AI-driven protein and antibody design platforms can rapidly generate large candidate sets, but timely experimental data remains a key limitation for effective lead identification and optimization. Here, we present an integrated sequence-to-data workflow that combines cell-free protein expression with high-throughput analytical and functional assays to generate decision-ready datasets within 4 calendar days. The workflow enables multi-dimensional characterization of candidate molecules, including affinity, stability, developability, polyspecificity, and FcRn binding. By accelerating the conversion of sequence inputs into experimentally grounded profiles, this platform supports candidate ranking, lead prioritization, model refinement, and iterative optimization in AI-driven antibody discovery.