The bottleneck
After every call or job, the same chore begins: typing it all up. What was discussed in a sales call, what a technician did on site, which parts were used. It happens in the evening, or not at all, and then half of it is missing. The result: "what did we agree on again?", loose notes that go nowhere, and admin that piles up long after the real work is done.
How AI solves it
- Calls into text and tasks: a sales call ends, the platform transcribes the recording, summarises the key points and creates follow-up tasks. The summary and action items land on the right contact in your CRM right away.
- Dictate instead of type: just describe what happened instead of filling in a form. The app recognises which customer, which equipment and which parts are involved, and turns your spoken words into clean, structured text.
- Catalogue recognition: speak product names or codes and the AI matches them against your own catalogue. No manual lookup of article numbers, no mistyped references.
How it works under the hood
Speech to text listens to your audio and turns it into written text. A language model then cleans that text up: it strips out the "ums", structures loose sentences into a tidy report, and converts your words into concrete tasks. The key point: the system must not make anything up. It sticks to what you actually said and to the data in your systems, so no AI hallucination creeps into a work order or summary. Uncertain parts get flagged instead of guessed.
Why transcription quality decides the value
The value of speech-to-text stands or falls on accuracy. If you still have to correct a lot manually afterwards, you lose the benefit: typing it yourself is almost as fast. A well-tuned transcription model with the right vocabulary (jargon, product names, customer names) is therefore not a luxury, it is the foundation.
There is a second reason why quality is critical: those transcripts land in your central company data, alongside everything else. Every error that gets in there carries through into every app, automation and AI assistant that works on it later. A misheard customer name or a missing amount in a work order poisons every report, every search and every answer further down the line. Good transcription at the source is not a detail, it is a prerequisite for the entire chain.
One central setup for all your audio sources
Instead of setting up a separate transcription solution per app or per channel, we use one central model called from everywhere: in-person meetings internal and external, video calls, phone calls and dictation on the move. The same model, the same vocabulary, the same quality, regardless of where the audio comes from.
This has two big advantages. One: you tune the vocabulary and the settings in one place and the effect is felt everywhere. Two: when a better model comes on the market tomorrow, and that happens fast in this field, you swap it in centrally, without having to revisit every app and automation.
Systems involved
- Speech to text to turn your audio into written text
- A language model that cleans up, summarises and turns it into tasks
- Your product catalogue, so spoken items are recognised automatically
- Your CRM and ERP, where summaries, tasks and records land
- An app or chat to dictate into, at the office or on the road
What it delivers
- No more "what did we agree on again?" after a call
- Summaries and tasks that land in the right place by themselves
- No more evening hours spent on admin
- Correct article numbers, without manual lookup
- Text that matches what was actually said, with nothing invented
Related services
- Custom apps: the app you talk to instead of type into.
- AI: the models that clean up, summarise and turn your words into tasks.
Related terms
- Speech to text: the technique that turns your audio into written text.
- AI hallucination: a convincing but incorrect answer, exactly what you want to avoid in a report or work order.
Read also
- AI site reporting: speech to text put to work on site.
