Hear the sentiment
في tone, emotion, energy.

A different approach to voice sentiment analysis.

Sentiment analysis started as a text problem: score a review positive or negative, average the words, move on. Businesses and researchers now treat it as a core way to understand customers, participants, and markets, turning subjective reactions into structured, measurable data. But most of the sentiment that matters happens on a call, not on a page. The way a caller’s voice tightens mid-sentence, the pause before a hard answer, the pitch that rises right after your pricing lands. Text-only tools never see any of it.

Why sentiment scoring stops at the words

Early sentiment tools ran on keyword rules: a word like “terrible” scored negative, “great” scored positive, and the tool averaged the two. That approach missed sarcasm, hedging, and the way tone can flip a compliment into a complaint. Most tools are still built for text only, which means transcribing a recording separately before analysis can even start, one more tool bolted onto the workflow, one more place for signal to get lost between the recording and the report.

Reading the voice, not just the transcript

Speak AI treats a conversation the way a sharp CX lead or researcher would, at machine speed. Each recording is transcribed in your language, with 100+ supported, and then the audio itself is analyzed: the words, the tone and emotional energy behind them, and the pitch and pacing that signal frustration before a caller ever says the word. Sentiment is scored at the sentence and speaker level, so you see exactly where a conversation turns and who turned it, and you can ask across your entire library with AI Chat using Claude, ChatGPT, and Gemini.

What teams ask their conversations

• “When did sentiment turn negative in this call, and what happened right before it?”
• “Which interviews had the most positive responses about pricing this month?”
• “Show me every call where a customer mentioned a competitor with negative tone.”
• “Which reps keep sentiment positive longest, and what do they do differently?”
• “Summarize the emotional tone of this quarter’s exit interviews.”

From a sentiment score to a decision

The result is sentiment analysis your team can act on, not just a chart. Frustration spikes surface before a churn call happens instead of after. Keyword-sentiment correlation shows that “pricing” mentions skew negative while “onboarding” skews positive, and dashboards you can customize and white-label track how sentiment moves across a speaker, a project, or a quarter, so this month is measured against last month instead of a gut feeling. One healthcare consulting firm put patient sessions through this workflow, cutting processing time from 8 hours to 0.3 with empathy scoring built into every session, and saved $190K+ across 10,000+ hours.

And because tone rarely lives alone, the same engine reads sentiment across calls, meetings, and interviews from inside Claude, ChatGPT, and Cursor through the MCP server, so the question and the answer live in the same place.