Materialfluss-Optimierung KI — Förderband-Klassifikation

USE CASEANWENDUNGSFALLUSE CASEUSE CASEUSE CASEUSE CASE · Material flow classification · LiDAR + camera on the beltMaterialfluss-Klassifikation · LiDAR und Kamera am BandMaterial flow classification · LiDAR + camera on the beltMaterial flow classification · LiDAR + camera on the beltMaterial flow classification · LiDAR + camera on the beltMaterial flow classification · LiDAR + camera on the belt

A real material balance, not an inferred one. Eine echte Materialbilanz — keine geschätzte. A real material balance, not an inferred one. A real material balance, not an inferred one. A real material balance, not an inferred one. A real material balance, not an inferred one.

Material flow AI is a perception pipeline that runs on top of a conveyor-belt scanner (LiDAR + camera) to continuously classify and analyse the material crossing the belt — ore grade, contamination type, blend ratio — and feeds the result into your blending model, your control room, your material balance.Materialfluss-Optimierung KI ist eine Wahrnehmungs-Pipeline, die auf einem Förderband-Scanner (LiDAR + Kamera) läuft, das Schüttgut am Band kontinuierlich klassifiziert und vermisst — Erzqualität, Kontaminations-Typ, Mischungs-Verhältnis — und das Ergebnis direkt in Mischmodell, Leitwarte und Materialbilanz speist.Material flow AI is a perception pipeline that runs on top of a conveyor-belt scanner (LiDAR + camera) to continuously classify and analyse the material crossing the belt — ore grade, contamination type, blend ratio — and feeds the result into your blending model, your control room, your material balance.Material flow AI is a perception pipeline that runs on top of a conveyor-belt scanner (LiDAR + camera) to continuously classify and analyse the material crossing the belt — ore grade, contamination type, blend ratio — and feeds the result into your blending model, your control room, your material balance.Material flow AI is a perception pipeline that runs on top of a conveyor-belt scanner (LiDAR + camera) to continuously classify and analyse the material crossing the belt — ore grade, contamination type, blend ratio — and feeds the result into your blending model, your control room, your material balance.Material flow AI is a perception pipeline that runs on top of a conveyor-belt scanner (LiDAR + camera) to continuously classify and analyse the material crossing the belt — ore grade, contamination type, blend ratio — and feeds the result into your blending model, your control room, your material balance.

The pain is simple. A belt scale gives you mass per unit time. A volume scanner — like our OWL EYE® Volume Flow — gives you volume and density. Neither tells you what is on the belt. And in cement, sugar, fertiliser, recycling, ore processing and grain handling, that is exactly the question that drives blending, off-spec prevention and contract billing. The control room ends up inferring composition from feeder set-points and lab samples that arrive hours after the shift is over.Der Schmerz ist einfach. Eine Bandwaage liefert Masse pro Zeit. Ein Volumen-Scanner — wie unser OWL EYE® Volume Flow — liefert Volumen und Schüttdichte. Keiner sagt Ihnen, was auf dem Band liegt. Und in Zement, Zucker, Düngemittel, Recycling, Erzaufbereitung und Getreide ist genau das die Frage, die Verblendung, Off-Spec-Vermeidung und Vertrags-Abrechnung treibt. Die Leitwarte schätzt die Zusammensetzung am Ende aus Aufgeber-Sollwerten und Laborproben, die Stunden nach Schichtende eintreffen.The pain is simple. A belt scale gives you mass per unit time. A volume scanner — like our OWL EYE® Volume Flow — gives you volume and density. Neither tells you what is on the belt. And in cement, sugar, fertiliser, recycling, ore processing and grain handling, that is exactly the question that drives blending, off-spec prevention and contract billing. The control room ends up inferring composition from feeder set-points and lab samples that arrive hours after the shift is over.The pain is simple. A belt scale gives you mass per unit time. A volume scanner — like our OWL EYE® Volume Flow — gives you volume and density. Neither tells you what is on the belt. And in cement, sugar, fertiliser, recycling, ore processing and grain handling, that is exactly the question that drives blending, off-spec prevention and contract billing. The control room ends up inferring composition from feeder set-points and lab samples that arrive hours after the shift is over.The pain is simple. A belt scale gives you mass per unit time. A volume scanner — like our OWL EYE® Volume Flow — gives you volume and density. Neither tells you what is on the belt. And in cement, sugar, fertiliser, recycling, ore processing and grain handling, that is exactly the question that drives blending, off-spec prevention and contract billing. The control room ends up inferring composition from feeder set-points and lab samples that arrive hours after the shift is over.The pain is simple. A belt scale gives you mass per unit time. A volume scanner — like our OWL EYE® Volume Flow — gives you volume and density. Neither tells you what is on the belt. And in cement, sugar, fertiliser, recycling, ore processing and grain handling, that is exactly the question that drives blending, off-spec prevention and contract billing. The control room ends up inferring composition from feeder set-points and lab samples that arrive hours after the shift is over.

