Start with the interaction, not the chip name
A mobile application captures a spoken note and reads a response aloud. Sending audio to a remote speech service introduces a network round trip before the application can use it. Running the speech layer locally changes that dependency—but does not automatically make the entire assistant offline.
Assisto optimises ASR, speech translation, and TTS models for supported edge devices. This illustrative architecture places speech processing inside the application’s device boundary and keeps business-system access separate. That boundary is the central design decision.
Name the hardware precisely
On supported Snapdragon platforms, the relevant acceleration target is Qualcomm’s Hexagon Tensor Processor (HTP), part of the Hexagon processing architecture. On supported iOS devices, the target platform is Apple silicon. Device generation, available memory, operating-system version, and model configuration define the deployment specification.
The implementation must validate its execution path on the selected device. Apple silicon includes several compute resources; the platform name alone does not establish which one executes a particular model. The supported configuration should be documented at integration time rather than implied by a generic hardware badge.
Draw the offline boundary honestly
Recognition can turn local audio into text, and synthesis can produce spoken output on-device. A request to retrieve a customer’s current order still depends on the order service. If that service is unavailable, the application must distinguish local speech success from business-action failure.
Local inference also does not, on its own, prove that no data leaves the device. Application logging, analytics, synchronization, and connected tools require their own decisions. For this design, the team specifies which audio, transcripts, and operational records may be transmitted and retained.
Measure the experience that users receive
Assisto reports sub-second speech inference on supported model-and-device configurations. That is not a blanket promise about a complete voice-agent response. Audio capture, preprocessing, inference, business-tool requests, and playback each contribute to the experience.
A device evaluation should record first-output latency, sustained processing, memory consumption, power use, and behavior under thermal load. Test the selected languages and realistic audio rather than a single short sample. The shipping decision follows those measurements on named hardware—not a broad platform claim.
