Speech models built for local execution
Deploy ASR, AST, and TTS components at the edge. Processing the speech workload on the device reduces the distance between input and inference.
SPEECH INTELLIGENCE. RIGHT ON THE DEVICE.
Edge deployment
Less distance.
Faster spoken interactions.
Run Assisto’s ASR, AST, and TTS models on supported edge devices. Our optimised speech models run on Qualcomm Hexagon and Apple silicon with sub-second latency.
Discuss your use caseEDGE DEPLOYMENT01 / 03
On-device ASR · AST · TTSQualcomm Hexagon & iPhoneSub-second speech inference
EDGE DEPLOYMENT / OVERVIEW
Move the speech model closer to the person using it.
Some experiences need the speech layer on the device itself. Assisto has optimised its speech models for edge execution, bringing recognition, translation, and synthesis to supported Snapdragon and Apple silicon. The deployment is matched to the target device and required speech workload.
What the engine
can do.
Capabilities that connect the model to the job you need it to perform.
Qualcomm Hexagon acceleration
Run supported models on Snapdragon’s HTP hardware. Configure the model and runtime for the selected chipset and the device’s available resources.
Speech on Apple silicon
Bring the speech components to supported Apple silicon. This supports local speech experiences within the device application rather than requiring every speech operation to be handled remotely.
Sub-second latency for supported workloads
The optimised models support sub-second speech inference on supported hardware. End-to-end experience also depends on audio length, model configuration, device, and any downstream tools the application calls.
BUILT AROUND THE WORK
Where edge deployment fits.
On-device transcription
Convert spoken input into text within a device-based workflow.
Portable speech translation
Use local speech models for language interfaces in mobile applications.
Embedded voice experiences
Add responsive speech input and output to a supported edge application.
FROM CONFIGURATION TO USE
Make it work
for your business.
The deployment starts with your sources, your users, and the task the system needs to complete.
- 01Choose the target device, speech task, and required languages.
- 02Match the model and runtime to the supported hardware.
- 03Integrate the speech input and output with the device application.
- 04Evaluate latency, resource use, and speech quality on the actual target devices.
A CLOSER LOOK
Common questions.
Which hardware can you support?
Assisto supports speech model execution on Qualcomm Hexagon and Apple silicon. The exact device and model combination is established during deployment.
Which models can run at the edge?
ASR, AST, and TTS are the speech capabilities offered for supported edge deployments.
Is every part of a voicebot offline?
On-device speech does not imply that every connected service is local. A CRM lookup or remote API call still depends on that service; the overall architecture is defined for the use case.
How should latency be evaluated?
Measure the selected model and workload on the target device. Separate speech inference latency from network calls and other application processing.
YOUR NEXT CHAPTER
Let’s build something useful.
Bring your business challenge. We’ll bring AI, data, and a practical way forward.