Our approach starts at the belt cross-section. A 2D LiDAR profiles the geometry of the material flow, a colour camera captures surface signature, and reflectivity at the belt adds a third channel. A per-section classification model — trained on your material categories (ore grades, contamination types, blend components, off-spec patterns) — labels every scan. Results aggregate into per-minute, per-shift and per-day flow reports, and the same stream drives real-time alarms when contamination spikes or the blend drifts off target.Unser Ansatz beginnt am Band-Querschnitt. Ein 2D-LiDAR profiliert die Geometrie des Materialflusses, eine Farbkamera erfasst die Oberflächen-Signatur, und die Reflektivität am Band liefert einen dritten Kanal. Ein eigener Klassifikator pro Querschnitt — trainiert auf Ihren Material-Kategorien (Erzqualitäten, Kontaminations-Typen, Mischungs-Komponenten, Off-Spec-Muster) — labelt jeden Scan. Die Ergebnisse aggregieren zu Minuten-, Schicht- und Tages-Flussberichten, und derselbe Strom triggert Echtzeit-Alarme, wenn die Kontamination steigt oder die Mischung vom Ziel abweicht.Our approach starts at the belt cross-section. A 2D LiDAR profiles the geometry of the material flow, a colour camera captures surface signature, and reflectivity at the belt adds a third channel. A per-section classification model — trained on your material categories (ore grades, contamination types, blend components, off-spec patterns) — labels every scan. Results aggregate into per-minute, per-shift and per-day flow reports, and the same stream drives real-time alarms when contamination spikes or the blend drifts off target.Our approach starts at the belt cross-section. A 2D LiDAR profiles the geometry of the material flow, a colour camera captures surface signature, and reflectivity at the belt adds a third channel. A per-section classification model — trained on your material categories (ore grades, contamination types, blend components, off-spec patterns) — labels every scan. Results aggregate into per-minute, per-shift and per-day flow reports, and the same stream drives real-time alarms when contamination spikes or the blend drifts off target.Our approach starts at the belt cross-section. A 2D LiDAR profiles the geometry of the material flow, a colour camera captures surface signature, and reflectivity at the belt adds a third channel. A per-section classification model — trained on your material categories (ore grades, contamination types, blend components, off-spec patterns) — labels every scan. Results aggregate into per-minute, per-shift and per-day flow reports, and the same stream drives real-time alarms when contamination spikes or the blend drifts off target.Our approach starts at the belt cross-section. A 2D LiDAR profiles the geometry of the material flow, a colour camera captures surface signature, and reflectivity at the belt adds a third channel. A per-section classification model — trained on your material categories (ore grades, contamination types, blend components, off-spec patterns) — labels every scan. Results aggregate into per-minute, per-shift and per-day flow reports, and the same stream drives real-time alarms when contamination spikes or the blend drifts off target.

This is not an off-the-shelf SaaS. Every plant has its own feed sources, its own blend recipe and its own control-room stack. We treat every engagement as a discovery + fixed-scope build, descending from our hub service Industrial Perception AI. Often layered on top of an existing OWL EYE® Volume Flow or Stockpile installation — same sensor, additional software layer.Das ist kein SaaS von der Stange. Jede Anlage hat eigene Aufgabe-Quellen, eigene Mischungs-Rezeptur und eigenen Leitwarten-Stack. Wir behandeln jedes Projekt als Discovery + Festscope-Build, abgeleitet aus unserem Hub-Service Industrielle Objekterkennung. Häufig als Software-Layer auf einer bestehenden OWL EYE® Volume Flow oder Stockpile-Installation — selber Sensor, zusätzliche Software.This is not an off-the-shelf SaaS. Every plant has its own feed sources, its own blend recipe and its own control-room stack. We treat every engagement as a discovery + fixed-scope build, descending from our hub service Industrial Perception AI. Often layered on top of an existing OWL EYE® Volume Flow or Stockpile installation — same sensor, additional software layer.This is not an off-the-shelf SaaS. Every plant has its own feed sources, its own blend recipe and its own control-room stack. We treat every engagement as a discovery + fixed-scope build, descending from our hub service Industrial Perception AI. Often layered on top of an existing OWL EYE® Volume Flow or Stockpile installation — same sensor, additional software layer.This is not an off-the-shelf SaaS. Every plant has its own feed sources, its own blend recipe and its own control-room stack. We treat every engagement as a discovery + fixed-scope build, descending from our hub service Industrial Perception AI. Often layered on top of an existing OWL EYE® Volume Flow or Stockpile installation — same sensor, additional software layer.This is not an off-the-shelf SaaS. Every plant has its own feed sources, its own blend recipe and its own control-room stack. We treat every engagement as a discovery + fixed-scope build, descending from our hub service Industrial Perception AI. Often layered on top of an existing OWL EYE® Volume Flow or Stockpile installation — same sensor, additional software layer.

„A belt scale tells you how much. A volume scanner tells you how big. Neither tells you what — and 'what' is what drives the blend." „Die Bandwaage sagt Ihnen wie viel. Der Volumen-Scanner sagt Ihnen wie groß. Keiner sagt Ihnen, was — und 'was' treibt die Mischung." „A belt scale tells you how much. A volume scanner tells you how big. Neither tells you what — and 'what' is what drives the blend." „A belt scale tells you how much. A volume scanner tells you how big. Neither tells you what — and 'what' is what drives the blend." „A belt scale tells you how much. A volume scanner tells you how big. Neither tells you what — and 'what' is what drives the blend." „A belt scale tells you how much. A volume scanner tells you how big. Neither tells you what — and 'what' is what drives the blend."

Built on the same perception stack that powers OWL EYE® Volume Flow and Stockpile in production at industrial sites today. Gebaut auf demselben Wahrnehmungs-Stack, der OWL EYE® Volume Flow und Stockpile heute in industriellen Anlagen produktiv betreibt. Built on the same perception stack that powers OWL EYE® Volume Flow and Stockpile in production at industrial sites today. Built on the same perception stack that powers OWL EYE® Volume Flow and Stockpile in production at industrial sites today. Built on the same perception stack that powers OWL EYE® Volume Flow and Stockpile in production at industrial sites today. Built on the same perception stack that powers OWL EYE® Volume Flow and Stockpile in production at industrial sites today.

Three stagesDrei StufenThree stagesThree stagesThree stagesThree stages

From belt to blender — three stages of the pipeline. Vom Band zum Mischer — drei Stufen der Pipeline. From belt to blender — three stages of the pipeline. From belt to blender — three stages of the pipeline. From belt to blender — three stages of the pipeline. From belt to blender — three stages of the pipeline.

