Turn raw text
into themes you can trust.

A different approach to textual analysis.

Textual analysis is the process of using natural language processing and machine learning to turn written text, survey responses, reviews, transcripts, documents, into insight: what people are saying, how they feel about it, and how often. Researchers, marketers, and operations teams have used free tools like Leximancer, TextStat, Voyant Tools, and the R language for years to find themes and word patterns in a body of text.

Why free textual analysis tools stall at the first real dataset

Those tools are genuinely useful for a single pass on a single file. The trouble starts at the second dataset. Each one asks something different of you: export a CSV, learn a query syntax, install an R package, or work inside a browser tool that was not built to combine sources. None of them connect a batch of survey responses to the call recordings, interviews, or reviews that talk about the same thing. The output stops at word frequency and topic clusters, with no sentiment on how something was said and no structured record your other systems can use.

Reading the text, not just the words

Speak AI treats every document, transcript, and response the way a research lead would, at machine speed. Text is analyzed in your language, with 100+ supported, and themes, sentiment, and keyword frequency are extracted into structured fields your systems can use. Because the same engine also reads audio and video, a survey response, a support call, and a recorded interview about the same topic land in one place instead of three separate tools.

Then the questions start. Ask across your entire text library with AI chat, using the same high-quality prompt workflows teams once stitched together in R or a notebook, now running natively over your documents with ChatGPT, Claude, and Gemini built in.

What teams ask their text data

• “What are the most common themes in this quarter’s open-text survey responses?”
• “Which reviews mention pricing, and what exactly did customers say?”
• “Show me every response that mentions a competitor by name.”
• “Which responses sound frustrated, and how does that compare to last quarter?”
• “Summarize the most common complaints across all support tickets this month.”

From a folder of documents to a themebook you can chart

The result is a themebook that updates itself instead of a one-off report. Recurring complaints surface automatically. Theme frequency and sentiment become dashboards you can customize and white-label, tracked over time so this quarter’s responses are measured against last quarter’s, and queryable through Claude, ChatGPT, and Cursor via the MCP server. One legal intelligence firm put its carrier call transcripts and case documents through this workflow and processed 5,100+ hours and saved $700K, at a scale free tools were never built to handle.

And because text rarely lives alone, the same engine scores calls, meetings, and recordings on the same criteria, connecting your document analysis to call scoring and coaching across every conversation your team has.