delimitation, followed by evaluation with other methods and lines of evidence to ensure robustness.