Speech-to-text automatic punctuation systems aim to enhance the readability of transcripts by converting raw audio into text with correct punctuation marks like commas, periods, and question marks, which speakers do not explicitly articulate. While demos often showcase seamless integration with clean audio, real-world applications face challenges such as background noise, multiple speakers, and regional accents that disrupt the acoustic and linguistic signals essential for accurate punctuation. These systems employ dual engines: one for detecting acoustic cues like pauses and intonations, and another for contextual language understanding to achieve higher accuracy, with batch processing often outperforming streaming due to access to complete audio context. In production, achieving stable punctuation involves a structured pipeline that includes recording, ingestion, speech-to-text conversion, punctuation, formatting, and storage, with additional considerations for domain-specific scenarios in healthcare and finance where precision is critical. Quality measurement extends beyond Word Error Rate (WER) to Punctuation Error Rate (PER), ensuring that transcripts are not only accurate but also readable, while trade-offs in cost, latency, and accuracy are managed through strategic choices between real-time and batch processing. The systems must be robust enough to handle the complexities of real-world speech, and integration involves rigorous validation and compliance checks to ensure reliability and accuracy in diverse applications.