Convertir transcripciones
en meaning you can prove.

Un enfoque diferente al análisis textual.

Textual analysis is the interpretation of a text’s structure, meaning, and implications: what a document, transcript, or article means and how it produces that meaning. Content analysis is the related technique that measures the frequency and context of words, phrases, and topics across the same material. Speak AI runs both, on the same file.

Where the comparison breaks down in practice

Content analysis and textual analysis get taught as opposites: counting versus reading, quantitative versus qualitative. In a real project they are rarely separate. A researcher coding twenty interviews still has to notice that “blindsided” keeps showing up, and still has to work out what that word is doing in each conversation. Doing the count by hand in a spreadsheet and the reading by hand in the margins of a transcript means two passes through the same material, on two different tools, usually by two different people.

Reading meaning, not just counting words

Speak AI treats every transcript, document, or article the way a careful analyst would, at machine speed. Each file is transcribed or ingested in your language, with 100+ supported, and then read on three layers: the words themselves, the tone and energy behind them where audio or video is available, and the structure and framing of the piece as a whole. Codes, themes, and frequency counts are extracted automatically, and the interpretation, what the framing means for the reader, is generated alongside them, not left for a second pass.

Then the questions start. Ask across the entire project with AI chat, using the same high-quality prompt workflows teams once stitched together manually, now running natively over your transcripts through the Servidor MCP, with Claude, ChatGPT, and Gemini built in.

What researchers ask their transcripts

• “How many interviews mention this theme, and what did people actually say about it?”
• “Compare how these three articles frame the same event.”
• “Which transcripts use passive voice when describing the institution, and which use active voice?”
• “Summarize the recurring codes across this project, ranked by frequency.”
• “Show me every quote where a participant sounds frustrated or dismissive.”

From two methods to one workflow

The result is a project where the count and the reading happen in the same pass. Codebooks apply consistently across hundreds of transcripts instead of drifting between coders, and dashboards que puedes personalizar y etiquetar con tu marca track code frequency and theme trends over time, so this quarter’s interviews are measured against last quarter’s. A respected media brand used this workflow to turn 500 horas de video de conferencia en contenido de alto rendimiento, coding and repurposing footage that would have taken a team weeks to review by hand.

And because textual analysis rarely stays inside one project, the same engine carries the codebook into codificación cualitativa y análisis temático across every transcript your team collects, built for investigadores cualitativos from the first upload.