Turn transcripts
в answers, not words.

A different approach to transcription prompts.

Audio transcription prompts are instructions you run against a finished transcript to pull out the parts that matter: themes, sentiment, quotes, and a summary a reader can act on. Transcriptionists, researchers, and analysts have written these prompts by hand for years, pasting text into a chat window one file at a time to get a usable answer.

Why a prompt library on its own falls flat

The prompt itself was never the hard part. Ten good prompts, saved in a document, still leave you copying and pasting one transcript at a time, watching for a model that drifts off your wording, and re-checking every summary against the original audio because there is no link back to what was actually said. A prompt library speeds up the typing. It does not fix the workflow underneath it.

Reading the transcript, not just the words

Speak AI runs your prompts against three layers at once: the words on the page, the tone, emotion, and energy in the voice underneath them, and the visuals when video is involved. Every file is transcribed in your language, with 100+ supported, then the recording itself is analyzed for hesitation, emphasis, and the emotion behind what was said. Ask across your entire file library with AI chat, using the prompt workflows you already wrote, now running natively with ChatGPT, Claude, and Gemini built in, cited back to the exact line and timestamp.

The prompts transcriptionists run every day

• “Pull the recurring themes and overall sentiment from this batch of interviews.”
• “Summarize the talking points and conclusions, sorted by which stakeholder needs them.”
• “Condense this focus group into the points of consensus and the outliers.”
• “Highlight the decisions and any change in direction from this meeting.”
• “Turn this deposition into a timeline of admissions and contradictions, with citations.”

From a folder of files to a finished digest

The result is a transcript queue that reads itself first. Recurring themes across a study become a report instead of a highlighter and a notebook, and dashboards you can customize and white-label track sentiment and theme frequency over time, so this month’s files are measured against last month’s. One legal intelligence firm put its call and interview transcripts through this workflow and processed 5,100+ hours of carrier calls, 95% faster, without adding headcount.

And because a transcript is rarely the only thing your team reviews, the same prompts and fields are queryable from Claude, ChatGPT, and Cursor through the MCP server, so a transcriptionist’s prompt library becomes something the whole team can ask questions against.