EnterpriseAbyss Solutions
Data annotation at scale
A dedicated annotation and quality-assurance operation for an asset inspection specialist — scaled from 10 to 120 engineers.
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Data Annotation & AI Enablement Training data, industrialised
The labelled data your models need, at production scale.
All AI & Data
Models are only as good as the data they learn from, and producing that data is an operational problem more than a technical one. We run a dedicated annotation practice that scaled from 10 engineers to 120 for a single client once the quality assessment was passed — working two shifts covering 20 hours a day.
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Image & video annotation
Bounding boxes, segmentation, keypoints and classification across large volumes of visual data.
Asset tagging & labelling
Structured labelling of physical assets and their condition, the work behind inspection and maintenance models.
Quality assurance & enablement
A dedicated QA layer over the labelling itself, because inconsistent labels are worse for a model than fewer labels.
Evaluation & benchmark datasets
Curated held-out sets that let you measure a model honestly instead of grading it on its own training data.
Edge-case curation
Deliberately sourcing the rare and difficult examples, which is usually where a model's real-world failures come from.
Domain-specialist teams
Engineers with sector expertise rather than general crowd labour, for work where the judgement matters.
How the operation runs
Small teams of up to ten under a dedicated manager
A structured quality assurance framework with regular evaluation
Two shifts spanning 20 hours daily, five days a week
ISO 27001 and 27701 compliant data handling
Scales on proven quality, not on promises
Delivered 90% inspection cost reduction for end clients
Related work
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