Turn client work
naar binnen 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. Dashboards you can customize and white-label 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 large-scale comparative analysis, 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.