1 · Sensor at the belt1 · Sensor am Band1 · Sensor at the belt1 · Sensor at the belt1 · Sensor at the belt1 · Sensor at the belt

A 2D LiDAR for geometry plus a colour camera for material signature, mounted over the belt or at a transfer point. Often the same sensor pair already installed for OWL EYE® Volume Flow — one mechanical install, two pipelines. IP65+ housings, dust-tolerant optics, runs through the full shift in mill, mine and recycling-plant conditions. Ein 2D-LiDAR für die Geometrie plus eine Farbkamera für die Material-Signatur, montiert über dem Band oder am Übergabepunkt. Häufig dasselbe Sensor-Paar, das bereits für OWL EYE® Volume Flow installiert ist — eine mechanische Montage, zwei Pipelines. IP65+-Gehäuse, staubtolerante Optik, läuft die ganze Schicht in Mühle, Bergwerk und Recyclinganlage. A 2D LiDAR for geometry plus a colour camera for material signature, mounted over the belt or at a transfer point. Often the same sensor pair already installed for OWL EYE® Volume Flow — one mechanical install, two pipelines. IP65+ housings, dust-tolerant optics, runs through the full shift in mill, mine and recycling-plant conditions. A 2D LiDAR for geometry plus a colour camera for material signature, mounted over the belt or at a transfer point. Often the same sensor pair already installed for OWL EYE® Volume Flow — one mechanical install, two pipelines. IP65+ housings, dust-tolerant optics, runs through the full shift in mill, mine and recycling-plant conditions. A 2D LiDAR for geometry plus a colour camera for material signature, mounted over the belt or at a transfer point. Often the same sensor pair already installed for OWL EYE® Volume Flow — one mechanical install, two pipelines. IP65+ housings, dust-tolerant optics, runs through the full shift in mill, mine and recycling-plant conditions. A 2D LiDAR for geometry plus a colour camera for material signature, mounted over the belt or at a transfer point. Often the same sensor pair already installed for OWL EYE® Volume Flow — one mechanical install, two pipelines. IP65+ housings, dust-tolerant optics, runs through the full shift in mill, mine and recycling-plant conditions.

2 · Per-section classification2 · Klassifikation pro Querschnitt2 · Per-section classification2 · Per-section classification2 · Per-section classification2 · Per-section classification

The classifier model labels every belt cross-section against your material categories — ore grades, contamination types, blend components, off-spec patterns. Models retrained on your labelled data, not on a public benchmark. Aggregated to per-second, per-minute, per-shift and per-day flow records ready for the control room and the historian. Der Klassifikator labelt jeden Band-Querschnitt gegen Ihre Material-Kategorien — Erzqualitäten, Kontaminations-Typen, Mischungs-Komponenten, Off-Spec-Muster. Modelle trainiert auf Ihren gelabelten Daten — nicht auf einem öffentlichen Benchmark. Aggregiert zu Sekunden-, Minuten-, Schicht- und Tages-Datensätzen, fertig für Leitwarte und Historian. The classifier model labels every belt cross-section against your material categories — ore grades, contamination types, blend components, off-spec patterns. Models retrained on your labelled data, not on a public benchmark. Aggregated to per-second, per-minute, per-shift and per-day flow records ready for the control room and the historian. The classifier model labels every belt cross-section against your material categories — ore grades, contamination types, blend components, off-spec patterns. Models retrained on your labelled data, not on a public benchmark. Aggregated to per-second, per-minute, per-shift and per-day flow records ready for the control room and the historian. The classifier model labels every belt cross-section against your material categories — ore grades, contamination types, blend components, off-spec patterns. Models retrained on your labelled data, not on a public benchmark. Aggregated to per-second, per-minute, per-shift and per-day flow records ready for the control room and the historian. The classifier model labels every belt cross-section against your material categories — ore grades, contamination types, blend components, off-spec patterns. Models retrained on your labelled data, not on a public benchmark. Aggregated to per-second, per-minute, per-shift and per-day flow records ready for the control room and the historian.

3 · Blend feedback + alarms3 · Misch-Rückführung + Alarme3 · Blend feedback + alarms3 · Blend feedback + alarms3 · Blend feedback + alarms3 · Blend feedback + alarms

Classification results write into the blending PLC, the historian and the ERP material balance — OPC UA, MQTT, REST. The blender sees real composition instead of feeder set-points. Contamination spikes and off-spec mixes trigger control-room alarms before the bad batch reaches the silo or the customer. Klassifikations-Ergebnisse schreiben in die Misch-SPS, den Historian und die ERP-Materialbilanz — OPC UA, MQTT, REST. Der Mischer sieht echte Zusammensetzung statt Aufgeber-Sollwerten. Kontaminations-Spitzen und Off-Spec-Mischungen lösen Leitwarten-Alarme aus, bevor die schlechte Charge ins Silo oder zum Kunden geht. Classification results write into the blending PLC, the historian and the ERP material balance — OPC UA, MQTT, REST. The blender sees real composition instead of feeder set-points. Contamination spikes and off-spec mixes trigger control-room alarms before the bad batch reaches the silo or the customer. Classification results write into the blending PLC, the historian and the ERP material balance — OPC UA, MQTT, REST. The blender sees real composition instead of feeder set-points. Contamination spikes and off-spec mixes trigger control-room alarms before the bad batch reaches the silo or the customer. Classification results write into the blending PLC, the historian and the ERP material balance — OPC UA, MQTT, REST. The blender sees real composition instead of feeder set-points. Contamination spikes and off-spec mixes trigger control-room alarms before the bad batch reaches the silo or the customer. Classification results write into the blending PLC, the historian and the ERP material balance — OPC UA, MQTT, REST. The blender sees real composition instead of feeder set-points. Contamination spikes and off-spec mixes trigger control-room alarms before the bad batch reaches the silo or the customer.

Pipeline architecture · #how-it-worksPipeline-Architektur · #how-it-worksPipeline architecture · #how-it-worksPipeline architecture · #how-it-worksPipeline architecture · #how-it-worksPipeline architecture · #how-it-works

How the pipeline works — concretely. Wie die Pipeline arbeitet — konkret. How the pipeline works — concretely. How the pipeline works — concretely. How the pipeline works — concretely. How the pipeline works — concretely.

