AcousticAI Lab is an industrial AI engineering and research firm. We build sensing-based AI systems for factories and critical assets, together with our industrial partner NeuroControls GmbH, and we build them with the client's engineers rather than selling a generic product.
We build multi-modal sensing (vision, audio from audible to ultrasonic, vibration, lidar, environmental and gas sensors), energy-efficient edge AI that runs on the machine, and sovereign AI setups where data never leaves the plant, including federated learning across sites.
Our methods are peer-reviewed and our experience comes from projects with German SMEs, automotive companies, battery manufacturers, technical universities and leading research institutes. Since 2026 we serve German, European and Indian manufacturers in India, remotely and on site, with working hours that overlap the Indian day.
The firm grew out of applied research in industrial sound and vibration analysis at Fraunhofer IDMT and TU Ilmenau, and from joint projects with German industry: automotive OEMs and suppliers, battery cell and module manufacturers, machine-building SMEs, technical universities and research institutes.
Those projects taught us one thing: general-purpose solutions rarely meet a company's real goal. Systems have to be developed with the company, on its process, under its constraints. In 2026 we joined forces with NeuroControls GmbH in Munich to co-develop multi-modal sensor units and edge AI hardware.
Engineer sensing-based AI that holds up under real plant conditions (noise, sensor mismatch, drift, edge cases) and that the client owns and runs on its own infrastructure.
The reference co-development partner for multi-modal industrial AI in India and Europe.
AcousticAI Lab covers AI, signal processing, dataset engineering and edge deployment; NeuroControls GmbH covers sensor hardware, electronics and industrial integration. Named engineers stay on the account.
Sensor selection, placement and synchronisation; ruggedised multi-modal units and data acquisition with NeuroControls GmbH; industrial integration.
Time-frequency representations, sensor fusion, learned front-ends, honest evaluation with strong baselines; dataset engineering.
Model compression, embedded inference, on-premise training, federated learning, fleet MLOps and security documentation.
Peer-reviewed methods, open benchmarks, publicly funded R&D and university collaborations.
Industrial acoustic, vibration and multi-modal sensor AI; edge machine learning.
Saichand leads AcousticAI Lab's engineering and research. He was formerly a Senior Scientific Researcher at Fraunhofer IDMT, working on industrial sound and vibration analysis, and is a doctoral candidate at TU Ilmenau. His work spans acoustic monitoring of welding processes, partial discharge detection, open acoustic benchmark datasets and energy-efficient edge models. At Fraunhofer IDMT he was part of the team that deployed a low-power acoustic sensor network with on-device sound-source classification around the Veltins-Arena in Gelsenkirchen for the city's Open Innovation Lab (press release, June 2023). Consulting is performed independently of those affiliations.
NeuroControls GmbH, Munich, co-develops and deploys the multi-modal sensor units and edge hardware with us. Together we deliver sensing hardware and AI as one accountable team, from pilot line to roll-out. NeuroControls also leads a freight-rail bearing demonstrator in the EU Chips JU EdgeAI project (grant 101097300), described publicly at edge-ai-tech.eu.
Project experience, shown as categories until written permission for names exists: German machine-building SMEs, automotive OEMs and suppliers, battery cell and module manufacturers, turbines, motors and gearboxes in German industry, technical universities and leading research institutes.
Peer-reviewed work by our founder, published before and alongside the founding of AcousticAI Lab. Full list with DOIs on Google Scholar and ORCID.
13+ publications in total · See Google Scholar and ORCID for the complete list with DOIs.
Coverage of projects our founder worked on before founding AcousticAI Lab.
Five wireless sensor nodes with machine-learning sound-source classification, about 8.5 W per node, for the City of Gelsenkirchen's Open Innovation Lab.
Read the releaseBook a free discovery call. We will look at your process and the recordings you already have and give you a straight answer, including no-go where the physics says so.
The asset or process, what goes wrong, and what recordings you already have. We reply within 48 hours on working days. NDAs are standard.
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