Neo is a technology company operating in the real-time communication and translation industry. Their core product is a real-time AI-driven speech translation assistant that enables multilingual conversations by transcribing live speech, translating it into a target language, and converting it back into natural-sounding audio. This innovation empowers users across linguistic backgrounds to communicate fluidly in meetings, events, and live interactions without the need for human interpreters, significantly improving accessibility and inclusivity.
Customer Challenge
Neo’s primary challenge was delivering real-time multilingual voice communication that felt natural, scalable, and reliable across a range of industries, including customer support, healthcare, and education. The customer’s previous solutions introduced excessive latency—often exceeding five seconds—and produced robotic voice outputs that disrupted the conversational flow. These shortcomings hindered user satisfaction, limited the effectiveness of global communication efforts, and threatened the scalability of their product. Additionally, Neo required a system capable of maintaining the emotional tone and contextual accuracy of speech, which is critical for conveying intent in high-stakes conversations. Without a robust solution, the customer faced the risk of increased churn, elevated operational costs due to manual translation workflows, and declining engagement in competitive markets demanding language inclusivity.
Solution
White Stork partnered with Neo to develop a scalable, low-latency, and context-aware real-time speech translation pipeline using AWS technologies. The solution integrates Amazon Transcribe for accurate speech-to-text conversion with multilingual and speaker recognition support. Amazon Translate handles fast neural machine translation for straightforward content, while Amazon Bedrock, leveraging foundation models such as Claude 3.5 and Nova Pro, provides context-aware translation that preserves tone, emotional nuance, and idiomatic expressions. The translated content is then processed through Amazon Polly to generate human-like voice responses using neural text-to-speech (TTS) models, ensuring clarity and natural delivery. The architecture is deployed on Amazon ECS for scalable, containerized orchestration, supported by EC2, AWS Lambda, and Amazon CloudWatch for performance monitoring and reliability. To improve performance under varied acoustic conditions and dialects, additional transcription engines like DeepGram and Speechmatics were selectively integrated. The system uses modular components and asynchronous streaming to maintain a latency of under three seconds while supporting multiple languages.
Results and Benefits
The deployment of the AI-driven translation assistant resulted in a dramatic 80% reduction in costs compared to traditional interpreter-based services. Neo’s new platform now achieves an average end-to-end latency of less than three seconds, enabling truly seamless multilingual communication. Adoption increased by 45% among organizations with global teams, while user satisfaction improved significantly due to the natural, expressive voice output and real-time responsiveness. The solution supports multiple languages with zero additional human overhead, enabling Neo to scale across multiple industries and regions efficiently. In business settings, meeting durations decreased and engagement improved, as participants could converse without interruption or confusion. Customer support teams were able to deliver native-language assistance on demand, improving service quality and retention. Overall, the solution not only enhanced accessibility but also allowed Neo to bring a highly differentiated and scalable product to market more quickly and cost-effectively.