We keep the architecture boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the sensor stack or the recipe changes. Built on the same stack we use across all our perception work: PCL, Open3D, OpenCV, PyTorch. Wir halten die Architektur absichtlich langweilig. Drei lose gekoppelte Stufen, jede einzeln testbar, jede einzeln tauschbar, wenn sich der Sensor-Stack oder die Rezeptur ändert. Gebaut auf demselben Stack, den wir für unsere gesamte Wahrnehmungs-Arbeit nutzen: PCL, Open3D, OpenCV, PyTorch. We keep the architecture boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the sensor stack or the recipe changes. Built on the same stack we use across all our perception work: PCL, Open3D, OpenCV, PyTorch. We keep the architecture boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the sensor stack or the recipe changes. Built on the same stack we use across all our perception work: PCL, Open3D, OpenCV, PyTorch. We keep the architecture boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the sensor stack or the recipe changes. Built on the same stack we use across all our perception work: PCL, Open3D, OpenCV, PyTorch. We keep the architecture boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the sensor stack or the recipe changes. Built on the same stack we use across all our perception work: PCL, Open3D, OpenCV, PyTorch.

Belt cross-section captureBand-Querschnitt-AufnahmeBelt cross-section captureBelt cross-section captureBelt cross-section captureBelt cross-section capture

The 2D LiDAR profiles the belt at 10–50 Hz, producing overlapping cross-sections of the material flow. The colour camera adds a synchronised, lens-calibrated frame of the same patch of belt. Belt-speed input from the existing encoder ties geometry to mass and aligns frames into a continuous longitudinal record. Raw data writes to local storage with a per-section UUID for audit trail and re-labelling. Der 2D-LiDAR profiliert das Band mit 10–50 Hz und erzeugt überlappende Querschnitte des Materialflusses. Die Kamera ergänzt ein synchronisiertes, objektiv-kalibriertes Bild derselben Band-Stelle. Die Bandgeschwindigkeit aus dem vorhandenen Encoder verknüpft Geometrie und Masse und richtet die Frames zu einer durchgehenden Längs-Aufzeichnung aus. Rohdaten schreiben auf lokalen Speicher mit pro-Querschnitt-UUID für Audit-Trail und Re-Labelling. The 2D LiDAR profiles the belt at 10–50 Hz, producing overlapping cross-sections of the material flow. The colour camera adds a synchronised, lens-calibrated frame of the same patch of belt. Belt-speed input from the existing encoder ties geometry to mass and aligns frames into a continuous longitudinal record. Raw data writes to local storage with a per-section UUID for audit trail and re-labelling. The 2D LiDAR profiles the belt at 10–50 Hz, producing overlapping cross-sections of the material flow. The colour camera adds a synchronised, lens-calibrated frame of the same patch of belt. Belt-speed input from the existing encoder ties geometry to mass and aligns frames into a continuous longitudinal record. Raw data writes to local storage with a per-section UUID for audit trail and re-labelling. The 2D LiDAR profiles the belt at 10–50 Hz, producing overlapping cross-sections of the material flow. The colour camera adds a synchronised, lens-calibrated frame of the same patch of belt. Belt-speed input from the existing encoder ties geometry to mass and aligns frames into a continuous longitudinal record. Raw data writes to local storage with a per-section UUID for audit trail and re-labelling. The 2D LiDAR profiles the belt at 10–50 Hz, producing overlapping cross-sections of the material flow. The colour camera adds a synchronised, lens-calibrated frame of the same patch of belt. Belt-speed input from the existing encoder ties geometry to mass and aligns frames into a continuous longitudinal record. Raw data writes to local storage with a per-section UUID for audit trail and re-labelling.

Per-section classification + aggregationKlassifikation pro Querschnitt + AggregationPer-section classification + aggregationPer-section classification + aggregationPer-section classification + aggregationPer-section classification + aggregation

The point cross-section goes through ground removal, voxel down-sampling and a PointNet-style classifier trained on your material categories. The colour frame goes through a CNN trained on the same labels. Outputs fuse into one per-section prediction — typically 85–95 % classification accuracy on production data, depending on how clean your material categories actually are — then aggregate to per-second, per-minute, per-shift and per-day rollups. Der Punkt-Querschnitt läuft durch Boden-Entfernung, Voxel-Downsampling und einen PointNet-artigen Klassifikator, trainiert auf Ihren Material-Kategorien. Das Farbbild läuft durch ein CNN, trainiert auf denselben Labels. Beide Köpfe werden zu einer Vorhersage pro Querschnitt fusioniert — typisch 85–95 % Klassifikations-Genauigkeit auf Produktionsdaten, abhängig davon, wie sauber Ihre Material-Kategorien tatsächlich abgegrenzt sind — und aggregieren dann zu Sekunden-, Minuten-, Schicht- und Tages-Rollups. The point cross-section goes through ground removal, voxel down-sampling and a PointNet-style classifier trained on your material categories. The colour frame goes through a CNN trained on the same labels. Outputs fuse into one per-section prediction — typically 85–95 % classification accuracy on production data, depending on how clean your material categories actually are — then aggregate to per-second, per-minute, per-shift and per-day rollups. The point cross-section goes through ground removal, voxel down-sampling and a PointNet-style classifier trained on your material categories. The colour frame goes through a CNN trained on the same labels. Outputs fuse into one per-section prediction — typically 85–95 % classification accuracy on production data, depending on how clean your material categories actually are — then aggregate to per-second, per-minute, per-shift and per-day rollups. The point cross-section goes through ground removal, voxel down-sampling and a PointNet-style classifier trained on your material categories. The colour frame goes through a CNN trained on the same labels. Outputs fuse into one per-section prediction — typically 85–95 % classification accuracy on production data, depending on how clean your material categories actually are — then aggregate to per-second, per-minute, per-shift and per-day rollups. The point cross-section goes through ground removal, voxel down-sampling and a PointNet-style classifier trained on your material categories. The colour frame goes through a CNN trained on the same labels. Outputs fuse into one per-section prediction — typically 85–95 % classification accuracy on production data, depending on how clean your material categories actually are — then aggregate to per-second, per-minute, per-shift and per-day rollups.

