Industrial AI engineering and research · Germany and India
Sensing-based AI for factories and critical assets: end-of-line acoustic testing, in-process weld monitoring, switchgear partial discharge and multi-modal condition monitoring — with energy-efficient edge AI and sovereign deployments where your data never leaves the plant. Co-developed with your engineers, on your process, against your targets.
Every one of these has a signature in sound, vibration, ultrasound or image data. Detecting it reliably, in your noise, on your machines, with your data staying on site, is engineering work, and it is the work we do — alongside the monitoring system you already run, not instead of it.
A bearing, a compressor or a transformer fails without warning and the whole line waits.
Welding, machining, extrusion and cell-assembly defects found too late to prevent.
Acoustic test limits set by hand per variant: good units torn down, marginal units shipped.
Partial discharge and insulation faults that announce themselves for months, unheard.
An internal pilot or a vendor tool whose alerts your team has learned to ignore, running alongside the system you already pay for.
Multi-site learning and cloud dashboards blocked by IT security, and rightly so.
We do not sell a generic product. Every system is co-developed with your engineers against your process, your machines and your targets. These are the engagements most clients start with; the full list includes feasibility studies, datasets, advisory and training.
Applied R&D done together with your engineers, on your process, with a measurable target agreed up front.
Co-developed sensor units and on-device AI with NeuroControls GmbH: vision, audio (audible to ultrasonic), vibration, lidar, environmental and gas sensing across one platform.
AI that stays on your premises: on-device and on-premise deployment, and federated learning across plants without moving raw data.
Models sized for the hardware and the site's power budget: quantisation, pruning, efficient architectures, real-time on embedded devices.
Each step has a fixed scope and a clear output. You decide at every rung whether to climb the next one. Most Indian clients start remotely; measurement and deployment steps happen on site.
You bring the problem and the data you have. We ask the awkward questions and tell you whether sensing AI is the right tool.
Fixed scope, fixed fee. Where you already have recordings, from an end-of-line test cell, a weld monitor or a survey instrument, the study runs on those first; no new hardware is needed for a first answer.
Milestone-based. A validated prototype on your data, evaluated against strong baselines, with a dataset you own.
Sensor units and edge AI on one line, integrated with PLC, SCADA or MES, handed over to your OT/IT team, then scaled.
Already have a data science team? An advisory retainer gives you senior engineering time by the day or month.
With our industrial partner NeuroControls GmbH we co-develop ruggedised sensor units and edge AI platforms that fuse vision, audio from audible to ultrasonic, vibration, lidar, temperature, humidity and gas. Fusion is where robustness comes from: a microphone fooled by a forklift is corrected by the accelerometer that felt nothing.
Illustrative. In projects we show real spectrograms, point clouds and benchmark plots from your data, not stock visuals.
Cloud-first industrial AI runs into data residency law, customer contracts and trade-secret reality. Our default architecture is inference on the edge, training and monitoring on-premise, and federated learning across sites so only model updates travel, never raw recordings.
Acoustic testing and monitoring are standard practice in three places. We do not ask you to believe in a new method; we add learning to one you already run.
Our founder's methods were published and peer-reviewed before they reached a plant. We evaluate honestly: strong baselines, realistic conditions, no invented numbers. Performance figures are quoted from the papers or from your own data, never from a brochure.
Large enterprises and large SMEs in India's manufacturing clusters, and our existing project base in Germany, Austria, Switzerland and the EU. Or start from the asset: bearings, motors, welds, transformers, extruders — find your asset.
End-of-line acoustic testing, weld monitoring and machine monitoring, from body shop to e-drive assembly.
Partial discharge and rotating-machine monitoring for plant substations, captive power and generation.
Embedded sensing and AI as a feature of your machine, engineered with your R&D team.
Specialist acoustic diagnostics alongside the monitoring you already run, where downtime is priced per hour.
Inline quality for cell, module and pack lines: in Europe now, in India as the cell plants come online.
Inline process and quality tracking for extrusion, filling, packaging and utilities.
What our sensing partner is proving on freight wagons: bearing condition classified on the vehicle, without batteries or cabling.
Not our figures. Published ones, from the organisations that measured them, with the source on every tile.
Unplanned downtime in automotive manufacturing.
As reported by respondents to the Siemens True Cost of Downtime survey, 2024.
And 25 to 30 percent lower maintenance cost with predictive maintenance.
US Department of Energy figures, FEMP O&M Best Practices Guide.
Per percentage point of scrap in a 40 GWh battery cell plant.
