AI hardware devices are devices built around a spoken conversation with an AI model, instead of a screen and apps. Examples include a small pin that clips to clothing, a pendant, a pair of glasses with a camera and speakers, and earphones that answer questions. The user speaks, and the device replies in speech. The category is new, and few of these devices have yet sold in large numbers.
Here is how such a device usually works. Its microphones are always ready. A small, low-power chip listens for a wake word, or waits for the user to press a button. These chips are TinyML chips, which run very small AI models on very little power. When the device wakes, it records what the user says. The recording goes to the user's phone over Bluetooth, or straight to the internet over the device's own Wi-Fi or mobile connection. On the maker's servers, a speech and voice AI model turns the speech into text. A large language model writes a reply. Another speech model turns the reply into a spoken voice, which the device plays. The whole exchange takes one to three seconds.
Some of these steps can run on the device itself. This is called edge AI. It gives faster replies and keeps the recording private, because the audio never leaves the device. But it needs a more powerful chip and drains the battery faster. Most devices therefore split the work: the device listens and records, and the servers do the rest. Firmware on the device, an app on the phone and the maker's servers together form the three layers described on IoT architecture.
Imagine a field engineer in Nashik who repairs solar water pumps on farms. Her hands are covered in grease, so she cannot use a phone screen easily. She presses the button on a pin clipped to her shirt and asks, in Marathi, what fault code E07 means on the pump's controller. The pin records about four seconds of speech and sends it through her phone to the maker's servers. About two seconds later, the pin reads out the answer: E07 means the pump's motor is drawing too much current, and she should check the motor for a jammed impeller.
These devices face hard problems. A small device has a small battery, and listening all the time uses power. Heavy computing in a small case makes it warm. A device that records sound and pictures raises privacy questions for the people around the user. AI models sometimes give wrong answers in a confident voice. And each question costs the maker money for computing on its servers. Phones now have AI assistants of their own, so a new device must do something that a phone cannot do as well, such as working hands-free.
Several Bengaluru start-ups are building devices of this kind. Building one needs the same skills as smartphone design: choosing chips, fitting antennas and writing firmware.