Blender / control-room write-backRück-Schreibung in Mischer und LeitwarteBlender / control-room write-backBlender / control-room write-backBlender / control-room write-backBlender / control-room write-back

Per-second output: class label and confidence, mass per category, contamination flags. Writes into the blending PLC, the DCS, the historian and the ERP material balance over OPC UA, MQTT or REST — whatever the plant already runs. The blending model now reads real composition instead of inferring it from feeder set-points and delayed lab samples. Pro Sekunde: Klassen-Label und Konfidenz, Masse je Kategorie, Kontaminations-Flags. Schreibt in die Misch-SPS, das DCS, den Historian und die ERP-Materialbilanz per OPC UA, MQTT oder REST — was die Anlage eben hat. Das Mischmodell liest jetzt echte Zusammensetzung statt sie aus Aufgeber-Sollwerten und verspäteten Laborproben zu schätzen. Per-second output: class label and confidence, mass per category, contamination flags. Writes into the blending PLC, the DCS, the historian and the ERP material balance over OPC UA, MQTT or REST — whatever the plant already runs. The blending model now reads real composition instead of inferring it from feeder set-points and delayed lab samples. Per-second output: class label and confidence, mass per category, contamination flags. Writes into the blending PLC, the DCS, the historian and the ERP material balance over OPC UA, MQTT or REST — whatever the plant already runs. The blending model now reads real composition instead of inferring it from feeder set-points and delayed lab samples. Per-second output: class label and confidence, mass per category, contamination flags. Writes into the blending PLC, the DCS, the historian and the ERP material balance over OPC UA, MQTT or REST — whatever the plant already runs. The blending model now reads real composition instead of inferring it from feeder set-points and delayed lab samples. Per-second output: class label and confidence, mass per category, contamination flags. Writes into the blending PLC, the DCS, the historian and the ERP material balance over OPC UA, MQTT or REST — whatever the plant already runs. The blending model now reads real composition instead of inferring it from feeder set-points and delayed lab samples.

All three stages run on an industrial PC at the belt cabinet. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. Alle drei Stufen laufen auf einem Industrie-PC im Band-Schaltschrank. Keine Cloud-Abhängigkeit, keine externe API, kein Lizenz-Heimruf. Der Code gehört Ihnen bei der Übergabe. All three stages run on an industrial PC at the belt cabinet. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. All three stages run on an industrial PC at the belt cabinet. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. All three stages run on an industrial PC at the belt cabinet. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. All three stages run on an industrial PC at the belt cabinet. No cloud dependency, no external API, no licence dial-home. The code is yours at handover.

What you getWas Sie bekommenWhat you getWhat you getWhat you getWhat you get

Three deliverables — from the same pipeline. Drei Liefer-Ergebnisse — aus derselben Pipeline. Three deliverables — from the same pipeline. Three deliverables — from the same pipeline. Three deliverables — from the same pipeline. Three deliverables — from the same pipeline.

Per-section classification streamKlassifikations-Strom pro QuerschnittPer-section classification streamPer-section classification streamPer-section classification streamPer-section classification stream

Every belt scan gets a structured record: class label, confidence, estimated mass per category, contamination flags, audit image. Published continuously over OPC UA or MQTT, archived to the historian, and ready for cross-link with belt-scale and feeder records. Jeder Band-Scan erhält einen strukturierten Datensatz: Klassen-Label, Konfidenz, geschätzte Masse je Kategorie, Kontaminations-Flags, Audit-Bild. Wird laufend per OPC UA oder MQTT veröffentlicht, im Historian archiviert und ist bereit für die Verknüpfung mit Bandwaage- und Aufgeber-Daten. Every belt scan gets a structured record: class label, confidence, estimated mass per category, contamination flags, audit image. Published continuously over OPC UA or MQTT, archived to the historian, and ready for cross-link with belt-scale and feeder records. Every belt scan gets a structured record: class label, confidence, estimated mass per category, contamination flags, audit image. Published continuously over OPC UA or MQTT, archived to the historian, and ready for cross-link with belt-scale and feeder records. Every belt scan gets a structured record: class label, confidence, estimated mass per category, contamination flags, audit image. Published continuously over OPC UA or MQTT, archived to the historian, and ready for cross-link with belt-scale and feeder records. Every belt scan gets a structured record: class label, confidence, estimated mass per category, contamination flags, audit image. Published continuously over OPC UA or MQTT, archived to the historian, and ready for cross-link with belt-scale and feeder records.

Aggregated flow reportAggregierter Fluss-BerichtAggregated flow reportAggregated flow reportAggregated flow reportAggregated flow report

Material composition rolled up per minute, per shift and per day — comparable across feeders, belts and product lines. Same temporal model we use on stockpile monitoring — applied to belt flow. Drops straight into the production-quality dashboard and the contract-billing record. Material-Zusammensetzung pro Minute, pro Schicht und pro Tag — vergleichbar über Aufgeber, Bänder und Produktlinien hinweg. Dasselbe zeitliche Modell, das wir auf Halden-Monitoring einsetzen — übertragen auf den Band-Fluss. Geht direkt ins Produktions-Qualitäts-Dashboard und in die Vertrags-Abrechnung. Material composition rolled up per minute, per shift and per day — comparable across feeders, belts and product lines. Same temporal model we use on stockpile monitoring — applied to belt flow. Drops straight into the production-quality dashboard and the contract-billing record. Material composition rolled up per minute, per shift and per day — comparable across feeders, belts and product lines. Same temporal model we use on stockpile monitoring — applied to belt flow. Drops straight into the production-quality dashboard and the contract-billing record. Material composition rolled up per minute, per shift and per day — comparable across feeders, belts and product lines. Same temporal model we use on stockpile monitoring — applied to belt flow. Drops straight into the production-quality dashboard and the contract-billing record. Material composition rolled up per minute, per shift and per day — comparable across feeders, belts and product lines. Same temporal model we use on stockpile monitoring — applied to belt flow. Drops straight into the production-quality dashboard and the contract-billing record.

