Turn client work
into prompts you can reuse.

A different approach to AI prompts for consultants.

An AI prompt library is a set of pre-built instructions that tell a language model exactly what to pull out of a piece of client data: the themes in an interview, the sentiment in a survey, the objections in a call. Consultants have used versions of this for years, first as saved chat threads, now as something a whole practice can run against every file in an engagement.

Why prompt libraries usually stall

Most firms have tried this before. Someone builds a shared doc of good prompts after a project goes well, it gets pinned to a Slack channel, and within a quarter nobody remembers it exists. The prompts assume a single interview transcript pasted into a chat window, not a folder of forty. And they only ever read the words on the page, missing the hesitation in an interview, the frustration on a support call, or the energy behind a stakeholder’s objection.

Reading client data the way a partner would

Speak AI runs prompts across the full engagement, not one file at a time. Every recording is transcribed in your language, with 100+ supported, and then read on three layers: the words themselves, the tone, emotion, and energy behind them, and any visuals from a video call or screen share. A prompt built for stakeholder synthesis pulls the same themes a senior partner would flag, cited back to the exact interview and timestamp.

What consultants ask their prompts

• “Synthesize the key sentiment trends and actionable insights from this round of customer interviews.”
• “Summarize the most-mentioned features, complaints, and opportunities across these product reviews.”
• “Pull the shared themes and quotable evidence from these stakeholder interviews into one summary.”
• “Extract the main opinions, objections, and suggestions raised across this focus group.”
• “Turn these survey responses into ranked findings with clear, data-backed recommendations.”
• “Combine the interview, survey, and call data into one integrated summary with the trend chart attached.”

From a folder of transcripts to a client-ready synthesis

The result is a prompt library that gets faster, not slower, as an engagement grows. Ask a question once and Speak AI answers it across every interview, survey, and call in the folder, from inside Claude, ChatGPT, or Gemini. 您可以自定义和白标的仪表板 track sentiment and theme frequency across an engagement over time, so wave two of a study is measured against wave one instead of read cold. One legal intelligence firm ran this same prompt-driven analysis at scale in its 大规模比较分析, processing 5,100+ hours of carrier calls and saving $700K+ without adding headcount. And because a consulting practice runs on more than transcripts, the same prompts stay reachable from Claude, ChatGPT, and Cursor through the MCP server, so a partner can ask a question about a client engagement without leaving their own tools.