AI-Powered Speech-to-Text Solution Boosts Investigation Accuracy by 80% 

About the Client 

Our client is a premier global health non-profit headquartered in Geneva, Switzerland, investing over $4.5 billion annually to combat HIV, tuberculosis, and malaria in over 100 countries. To ensure that these funds are used effectively, a dedicated audit and investigation unit manages whistleblower reports and conducts confidential investigations to uphold accountability in the process of utilizing grants.

Business Challenge 

Manual transcription processes threatened data security and slowed investigations 

The organization’s procedure for investigating whistle-blower reports involves confidential audio interviews that must be accurately transcribed into text for evidence and further analysis. However, the manual transcription process posed significant challenges. Not only did it expose sensitive investigation details to privacy risks, but it also struggled to accurately interpret diverse global accents and specialized jargon, leading to frequent transcription errors. This painstakingly slow process took 2-3 days for each case registered, thereby impeding timely decision-making and reducing operational efficiency. As case volumes grew, inconsistent transcription quality further threatened the reliability of vital evidence.  

The situation demanded a secure, scalable, and highly accurate AI-powered transcription solution that could speed up investigations while protecting sensitive data and supporting increased workloads. 

The Zuci Solution 

Built a secure AI transcription platform with confidentiality at its core  

Zuci engineered a custom end-to-end speech-to-text platform for our client’s investigation team, designed to securely convert confidential audio interviews into accurate, actionable transcripts. The platform is architected as a streamlined four-layer pipeline, each layer addressing critical aspects of security, data validation and advanced machine learning, and user-friendly output. 

Empowered investigators with an intuitive, secure interface 
The audit team uses a Streamlit web interface on Azure to seamlessly upload audio files, configure transcription options, and ensure privacy controls at every step, simplifying evidence handling while maintaining data integrity. 

Ensured end-to-end data validation and compliance 
Every file and metadata entry is rigorously checked for format and compliance before processing. This automated validation not only prevents errors but guarantees that only authorized and compliant data proceeds, minimizing security risks. 

Enhanced transcription quality with advanced machine learning 
At the heart of the pipeline, Zuci’s integration of Azure Speech-to-Text, custom-enhanced in Python, delivers high-precision, multi-speaker transcription with advanced accent recognition. Automated speaker tagging and timestamping equip investigators for more reliable evidence analysis. 

Facilitated privacy-first delivery and instant results 
Transcripts are generated with 80% higher accuracy and delivered in hours, not days, via the secure interface or email. Flexible options to delete files and transcripts ensure full control over sensitive data, maintaining the highest privacy standards. 

Sincere thanks for your team’s outstanding work on this challenging project. Your commitment to quality and timely delivery was truly appreciated. Special thanks to Thowfeek for making this collaboration possible—we look forward to more successful projects together!

Head of IT Analytics

Global Healthcare Non-Profit