Real-time contamination + off-spec alarmsEchtzeit-Alarme bei Kontamination + Off-SpecReal-time contamination + off-spec alarmsReal-time contamination + off-spec alarmsReal-time contamination + off-spec alarmsReal-time contamination + off-spec alarms

When contamination spikes or the blend drifts off target, the system raises a control-room alarm before the bad batch reaches the silo, the rail wagon or the customer. Routed into the existing DCS alarm list — no new operator screen to learn, no parallel dashboard to ignore. Wenn Kontamination steigt oder die Mischung vom Ziel abdriftet, löst das System einen Leitwarten-Alarm aus, bevor die schlechte Charge ins Silo, in den Bahnwaggon oder zum Kunden geht. Eingespeist in die bestehende DCS-Alarm-Liste — kein neuer Bediener-Screen zum Lernen, kein paralleles Dashboard zum Ignorieren. When contamination spikes or the blend drifts off target, the system raises a control-room alarm before the bad batch reaches the silo, the rail wagon or the customer. Routed into the existing DCS alarm list — no new operator screen to learn, no parallel dashboard to ignore. When contamination spikes or the blend drifts off target, the system raises a control-room alarm before the bad batch reaches the silo, the rail wagon or the customer. Routed into the existing DCS alarm list — no new operator screen to learn, no parallel dashboard to ignore. When contamination spikes or the blend drifts off target, the system raises a control-room alarm before the bad batch reaches the silo, the rail wagon or the customer. Routed into the existing DCS alarm list — no new operator screen to learn, no parallel dashboard to ignore. When contamination spikes or the blend drifts off target, the system raises a control-room alarm before the bad batch reaches the silo, the rail wagon or the customer. Routed into the existing DCS alarm list — no new operator screen to learn, no parallel dashboard to ignore.

Why customWarum CustomWhy customWhy customWhy customWhy custom

Why a custom build — not an off-the-shelf product. Warum nach Maß — und nicht von der Stange. Why a custom build — not an off-the-shelf product. Why a custom build — not an off-the-shelf product. Why a custom build — not an off-the-shelf product. Why a custom build — not an off-the-shelf product.

Every plant has its own feed sources, its own blend recipe and its own control-room stack. A generic classifier solves the generic case; your case is not the generic case. Jede Anlage hat eigene Aufgabe-Quellen, eine eigene Mischungs-Rezeptur und einen eigenen Leitwarten-Stack. Ein generischer Klassifikator löst den generischen Fall; Ihr Fall ist nicht der generische Fall. Every plant has its own feed sources, its own blend recipe and its own control-room stack. A generic classifier solves the generic case; your case is not the generic case. Every plant has its own feed sources, its own blend recipe and its own control-room stack. A generic classifier solves the generic case; your case is not the generic case. Every plant has its own feed sources, its own blend recipe and its own control-room stack. A generic classifier solves the generic case; your case is not the generic case. Every plant has its own feed sources, its own blend recipe and its own control-room stack. A generic classifier solves the generic case; your case is not the generic case.

Material-specific classificationMaterial-spezifische KlassifikationMaterial-specific classificationMaterial-specific classificationMaterial-specific classificationMaterial-specific classification

Your blend, your contamination definitions, your off-spec patterns. We calibrate the classifier on your material under your lighting and dust conditions, and we re-calibrate when the feed source or recipe changes. A cement raw-mix classifier and a copper-ore-grade classifier share the same architecture and nothing else. Ihre Mischung, Ihre Kontaminations-Definitionen, Ihre Off-Spec-Muster. Wir kalibrieren den Klassifikator auf Ihrem Material unter Ihrem Licht und Staub — und wir re-kalibrieren, wenn sich Aufgabe-Quelle oder Rezeptur ändert. Ein Zement-Rohmehl-Klassifikator und ein Kupfererz-Qualitäts-Klassifikator teilen die Architektur und nichts weiter. Your blend, your contamination definitions, your off-spec patterns. We calibrate the classifier on your material under your lighting and dust conditions, and we re-calibrate when the feed source or recipe changes. A cement raw-mix classifier and a copper-ore-grade classifier share the same architecture and nothing else. Your blend, your contamination definitions, your off-spec patterns. We calibrate the classifier on your material under your lighting and dust conditions, and we re-calibrate when the feed source or recipe changes. A cement raw-mix classifier and a copper-ore-grade classifier share the same architecture and nothing else. Your blend, your contamination definitions, your off-spec patterns. We calibrate the classifier on your material under your lighting and dust conditions, and we re-calibrate when the feed source or recipe changes. A cement raw-mix classifier and a copper-ore-grade classifier share the same architecture and nothing else. Your blend, your contamination definitions, your off-spec patterns. We calibrate the classifier on your material under your lighting and dust conditions, and we re-calibrate when the feed source or recipe changes. A cement raw-mix classifier and a copper-ore-grade classifier share the same architecture and nothing else.

Builds on OWL EYE® Volume FlowBaut auf OWL EYE® Volume Flow aufBuilds on OWL EYE® Volume FlowBuilds on OWL EYE® Volume FlowBuilds on OWL EYE® Volume FlowBuilds on OWL EYE® Volume Flow

If you already run OWL EYE® Volume Flow on the belt, the AI layer reuses the same sensor — one mechanical install, two pipelines. Volume and mass continue to flow into the belt-scale and inventory record; classification flows into the blender and the contamination alarm. No second portal, no second cabinet. Wenn auf dem Band bereits OWL EYE® Volume Flow läuft, nutzt der KI-Layer denselben Sensor — eine mechanische Montage, zwei Pipelines. Volumen und Masse fließen weiter in Bandwaage und Bestands-Aufzeichnung; die Klassifikation fließt in den Mischer und in den Kontaminations-Alarm. Kein zweites Portal, kein zweiter Schaltschrank. If you already run OWL EYE® Volume Flow on the belt, the AI layer reuses the same sensor — one mechanical install, two pipelines. Volume and mass continue to flow into the belt-scale and inventory record; classification flows into the blender and the contamination alarm. No second portal, no second cabinet. If you already run OWL EYE® Volume Flow on the belt, the AI layer reuses the same sensor — one mechanical install, two pipelines. Volume and mass continue to flow into the belt-scale and inventory record; classification flows into the blender and the contamination alarm. No second portal, no second cabinet. If you already run OWL EYE® Volume Flow on the belt, the AI layer reuses the same sensor — one mechanical install, two pipelines. Volume and mass continue to flow into the belt-scale and inventory record; classification flows into the blender and the contamination alarm. No second portal, no second cabinet. If you already run OWL EYE® Volume Flow on the belt, the AI layer reuses the same sensor — one mechanical install, two pipelines. Volume and mass continue to flow into the belt-scale and inventory record; classification flows into the blender and the contamination alarm. No second portal, no second cabinet.

