Manufacturing AI training data. Rare faults, real parts, engineer-labeled.
A well-run line is the worst place to collect inspection data, because the faults you most need the model to catch almost never happen. eeko sources manufacturing AI training data to a written specification: defect imagery, CAD and 3D parts, technical documents, equipment recordings, and robot demonstrations, labeled by engineers to your tolerances and delivered with licence terms bound to every file.
What is manufacturing AI training data?
Manufacturing AI training data is the labeled material behind models that inspect parts, assist designers, read process documents, predict equipment failures, and guide robots through assembly. In practice it means photographs of defects labeled for type, location, and severity; CAD and 3D files annotated for features and tolerances; work instructions and manuals turned into structured records; audio and sensor captures of machines in known states; and recorded demonstrations of assembly tasks.
The defect classes that matter are scarce precisely because the process is good, so a visual inspection training data set assembled from last quarter's rejects is thin where it needs to be thick. Design knowledge sits in proprietary CAD and in the heads of engineers. And the judgment separating a cosmetic mark from a reject has to be applied identically across thousands of images, which is a review problem as much as a labeling one.
eeko builds production systems for plants and OEMs through eeko's manufacturing AI systems work, and this practice supplies the data those systems depend on. It is part of the eeko training data practice: engineers from the eeko expert network source and label to your specification, each item passes the verification pipeline, and delivery includes a receipt documenting source to training set for your internal validation.
Industrial AI data, sourced and labeled to your tolerances.
You set the tolerance. We write the spec, pilot, and scale once your engineers have scored it.
- Sourced to a written defect specification
- Boxes, masks, or image-level classes
- Scarce classes flagged during scoping
- Shape-fingerprinted against duplicates
- Feature and tolerance annotation
- Exclusive licence on commissioned parts
- Steps, parameters, and references extracted
- Your schema, piloted first
- Croissant metadata for the set
- Episodes with synchronized sensor streams
- Success, failure, and recovery labels
- Operators trained on your task
- Invented scenarios, real methods
- Second-engineer review
- Graded answers on request
- Condition and fault labels per capture
- Equipment and operating point recorded
- WebDataset or Parquet delivery
Line data as it is found, and as an inspection model needs it.
Thousands of good parts, a handful of faults, half photographed on a phone. Labels from whoever had time, with a different threshold every shift. Nobody knows which part files you may train on.
- Imbalanced The classes that matter are missing
- Inconsistent Reject threshold drifts with each labeler
- Duplicated The same part in five file formats
- Unlicensed Design IP of unknown provenance
Defect classes sourced to a specification until coverage is adequate. Engineer labels under one written criterion, borderline items escalated. Every file fingerprinted, licensed, and on a signed receipt.
- Covered Scarce classes sourced on purpose
- Consistent One criterion, second-engineer review
- Deduplicated Shape fingerprints catch near-copies
- Licensed Permission and terms bound to each file
Engineers, designers, and technicians from the eeko expert network.
Each task goes to someone who has done it on a plant floor.
Experts and the data types they produce
- Quality engineers — defect type, location, and severity labels, and the reject criteria behind them.
- Mechanical engineers and CAD designers — feature, tolerance, and assembly annotation on 3D data; design rationale traces.
- Manufacturing engineers — work instruction structuring, process troubleshooting, and root-cause write-ups.
- Maintenance technicians — condition and fault labels on equipment recordings.
- Teleoperators — assembly demonstrations on real hardware, with failure reasons recorded.
Data types: defect photographs, CAD models, point clouds, process documents, teleoperation episodes, machine audio, and inspection video. Every label records its author and reviewer, and borderline items go to a second engineer.
A 3D part dataset with the paperwork attached.
From tolerance to delivered dataset.
Fixed scope and fixed price once the specification is agreed. A pilot batch before any volume order, every time.
Manufacturing AI training data questions.
What is manufacturing AI training data?
Manufacturing AI training data is the labeled material behind models that inspect parts, assist with design, read process documents, predict equipment failures, or drive robots on an assembly line. It includes photographs of defects labeled for type, location, and severity, CAD and 3D files annotated for features and tolerances, structured work instructions and manuals, recordings of equipment in known conditions, and demonstrations of assembly tasks performed by trained operators. The value comes from engineers doing the labeling and from a provenance record on each file that shows where it came from and what you are licensed to do with it.
Can you supply images of a specific defect class?
Usually, and we tell you early when we cannot. You give us a defect specification: materials, process, defect classes, severity bands, lighting, camera, and background. We source against it from consenting creators in the eeko catalogue and from commissioned capture on real equipment, and quality engineers label type, location, and severity to your scale. Some classes are genuinely scarce, such as a particular weld porosity on a particular alloy, and we flag those during scoping rather than after. The pilot batch shows the coverage you will get before you commit to volume.
Which CAD and 3D file formats do you deliver?
GLB, GLTF, STL, OBJ, PLY, DXF, and point clouds, with feature, tolerance, and assembly annotations delivered beside them in JSON Lines or Parquet. Every 3D file is given a shape fingerprint when it enters our pipeline, so duplicate and near-duplicate parts are caught before they pad out your dataset. Croissant metadata describes the whole set for your data catalog, and the signed receipt names every file by identifier and hash. Other formats can be agreed in the specification if your toolchain needs them.
How is our design intellectual property protected?
Through the licence on each file and the agreement around the project. Every catalogue file carries its training permission and licence terms, which means the parts you receive are cleared for your use, and data we commission for you can ship under an exclusive licence that keeps it away from any other buyer. Files you share with us for labeling remain covered by your agreement's confidentiality terms and are handled in access-controlled environments. The receipt lists each item by identifier and hash, which gives you a precise record of what was exchanged in both directions.
Who labels the manufacturing data?
Mechanical, quality, and manufacturing engineers with checked credentials, plus CAD designers, maintenance technicians, and trained teleoperators from the eeko expert network, each assigned to the task they have done for real. Every label records its author and its reviewer. Borderline calls, such as whether a surface mark is cosmetic or a reject, are escalated to a second engineer and decided under the criteria written into your specification, so the line stays in the same place across the whole dataset rather than drifting with each labeler.
How much does a manufacturing dataset cost?
Price tracks the difficulty of sourcing the data, how dense the annotation is, which expertise it needs, how deep the review goes, and the licence scope. Scarce defect classes photographed to a tight specification and fully dimensioned CAD annotations cost more than common images with image-level tags. Exclusive licences add cost; volume commitments and standing orders reduce the per-item price. We quote once the specification is agreed, and every quote is fixed scope and fixed price.
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