Skip the data
annotation company.

A different approach to data annotation.

Data annotation is the process of labeling audio, video, and text so a model, a dashboard, or a person downstream can act on it: what was said, who said it, how they felt when they said it, and which category it belongs to. Research teams, support teams, and machine learning teams have all leaned on outside data annotation companies for this work, because doing it by hand across thousands of files never scaled.

Why outsourced annotation breaks down

The trade-off was always the same. You handed your calls, transcripts, or survey files to a data annotation company, waited days for labels to come back, and then found the taxonomy did not quite match how your team actually talks about the data. A guideline document went back and forth. Edge cases piled up in a queue. By the time the labeled set landed, the project it was meant to support had already moved on, and the next batch started the cycle over again.

Reading and labeling at the same time

Speak AI treats every file as three layers at once, not one. The words are transcribed in your language, with 100+ supported. The delivery, tone, energy, and emotion in a voice, is analyzed alongside the transcript. And for video, the visual layer, faces, screens, slides, comes with it. Every layer feeds the same set of labels: your own taxonomy, your own field names, your own categories, applied consistently across the whole library instead of drifting file to file.

Then you can ask questions across the whole labeled set with AI chat, using ChatGPT, Claude, and Gemini built in, instead of exporting a spreadsheet and starting a new analysis from scratch.

What teams tag with Speak AI

• Sentiment, tone, and urgency labeled on every recording, not just the words in the transcript.
• Named entities: people, companies, product mentions, and locations extracted into structured fields.
• Custom labels for your own taxonomy: intent, objection type, compliance flag, or research code.
• Confidence scores on every label, with a review queue for the ones the model is least sure about.
• Which files mention a given topic, competitor, or complaint, searchable across the entire library.

From raw files to a labeled dataset

The result is a dataset that stays labeled the same way, month after month, instead of drifting between annotation vendors or contractor batches. Trends in sentiment, topic frequency, or label distribution become 可自定义和白标的仪表板, tracked over time instead of re-run as a one-off project. A legal intelligence firm put this same tagging engine on carrier calls and 已处理 5,100+ 小时，节省 $700K+, without adding an outside annotation vendor to the process.

And because the same engine that labels your files also scores and codes them, the annotation work connects directly to 通话评分 and to your applications through the MCP server, so the labels are queryable from the tools your team already uses.