Run AI prompts built
for psychological analysis.

A different approach to psychological analysis prompts.

Searching for the right prompt to run against a session recording or a set of research interviews is a real, recurring task for psychologists, therapists, and behavioral researchers. A single well-worded prompt in a general AI tool can pull a theme or two out of a transcript, but it has no memory of the last session, no record of consent, and no way to read the recording itself, only the words someone typed in after the fact. To be clear about what this analysis is and is not: Speak AI does not diagnose, does not generate treatment plans, and is not a substitute for clinical judgment. It analyzes recordings you already have consent to use, and returns structured, evidence-based output a practitioner or researcher reviews and applies.

Why a single prompt runs cold

Most practitioners and researchers start the same way: paste a transcript excerpt into a general AI tool, ask for themes or a summary, and get an answer with no memory of the last session and no sense of how the person actually sounded. The prompt has to be rewritten every time, consent has to be tracked somewhere else entirely, and nothing connects one session to the next. For anyone coding a study or reviewing a caseload, that is hours of repeated setup for insight that still has to be stitched together by hand.

How Speak reads a recording, not just a transcript

Speak AI treats every consented recording on three layers: the words, transcribed in your language with 100+ supported; the voice, including tone, emotion, and energy across the recording; and a visual layer, where themes, sentiment, and frequency roll up into dashboards you can customize and white-label. Because every session is stored against the same fields, trends show up over time, this month's sessions measured against last month's, a study's early interviews against its later ones. The same prompts and fields run consistently across every recording in your account, and are queryable through Claude, ChatGPT, and Gemini, including through the MCP server if your workflow already lives in one of those tools.

Prompts practitioners and researchers run in Speak

• “Tag the themes and topics that recur across this set of interviews, and group them for coding.”
• “Summarize what this participant said about [topic], with supporting verbatim quotes.”
• “Pull every point in this recording where the tone or energy shifts, in order, with timestamps.”
• “Map the reasoning a participant describes for a decision, and what they say influenced it.”
• “Isolate verbatim quotes that best illustrate this theme, with timestamps, for a case write-up.”
• “Find every point in this session where a disagreement or conflict is raised, and how it resolves.”
• “Track how a participant's stated goals or concerns evolve across repeat sessions in this study.”
• “Compare this training recording against last month's on pacing, question type, and talk time, for supervision review.”

From a folder of recordings to a defensible record

The result is a research or practice record you can actually query instead of re-listening to. Themes are tagged consistently across a full caseload or study instead of drifting session to session. Verbatim quotes stay attached to their timestamp and consent status, ready to cite in a case write-up or a defense. One global market research firm ran its depth-interview analysis this way and saved $60K and 950+ hours without adding headcount. And because the same engine underlies every recording type, the same prompts, fields, and coding connect this session to every other scored conversation in your account.