Otter Built Its Own ASR

Building its own speech engine let Otter own the hardest part of the product instead of renting it from an API vendor. That matters because Otter is not just turning audio into text, it is trying to label who said what in messy group calls, sync that transcript live, and turn it into summaries and follow ups. A proprietary ASR stack gives Otter tighter control over accuracy, latency, speaker tagging, and gross margin as usage scales.

• The practical advantage shows up in meeting workflows. OtterPilot auto joins Zoom, Meet, and Teams, captures the meeting audio stream, and produces live speaker labeled transcripts. That end to end setup works better when the same company controls both the capture layer and the recognition layer.
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• The closest infrastructure style comparison is Deepgram. Deepgram sells speech recognition as a metered API to developers, while Otter uses speech recognition inside its own app. Deepgram wins by serving many use cases, Otter wins by tuning one workflow, multi speaker meetings with notes, search, and actions.
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• Owning ASR also helped Otter in the earlier phase of the market, when transcription quality and cost still mattered a lot. As speech recognition got cheaper and more available, the advantage shifted from raw transcription toward what happens after the call, summaries, coaching, CRM updates, and knowledge retrieval.
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The next step is to use proprietary speech recognition as the ingestion layer for a larger meeting data system. The companies that win from here are the ones that turn conversation into usable company memory and software actions, not just clean transcripts. Otter already has the capture and speech stack in place to make that shift.