Turn research data
en AI-ready insight.

A different approach to AI market research.

Market research is the process of gathering and analyzing what customers, prospects, and the market are actually saying, then turning it into decisions about product, positioning, and pricing. Researchers, marketers, and product teams have relied on it for decades, and the fundamentals have not changed: understand the market, then decide with evidence instead of opinion. What has changed is how fast a team can get from raw interview and survey data to an answer.

Why market research stalls in spreadsheets

For most teams, the practice never matched the promise. Interviews got recorded, then transcribed by hand or by whoever had a free afternoon. Verbatims were pasted into spreadsheets and tagged with codes that drifted from researcher to researcher. By the time a report went out, half the nuance in the room, the hesitation before an answer, the frustration in someone’s voice, was gone. A generic AI tool that summarizes a single transcript does not fix this: it answers one file at a time, with no memory of the last fifty interviews or the codebook your team actually uses.

Reading interviews the way a researcher would

Speak AI treats every interview, focus group, and open-ended survey response the way an experienced researcher would, at machine speed. Each recording is transcribed in your language, with 100+ supported, and then the delivery itself is analyzed: the words, the tone and emotion in the voice, and what the visuals in a video session show. Themes, sentiment, competitor mentions, and the reasons behind an answer are extracted into structured fields your team can filter and compare.

Then the questions start. Ask across your entire research library with AI chat, using ChatGPT, Claude, and Gemini built directly into your workspace through the MCP server.

What teams ask their research

• “Give me a SWOT analysis of what these customers said about the product.”
• “What are the top three action items from this round of interviews?”
• “Summarize the key issues that came up but were never resolved.”
• “Which respondents mentioned pricing, and what exactly did they say?”
• “What questions did we ask most often, and how did answers differ by segment?”

From raw transcripts to a coded dataset

The result is a research library that codes itself. Recurring themes surface without a manual pass, sentiment is scored consistently across every interview instead of drifting between researchers, and dashboards you can customize and white-label track how themes and sentiment move study over study, so this quarter’s findings are measured against last quarter’s, not just described. A global market research firm running Olson Zaltman research put its studies through this workflow and saved 950+ hours and $60K, without adding headcount.

And because research rarely lives in one format, the same engine analyzes calls, meetings, and recordings on the same criteria, connecting your interview library to call scoring and a searchable base de coneixement the rest of your team can query.