Plant-specific blender + ERP integrationAnlagenspezifische Mischer- und ERP-AnbindungPlant-specific blender + ERP integrationPlant-specific blender + ERP integrationPlant-specific blender + ERP integrationPlant-specific blender + ERP integration

We integrate with your blending PLC, your historian, your ERP material balance — OPC UA, MQTT, REST, analog outputs all supported. No generic "let's send you a CSV every hour". The blender reads classification at control-loop rates; the ERP reads aggregated composition per shift; the historian keeps the per-section record for audit. Wir binden an Ihre Misch-SPS, Ihren Historian, Ihre ERP-Materialbilanz an — OPC UA, MQTT, REST und Analog-Ausgänge alle unterstützt. Kein generisches „wir schicken Ihnen stündlich eine CSV". Der Mischer liest Klassifikation in Regelkreis-Takt; das ERP liest aggregierte Zusammensetzung pro Schicht; der Historian hält den Querschnitt-Datensatz fürs Audit. We integrate with your blending PLC, your historian, your ERP material balance — OPC UA, MQTT, REST, analog outputs all supported. No generic "let's send you a CSV every hour". The blender reads classification at control-loop rates; the ERP reads aggregated composition per shift; the historian keeps the per-section record for audit. We integrate with your blending PLC, your historian, your ERP material balance — OPC UA, MQTT, REST, analog outputs all supported. No generic "let's send you a CSV every hour". The blender reads classification at control-loop rates; the ERP reads aggregated composition per shift; the historian keeps the per-section record for audit. We integrate with your blending PLC, your historian, your ERP material balance — OPC UA, MQTT, REST, analog outputs all supported. No generic "let's send you a CSV every hour". The blender reads classification at control-loop rates; the ERP reads aggregated composition per shift; the historian keeps the per-section record for audit. We integrate with your blending PLC, your historian, your ERP material balance — OPC UA, MQTT, REST, analog outputs all supported. No generic "let's send you a CSV every hour". The blender reads classification at control-loop rates; the ERP reads aggregated composition per shift; the historian keeps the per-section record for audit.

IP ownership + clean handoverIP-Eigentum + saubere ÜbergabeIP ownership + clean handoverIP ownership + clean handoverIP ownership + clean handoverIP ownership + clean handover

You own the source code, the model weights and the labelled dataset at handover. We document the system, train your team, and walk away clean. No black box, no monthly per-tonne licence, no service contract you can't exit. See our FAQs on IP and engagement model for the standard terms. Sie besitzen Quellcode, Modell-Gewichte und gelabelten Datensatz nach der Übergabe. Wir dokumentieren das System, schulen Ihr Team und gehen sauber raus. Keine Black Box, keine monatliche Pro-Tonnen-Lizenz, kein Servicevertrag, aus dem Sie nicht rauskommen. Standard-Bedingungen siehe unsere FAQs zu IP und Zusammenarbeits-Modell. You own the source code, the model weights and the labelled dataset at handover. We document the system, train your team, and walk away clean. No black box, no monthly per-tonne licence, no service contract you can't exit. See our FAQs on IP and engagement model for the standard terms. You own the source code, the model weights and the labelled dataset at handover. We document the system, train your team, and walk away clean. No black box, no monthly per-tonne licence, no service contract you can't exit. See our FAQs on IP and engagement model for the standard terms. You own the source code, the model weights and the labelled dataset at handover. We document the system, train your team, and walk away clean. No black box, no monthly per-tonne licence, no service contract you can't exit. See our FAQs on IP and engagement model for the standard terms. You own the source code, the model weights and the labelled dataset at handover. We document the system, train your team, and walk away clean. No black box, no monthly per-tonne licence, no service contract you can't exit. See our FAQs on IP and engagement model for the standard terms.

FAQ

Questions about material flow AI. Fragen zur Materialfluss-KI. Questions about material flow AI. Questions about material flow AI. Questions about material flow AI. Questions about material flow AI.

The engagement-model questions we hear from every plant considering a custom perception build on top of OWL EYE® Volume Flow. Need something more specific to your belt? Ask us. Die Fragen zum Zusammenarbeits-Modell, die wir von jedem Anlagen-Kunden hören, der einen Custom-Perception-Build auf OWL EYE® Volume Flow erwägt. Brauchen Sie etwas Spezifischeres zu Ihrem Band? Sprechen Sie uns an. The engagement-model questions we hear from every plant considering a custom perception build on top of OWL EYE® Volume Flow. Need something more specific to your belt? Ask us. The engagement-model questions we hear from every plant considering a custom perception build on top of OWL EYE® Volume Flow. Need something more specific to your belt? Ask us. The engagement-model questions we hear from every plant considering a custom perception build on top of OWL EYE® Volume Flow. Need something more specific to your belt? Ask us. The engagement-model questions we hear from every plant considering a custom perception build on top of OWL EYE® Volume Flow. Need something more specific to your belt? Ask us.

Industrial Perception AIIndustrielle ObjekterkennungIndustrial Perception AIIndustrial Perception AIIndustrial Perception AIIndustrial Perception AI

What does custom perception AI software cost?Was kostet maßgeschneiderte Wahrnehmungs-Software?What does custom perception AI software cost?What does custom perception AI software cost?What does custom perception AI software cost?What does custom perception AI software cost? ＋

 We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
Wir veröffentlichen keine Listenpreise, weil jedes Projekt auf Ihre Daten und Ihre Entscheidungs-Logik zugeschnitten wird — aber wir arbeiten in drei berechenbaren Tiers.

Discovery & Assessment — ein 1–3-tägiger Workshop, vor Ort oder remote, Festpreis im unteren vierstelligen Bereich. Sie erhalten eine schriftliche Machbarkeits-Note, einen empfohlenen nächsten Schritt und ggf. ein Festpreis-Angebot für das Folge-Projekt. Lohnt sich auch ohne Folge-Auftrag — viele Kunden nutzen die Note, um zwei oder drei Anbieter zu vergleichen.

