Sound into
Insight

Deeply is an AI startup founded in 2017 that delivers sound analysis solutions across manufacturing, safety, and security use cases.

Sound AI automates the auditory inspection that used to rest on a person’s ears. A judgment made by ear or by fingertip shifts with the worker’s fatigue and leaves no inspection history behind as data, while Listen AI makes the same judgment from the sound signal and keeps every OK/NG result as a record. More than 60% cost reduction has been reported for large hearing inspection line operations. Accuracy and measurement range differ by inspection type, and the Listen AI Industrial page covers them per type in its own FAQ.

Listen AI judges from the sound signal rather than from an image or a vibration reading. It reaches the motor and actuator defects inside a product that a vision sensor misses, inspecting every unit 24/7 at the EOL stage and blocking 99.8% of defect escapes. That figure is scoped to full inspection of motors and actuators at the EOL stage and is not combined with figures from other inspection types. Sounds too faint for a person to tell apart, such as a connector engagement click, are judged the same way.

The three core Listen AI Industrial solutions are rotating-equipment noise inspection, assembly-line connector engagement sound inspection, and equipment aging detection. On the assembly process it overcomes the high noise of an OEM assembly line and detects continuous connector and bolt fastening work in real time with 99%+ accuracy. For predictive maintenance, which watches the equipment rather than the product, it draws on a 15TB manufacturing acoustic dataset and analysis experience across more than 70 sound types to catch a fault signal early and stop downtime before it starts. Deployed cases also cover glass breakage and scratch detection, semiconductor component wear diagnosis, robot equipment aging analysis, and gas and pressure valve sound analysis.

What it takes is the equipment to be inspected and the sound that equipment makes. Once the hardware is installed and the engagement or operating sounds of the target equipment are collected, initial evaluation can begin on top of Deeply’s Foundation Model. Within the scope of the connector engagement sound press release, this does not wait on weeks or months of site-specific defect data and changes no existing line equipment, tools, or layout, but that account assumes a Foundation Model specialized in manufacturing acoustic data, so it does not mean every deployment needs no data collection at all. Inspection history can be linked to a MES (Manufacturing Execution System) or PLC and kept as traceable data.

A PoC runs in four steps: install the hardware, collect sound from the target equipment, run the initial evaluation, then carry the result into production-line deployment. The initial evaluation starts on top of Deeply’s Foundation Model, and the PoC result is what carries into the production line. The microphone setup and the analysis server deployment are chosen against the noise conditions on site, and the Listen AI Industrial page covers the conditions per inspection type in its own FAQ.