We're not smarter than your AI. We just actually know your words.
Live
ASR heard
↓
vocab layer
Every industry has an “AXXE.”
In agronomy it's a herbicide brand. In legal it's Morgan Stanley. In medicine it's Lisinopril. Generic speech-to-text hears what it was trained on — not what your users actually say. Vocab Layer resolves industry vocabulary in milliseconds, before your app ever sees the text.
Try it yourself
The live demo runs against the Agronomy Layer. Type a mangled transcript or click an example — gray-violet is what your ASR heard; green is what vocab layer resolves it to.
Corrected output
Run a correction to see results
How it works
No model to train. No dictionary to maintain yourself. Your backend holds the API key — your app never does.
01
Capture a transcript
From your own ASR, Chrome's mic, Whisper — wherever speech becomes text.
02
Pick a Layer
Your backend calls POST /api/correct with a domain slug — agronomy today, more industries shipping soon.
03
Get clean text back
Corrected transcript, confidence scores, and latency — in milliseconds.
The honest answer
Why not just use built-in custom vocabulary?
DeepgramAzureGoogle
Good question — you should ask it. Every major speech-to-text provider ships some form of custom vocabulary or keyword boosting. If that's solving your problem, you don't need us.
Here's where it stops being enough:
01They give you the box
Their custom vocab
Add terms manually…
Track new launches
Fix regional names
Seasonal updates
Agronomy Layer
AXXE
Roundup
Liberty
+ Enlist Duo
↻ synced
You'd have to build and maintain the dictionary yourself.
Custom vocabulary features give you a box to type words into — they don't hand you the words. Every new product launch, every regional brand name, every season's additions become your job to track down and keep current. We maintain the agronomy vocabulary as the product. You get the updates, not the upkeep.
02Any ASR in, same layer out
DeepgramGoogleAzureApple
→
vocab layer
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AXXE on the north field
You're locked into whichever ASR you picked.
Azure's custom vocabulary only helps if you're on Azure. Switch providers, retrain everything. Vocab Layer sits after your ASR step, so it works the same whether your transcript came from Deepgram, Google, Apple's on-device recognition, or anything else. Change your ASR vendor next year — your correction layer doesn't care.
03Context decides
legal
the court denied justice
agronomy
spray Justice on the beans
Same word. Different domain.
Keyword boosting can't tell "Justice" the word from Justice the herbicide.
Biasing recognition toward a word list helps, but it doesn't reason about context. When a term is genuinely ambiguous, we run it through a disambiguation pass that looks at the sentence around it — something a static keyword list structurally can't do.
We're not claiming to out-transcribe Deepgram or Google — they're excellent at turning speech into text. We're the layer that makes sure the text is right for an industry neither of them was built to know.