Custom Pipeline / Tool — ein 4–12-Wochen-Projekt, das eine fertige Wahrnehmungs-Pipeline liefert (Parser + Algorithmus + Dashboard oder OPC-UA-Anbindung). Fester Scope, Festpreis, Code-Übergabe am Ende. Übliche Projektgröße liegt im mittleren fünf- bis unteren sechsstelligen Bereich, je nach Datenvolumen, Sensor-Anzahl und Integrations-Tiefe.

Langfristige Partnerschaft — monatlicher Retainer für laufende Entwicklung und On-Call-Support. Dimensioniert nach benötigter Team-Kapazität (typisch 0,5–2 FTE-Äquivalente). Quartalsweise Roadmap inklusive.

Alle drei Tiers enthalten Quellcode, schriftliche Dokumentation und saubere Übergabe — keine Lizenz-Abhängigkeit von uns. Vollständiges Zusammenarbeits-Modell siehe unsere Industrielle Objekterkennung.
We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.

How long does it take to build a custom perception pipeline?Wie lange dauert eine maßgeschneiderte Wahrnehmungs-Pipeline?How long does it take to build a custom perception pipeline?How long does it take to build a custom perception pipeline?How long does it take to build a custom perception pipeline?How long does it take to build a custom perception pipeline? ＋

 From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
Vom unterzeichneten Vertrag bis zum produktiven Tool rechnen Sie mit 4–12 Wochen für ein Standard-Custom-Pipeline-Projekt. Die Spanne wird von drei Faktoren bestimmt.

Datenlage. Wenn Sie bereits gelabelte Daten oder repräsentative Aufnahmen haben, starten wir in Woche 1 mit dem Training. Müssen wir Datenerfassung aufsetzen, einen Sensor ergänzen oder von Null labeln, kommt vorne 1–3 Wochen dazu.

Integrations-Tiefe. Ein eigenständiges Dashboard mit REST-Schnittstelle ist der schnellste Weg — typisch 4–6 Wochen. OPC-UA-Anbindung in eine laufende Leittechnik, mit Verriegelungen, Werks-Abnahme und kundenseitigem Change-Management, eher 8–12 Wochen.

Sensor-Anzahl und Anlagen-Komplexität. Ein LiDAR + eine Kamera in einer sauberen Innen-Umgebung geht schneller als ein 6-Sensor-Portal im Freien mit Staub, Schnee und wechselndem Licht. Den Unterschied schneiden wir vorab im Discovery-Workshop sauber zu.

Für ein schnelleres erstes Signal empfehlen wir das Discovery & Assessment-Tier — 1–3 Tage, Festpreis, schriftliche Machbarkeits-Note innerhalb einer Woche. Vollständiges Modell siehe Industrielle Objekterkennung.
From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.

Who owns the code and IP we pay you to build?Wem gehören Code und IP, die wir bei Ihnen beauftragen?Who owns the code and IP we pay you to build?Who owns the code and IP we pay you to build?Who owns the code and IP we pay you to build?Who owns the code and IP we pay you to build? ＋

 You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
Ihnen. Unser Standard-Vertrag überträgt das volle Eigentum am projektspezifischen Code, an den Modell-Gewichten, an Trainingsdaten (soweit die Daten Ihnen gehören) und an der Dokumentation bei der Abnahme an Sie. Wir behalten keine Hintertür-Lizenz und binden Sie nicht an einen Wartungsvertrag.

Was bei uns bleibt, ist unser vorhandener Werkzeugkasten — wiederverwendbare Bibliotheken, Kalibrier-Utilities, generische Daten-Parser, der OWL EYE®-Kern — den wir Ihnen lizenzgebührenfrei für das gelieferte Tool lizenzieren. Das ist dieselbe Grenze, die jedes seriöse Ingenieurbüro zieht: Wir bringen den Werkzeugkasten, Sie behalten das Ergebnis.

Wenn Sie eine andere IP-Vereinbarung wollen — etwa ein gemeinsames Patent auf einen neuartigen Algorithmus oder ein gemeinsam entwickeltes Modell, das wir beide mit Einwilligung wiederverwenden — regeln wir das explizit im Projektvertrag. Sagen Sie es vorab im Discovery-Workshop.

Vollständige Details auf unserer Seite zur Industriellen Objekterkennung.
You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.

Can you use our existing cameras and LiDAR sensors?Können Sie unsere vorhandenen Kameras und LiDAR-Sensoren weiterverwenden?Can you use our existing cameras and LiDAR sensors?Can you use our existing cameras and LiDAR sensors?Can you use our existing cameras and LiDAR sensors?Can you use our existing cameras and LiDAR sensors? ＋

 Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
In den meisten Fällen ja. Unsere Toolchain ist auf der Datenebene sensor-agnostisch. Wenn Ihre Sensoren ein Standard-Format liefern (PCD, LAS, E57 für Punktwolken; RTSP, GigE Vision, USB3 Vision für Kameras), binden wir sie an einem Tag an. Wir haben mit Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision und IDS gearbeitet.

Wir sagen Ihnen ehrlich, wenn die vorhandene Hardware nicht das richtige Werkzeug ist. Übliche Fälle, in denen wir zum Tausch raten:

- Der vorhandene LiDAR ist zu schmal (FoV oder Reichweite), um den gewünschten Bereich abzudecken — meist durch Repositionierung gelöst, bevor neu gekauft wird.
- Die Kamera auto-belichtet auf einer Szene mit hohem Kontrast (Sonne + Schatten am Tor), das bricht die Klassifikations-Zuverlässigkeit — gelöst durch Wechsel auf eine manuell belichtbare Industrie-Kamera mit HDR-Sensor.
- Vendor-Lock-in des vorhandenen Sensors (proprietärer Stream, kein dokumentiertes SDK) macht die Anbindung teurer als ein Livox-Ersatz.

Wir verdienen nicht am Weiterverkauf von Hardware an Sie. Wenn Sie wollen, dass wir neue Sensoren beschaffen, läuft das über unseren regulären Kanal Handelsware & Produkte zum Listenpreis. Scoping-Modell siehe Industrielle Objekterkennung.
Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
 