Using AI transcription for sales calls: what actually matters
Transcription accuracy is only half the story. Getting AI transcripts that reps trust takes more than an API call.
Modern speech-to-text has changed the economics of sales transcription. Models that were state-of-the-art a few years ago now run for pennies per minute. But getting transcripts that reps trust in production is a product problem, not a model problem.
What modern transcription does well
Out of the box, current models handle:
- Industry jargon surprisingly well (HVAC, pharmaceuticals, construction)
- Proper names better than previous models
- Punctuation in most languages
- Background noise common to field sales (trucks, warehouses)
What it struggles with
- Proper nouns that are phonetically ambiguous (names, brand names)
- Extremely short clips (under ~5 seconds)
- Heavy crosstalk between multiple speakers
- Audio recorded at very low bitrate
The production stack
A good production pipeline isn't just "call the transcription API and done." It includes:
- **Audio preprocessing** — normalize levels, strip silence, detect voice activity
- **Context injection** — pass the rep's known customer names and product list into the prompt so the model gets them right
- **Post-processing** — fix common transcription artifacts, standardize numbers and dates
- **Structured extraction** — a second LLM pass to pull out the fields that actually go into the CRM (action items, sentiment, products, close date)
Skipping any of these means shipping a transcript that's 92% accurate. That sounds fine until you realize 8% of every note is wrong, and reps don't trust the system.
The lesson
Transcription is a component. The product is the pipeline. The best AI sales products in 2026 aren't the ones with the best model; they're the ones with the best pipeline around the model.