Expert Echo, a platform for audio content management, was being squeezed by their cloud transcription provider. Per-minute pricing scaled faster than their business, sensitive customer audio had to leave their infrastructure to be processed, and they could not add the features their own users were asking for. They engaged us to bring the whole pipeline in house. We built a self-hosted Whisper deployment that gave them full ownership, better capability, and a fraction of the running cost.
View Impact
The client needed to keep quality while removing a variable cost that grew with every new customer. We delivered a containerised Whisper deployment on their own infrastructure, with speaker diarization to separate voices in a conversation, support for multiple languages and audio formats, and a webhook system that notifies their systems the moment a job completes.
The existing setup had four problems at once. Per-minute pricing was becoming unsustainable at scale, customisation for their specific use case was limited, sensitive audio had to leave their infrastructure to be processed, and the service offered only basic transcription with no speaker separation.
We containerised Whisper with Docker for consistent deployment, queued jobs through Redis to handle concurrent load, and exposed a FastAPI REST interface for straightforward integration. A microservices split separated audio processing from transcription, and the stack was tuned for Kubernetes with auto-scaling so cost tracks actual usage.
Transcription costs dropped 70 percent against commercial services and processing time fell 40 percent through better resource allocation. Expert Echo gained speaker diarization and multilingual support, took full ownership of the pipeline and its data, and can now absorb growth with minimal additional cost.
View Impact
The client needed to keep quality while removing a variable cost that grew with every new customer. We delivered a containerised Whisper deployment on their own infrastructure, with speaker diarization to separate voices in a conversation, support for multiple languages and audio formats, and a webhook system that notifies their systems the moment a job completes.
The existing setup had four problems at once. Per-minute pricing was becoming unsustainable at scale, customisation for their specific use case was limited, sensitive audio had to leave their infrastructure to be processed, and the service offered only basic transcription with no speaker separation.
We containerised Whisper with Docker for consistent deployment, queued jobs through Redis to handle concurrent load, and exposed a FastAPI REST interface for straightforward integration. A microservices split separated audio processing from transcription, and the stack was tuned for Kubernetes with auto-scaling so cost tracks actual usage.
Transcription costs dropped 70 percent against commercial services and processing time fell 40 percent through better resource allocation. Expert Echo gained speaker diarization and multilingual support, took full ownership of the pipeline and its data, and can now absorb growth with minimal additional cost.
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