Rare diseases can remain undiagnosed for years because individual disorders are uncommon and clinically heterogeneous, while relevant clues are distributed across structured phenotypes, facial appearance, prior cases and disease knowledge. Computational tools can use Human Phenotype Ontology (HPO)-encoded findings, genomic data and increasingly diverse medical images, but these modalities are processed by models with different outputs and score scales. ZebraSeek converts clinical and facial inputs into ranked disease candidates with specialist tools, then uses a large language model (LLM) to integrateand externally check those candidates while retaining their sources. Facial-image analysis provides a complementary machine-readable phenotype that can capture patterns notfully represented by manually encoded HPO terms. In 74 literature-derived cases spanning 19 diseases, ZebraSeek placed the recorded diagnosis first in 50 (67.6%) and within five in 59 (79.7%); it recovered 11 diagnoses outside PubCaseFinder’s top five, while five diagnoses present in PubCaseFinder’s top five were absent from ZebraSeek’s final top five. During DBCLS BioHackathon 2026, we expanded analysis to 368 patients, 462 images and 54 disorders to examine how candidate depth affects coverage and downstream candidate count, and to assess the feasibility of case-specific candidate selection.