How did you handle the lack of your own Speech-to-Text engine when creating speech analytics product?

In the absence of a proprietary Speech-to-Text (STT) engine for the speech analytics product, I used the following approaches:

1. Integration with external STT services — such as Google Speech API, Yandex SpeechKit, or Azure Speech Services. This allowed for quick access to quality transcription without developing from scratch.

2. Caching and post-processing — to reduce costs and improve quality, I implemented caching of results and additional error filtering.

3. Feedback and training — I collected user corrections of transcriptions to improve quality through custom models or parameter settings of external APIs.

4. Modular architecture — so that in the future, the external engine can be replaced with a proprietary one without major changes.

Thus, the absence of a proprietary STT did not become an obstacle but allowed focusing on analytics and user experience.