Technology
Technology
How the pipeline works end-to-end.
01
Shop-Floor Data Capture
Training a vision system starts with data, and our system puts that step in the hands of the people who know the product best. Using the same mobile app that later runs inspection, a quality engineer or line operator captures short video of each assembly area directly at the workstation.
- No tripods, lab conditions, or machine vision expertise required. Capture happens where the work happens.
- Real-time guidance for object scale, viewing angle, and lighting, including small fasteners at sufficient resolution for reliable detection.
- A typical capture session takes under three minutes per station, covering all zones and components that define a correct build.
The result is a production-representative dataset which becomes the single source of truth for component detection, zone definition, and assembly validation downstream.
02
One-Shot Labeling
Traditional computer vision projects spend weeks on manual annotation, drawing thousands of bounding boxes or polygons across thousands of images. We eliminate that bottleneck entirely.
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Single reference frame
An engineer labels one frame, identifying each component class with a few clicks; the system propagates annotations across the full dataset using a self-supervised segmentation pipeline.
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Curation built in
Rules filter low-quality frames and correct boundary drift, producing clean, training-ready labels with no additional human review.
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Speed at scale
Labeling for a multi-class assembly with up to 25 distinct component types typically completes in minutes.
Critically, all data processing happens on-premise: raw imagery, annotations, and trained models never leave the enterprise network, meeting the data sovereignty requirements of defense, aerospace, and government customers without exception.
03
Assembly Definition and Synthetic Failure Modes
Once a dataset of correctly built assemblies exists, the system derives the acceptance criteria directly from the data: what a correct spatial arrangement of components looks like, how many of each part should be present, and where they belong relative to one another.
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Synthetic failure modes
From the learned baseline, the system generates data representing hundreds of plausible failures: missing fasteners, extra components, swapped parts, and misaligned arrangements.
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Engineer control
Teams select specific failure types or randomize across possible defects to reflect uncertainty the model may see in production.
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No staged teardowns for every case
The inspection model trains on both real correct builds and realistic incorrect configurations without physically disassembling hardware for every failure scenario.
04
No-Code Assembly Line Configuration
Deploying an inspection session to the shop floor requires no scripting, no configuration files, and no involvement from a software team.
- Through a visual configuration interface, a senior engineer or quality lead composes an assembly line: zones, models per zone, and consensus thresholds.
- Multiple zones, each with its own component model and acceptance criteria, compose into a single inspection flow that reflects the physical workstation.
- When builds change, new product variants arrive, or line layouts shift, configuration updates push to operators in minutes.
This makes the pipeline practical in high-mix, low-volume environments where frequent changeovers are the norm rather than the exception.
05
Real-Time Inspection and Audit Trail
On the line, the operator points the device at each assembly zone and receives an immediate pass or fail verdict, typically within one to two seconds of the camera acquiring the scene.
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Actionable failures
When a build fails, the system gives specific reasons: missing fastener, extra component, or deviation from the trained baseline. It identifies the zone and, for simple assemblies, the likely root-cause part.
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Audit record
Every inspection event is logged with timestamp, operator identity, zone, verdict, model version, and raw score. A digital record suitable for ISO 9001, AS9100, and similar quality frameworks.
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Supervisor visibility
Shift supervisors and quality managers review history by batch, router number, or time window. Traceability for auditors without paperwork in the operator workflow.