Multifunctional antibodies (e.g., dual binding, pH-dependence, bispecific antibody internalization) are increasingly critical in therapeutics but lack scalable rational design methods. We will present a unique integration of generative AI protein design with high-throughput digital biotechnology in a data-rich finetuning manner. In an dual-binding antibody case study, we designed dual-binding antibodies which achieved sub-picomolar potency against two receptors, exceeding current best-in-class candidates by over 20-fold, with early-stage functional metrics outperforming clinical benchmarks. The entire process, from design to recombinant characterization, was completed in under 9 weeks, enabling parallel developability with minimal experimental overhead. Additional case studies for functional antibody discovery including pH dependence and bispecific ADCs will be presented. These results highlight data-driven functional design as a scalable, practical approach for next-generation therapeutics.