Azure’s raw text
becomes insight your team owns.

A different approach to Azure speech-to-text.

Azure Speech-to-Text is Microsoft’s cloud-based speech recognition service: a developer API you call from your own application to convert spoken audio into text. It is part of Azure AI Services, and teams use it to add transcription into products they are already building, through an SDK or REST endpoint and an Azure account. It is a solid engine for exactly what it is built to do: turn audio into words.

Why a transcript alone is not enough

The gap shows up right after that first call to the API. A transcript is a wall of text. It does not tell you which caller was frustrated, which recording needs a human review, or how sentiment shifted this quarter versus last. Most teams that build directly on a speech API end up writing their own scoring layer, their own dashboards, and their own routing logic just to make the output usable, work that has nothing to do with the product they set out to build.

Reading the recording, not just transcribing it

Speak AI treats every recording the way a manager reviewing calls by hand would, at machine speed. Each file is transcribed in your language, with 100+ supported, and then the recording itself is analyzed: the words, the tone and energy in the caller’s voice, and, where relevant, the visuals in a meeting or video. Names, scores, sentiment, and outcomes are extracted into structured fields your systems can use, and you can ask questions across your entire library with AI chat, using Claude, ChatGPT, and Gemini built in, the same way you would ask a well-briefed analyst.

What teams ask about Azure vs. Speak AI

• “Is Azure Speech-to-Text accurate enough on its own, or do we need something layered on top?”
• “How much engineering does it take to turn an Azure transcript into scored, searchable insight?”
• “Can we keep the engine we already use and still get dashboards, sentiment, and MCP access?”
• “What is actually different between a speech-to-text API and a call-scoring platform?”

From API output to team-ready insight

The result is a recording library that scores and tags itself, with trend-over-time reporting instead of a one-off transcript, and instrumentbord du kan tilpasse og white-label for your own team or your clients. One multilingual education provider needed exactly this pairing: raw transcription was not enough on its own, so Interpreting.com built a multilingual assessment workflow on embedded recorders and Speak AI instead of stitching together a transcription API by hand. And because recordings rarely live alone, the same engine scores calls, meetings, and voicemails on the same criteria, connecting Azure-sourced transcripts, or any others, to samtalevurdering and queryable through the MCP server from Claude, ChatGPT, and Cursor.