Speech-to-text. AI uses open-source or API-available models for automatic speech recognition, adjusting based on vocabulary;
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Structured processing and summarization. AI models summarize and organize interview records, and extract insights;
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Output processing workflow. AI models take specific actions on the content generated in step two. For example, pushing information from sales calls into CRM, or using the conversation content from both parties to fill out insurance pre-authorization forms.
AI transcribers can be applied in a wide range of areas, including documentation, customer communication, workflow automation, revenue cycle management, and coding, helping people improve productivity and operational efficiency.
Here are 5 startup projects from different fields:
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Freed: Listens to consultation audio and creates SOAP notes for doctors. Helped 9,000 clinicians in one year, recording 1 million patient visits per month, and the company reached $10 million ARR.
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Granola: A notebook connected to large language models, capable of merging manual and AI-generated meeting notes, creating quick summaries, and supporting multi-person sharing.
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Scribenote: A tool to "help veterinarians leave work earlier" that completes SOAP notes with just two clicks. Announced on December 2 last year that the platform had generated over 100,000 automatic notes.
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Rilla Voice: An AI transcriber accompanying sales trainees, capable of recording, transcribing, and analyzing every sales training session, allowing sales managers to guide trainees with more detailed methods.
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Aqua Voice: A hands-free voice writing and editing tool.
AI transcribers are not just efficiency tools, but have the potential to become industry-changing work systems. In the future, the market potential of AI transcribers may be further unleashed, with the medical field showing great interest in AI transcribers first.