The project is a long-term collaboration with a technology company that delivers real-time AI-powered speech interpretation, captioning, and translation services for live events, conferences, and broadcasts.
The platform ingests live audio streams and converts them into real-time captions and multilingual translations, combining speech recognition, text-to-speech, and translation providers into a unified pipeline. As the product matured, the client brought us in to help implement, optimize, and maintain the backend infrastructure powering these workflows.
Our work centered on the .NET-based services handling audio stream processing, live caption generation, translation pipelines, and inter-component communication across a distributed system. The engagement spanned modernizing legacy infrastructure, hardening system reliability, and supporting the client's broader shift from fully human-led interpretation toward AI-assisted workflows.
Latency and reliability were the core product priorities. The infrastructure had to continuously ingest live audio streams, generate captions on the fly, and sustain multilingual translation workflows across conferences, broadcasts, and large-scale live events.
Over time, the system became increasingly difficult to maintain and scale. Parts of the platform still relied on legacy infrastructure, and a growing number of integrations with external speech recognition and translation providers added operational complexity at every layer. On top of that, the system had to absorb high traffic volumes without degrading performance or disrupting live event delivery.
The deeper technical challenge was keeping latency low and processing stable across distributed services. External transcription providers could return inconsistent or malformed responses, making reliable caption generation during live sessions difficult to guarantee.
Meanwhile, the platform had to handle multiple languages, ensure dependable event delivery between services, and recover gracefully from connection or processing failures, all without interrupting the live stream.
All in all, the client needed a backend that could grow with them: resilient, observable, and ready to support AI-powered interpretation at scale.
We joined the project to strengthen and modernize the backend infrastructure behind real-time audio processing, live caption generation, and multilingual translation workflows. Scalability, resilience, and operational stability across the platform's distributed services were the main focus.
Our team started by taking a close look at the existing streaming and transcription architecture to map out performance bottlenecks, reliability gaps, and constraints left behind by legacy infrastructure. Rather than replacing everything at once, we approached modernization incrementally: reducing operational risk while keeping live event support uninterrupted throughout.
A core part of that work was overhauling the platform's event-driven communication layer. We improved real-time messaging and stream processing pipelines built on RabbitMQ and .NET services, making inter-service communication more reliable and significantly reducing the risk of delayed or dropped events during high-traffic sessions.
The platform leaned heavily on external speech recognition and translation providers like Microsoft Speech Services, Speechmatics, Amazon, and other cloud APIs. To make those integrations robust, we built smarter retry and reconnection logic capable of absorbing unstable or inconsistent provider responses without letting disruptions bubble up to live captioning workflows.
We migrated several backend services to newer .NET and ASP.NET Core versions, and improved deployment consistency and infrastructure maintainability through Docker-based containerization and AWS infrastructure services.
Observability got serious attention as well. We introduced structured logging with Serilog, tightened monitoring and error handling flows, and added integration testing with containerized dependencies, giving the team greater confidence in deployments and a cleaner path to troubleshooting production issues.
The result was a meaningfully more scalable and fault-tolerant backend part, capable of handling growing traffic, multilingual AI-powered interpretation workflows, and whatever the platform's next phase brings.
Improved infrastructure stability also gave the client the confidence to lean further into AI-powered interpretation during live events and conferences. Reducing dependence on human interpreters opened the door to a wider client base and healthier margins.
Beyond the immediate wins, the project laid a solid technical foundation for what comes next: ongoing infrastructure optimization, future feature development, and continued scaling of real-time speech and translation services.
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