Developed a Transcription Latency Benchmarking Tool for the openchlai/ai repository, focusing on validating and monitoring transcription performance against sub-five-second latency targets. The solution leveraged Python scripting to implement a latency measurement workflow, capturing results and exporting them via CSV reporting for further analysis. Comprehensive command-line interface options and detailed documentation were provided to facilitate adoption and interpretation of benchmarking results. The tool enabled reproducible performance validation and supported ongoing data-driven optimization of the transcription pipeline. Work emphasized data validation and observability, strengthening SLA compliance and providing a foundation for continuous improvement in system responsiveness and reliability without addressing bug fixes.