Fraunhofer FFB and RWTH PEM, Mastering Ramp-up of Battery Production, 2024.
Lost to compressed-air leaks that ultrasonic detection finds.
US Department of Energy, Improving Compressed Air System Performance.
Alongside them, the only figure that is ours: 13+ peer-reviewed publications from our founder's research; selected papers with DOIs on the About page, full list on Google Scholar and ORCID.
We work inside your process with your engineers, from measurement plan to deployed system, alongside the monitoring you already run.
Vision, audio from audible to ultrasonic, vibration, lidar, environmental and gas sensors, co-developed and deployed with NeuroControls GmbH.
On-premise and on-device deployment, no cloud dependency, data stays with you; federated learning across plants where needed.
Models sized for the hardware and the site's power budget, not for a data centre.
Baselines, ablations, realistic test conditions and a go / no-go gate before large spend.
Experience from German SMEs, automotive OEMs, battery manufacturers, technical universities and research institutes, including a field-deployed acoustic sensor network covered in the press. On-site work in India.
Practical writing on acoustic and vibration AI, end-of-line testing, sovereign architectures, edge deployment and what actually gets a project to production.
Five wireless acoustic sensor nodes around the Veltins-Arena classified noise sources on the device at about 8.5 W eac…
Read articleHandheld ultrasonic and TEV surveys of switchgear are routine. Making the same survey permanent and unattended is an e…
Read articlePowertrain and e-drive plants already screen every unit acoustically. The limits are still set by hand per variant. He…
Read articleWhy predictive maintenance AI pilots in Indian plants stall after the demo, and a practical path from feasibility stud…
Read articleThat is exactly what a feasibility study answers, in two to ten days, on your data or a short measurement campaign. We design sensing setups for hostile acoustic environments and train models across the process window so they generalise. If the physics says no, we say no-go.
It depends on the failure mode, not the asset. Vibration for rotating parts, audible acoustics for process context and coverage, ultrasound for leaks, arcing and early friction, acoustic emission for in-process material events. Often we start with the sensors and recordings you already have, from an end-of-line test cell, a weld monitor or a survey instrument, and add the NeuroControls multi-modal units only where fused data is needed.
No. Most continuous plants already have a vibration monitoring system and a vendor behind it, and that is usually the right tool for standard rotating assets. We work alongside it on the assets it cannot explain: acoustically hostile areas, intermittent faults, in-process quality, electrical assets, and systems whose alerts your team has stopped trusting and wants evaluated independently.
Yes. That is our default architecture: inference on an edge device at the machine, training and monitoring on-premise, and federated learning if several plants should learn together. We document the data flows for your IT security review, mapped to IEC 62443 where OT components are involved.
Yes. Every project delivers the dataset with its specification, the trained model weights and the documentation to you, and we do not train across clients. Hardware is yours once deployed. We use open-weight models and open tooling wherever they meet the requirement so you are not locked to us.
Usually less than you fear. A feasibility study needs a few hours of recordings with known outcomes, and where you already have recordings it runs on those first, with no new hardware. A proof of concept typically needs days to a few weeks across your product mix. Representativeness and label quality matter far more than volume.
Yes on both. NDAs are standard, and anonymised or synthetic samples are fine for a first conversation. Monday to Friday, 09:00 to 18:00 German time (CET in winter, CEST in summer). In Indian Standard Time that is 12:30 to 21:30 from late March to late October and 13:30 to 22:30 for the rest of the year. Calls with India are scheduled between 13:30 and 16:30 IST all year. On-site visits to Indian plants are part of the measurement and deployment steps.
It is a fixed-scope, fixed-fee engagement quoted after a free discovery call, because the scope depends on your data and whether a measurement campaign is needed. Invoices are issued in EUR from Germany; your finance team should check the applicable tax treatment.
Your OT/IT team, trained during handover, with an optional advisory retainer from us and named engineers who stay on the account. Fleet MLOps, monitoring and rollback paths are designed in from the start so the system is operable without us.
Start with a free 30 to 45 minute discovery call. Bring the problem, the recordings you already have and your questions. We will tell you honestly whether sensing-based AI is the right tool and what the next step would cost in days, not months.
The asset or process, what goes wrong, and what recordings you already have. We reply within 48 hours on working days. NDAs are standard.
We have received your enquiry. Our representative will contact you within 48 hours on working days, and a confirmation is on its way to your inbox.
Need us sooner? Write to contactus@acousticailab.com.
