Portfolio
Featured Samples
Example projects demonstrating our annotation methodology and QA process.
Due to client confidentiality agreements, we can't publish our actual case studies. These are self-directed projects using public datasets.
We deliver high-accuracy data labeling and quality-assured training datasets,
the fuel for the world's most capable AI models.
Intellocube was founded on a single conviction: the quality of AI begins with the quality of its data. We work with computer vision teams, NLP researchers, and enterprise AI organizations to deliver annotation at every scale, without sacrificing accuracy.
Multi-pass QA and consensus scoring catch errors before they ever reach your training set.
From a 10K pilot to a 100M+ asset pipeline, our workforce and tooling scale without breaking accuracy.
Dedicated pods and always-on shifts mean fast turnarounds without ever cutting corners on quality.
We cover 4+ annotation modalities so your AI pipeline gets clean, structured, deployment-ready data.
Example projects demonstrating our annotation methodology and QA process.
Due to client confidentiality agreements, we can't publish our actual case studies. These are self-directed projects using public datasets.
A battle-tested 6-step pipeline from raw data to deployment-ready labels.
Requirements workshop, ontology design, and data security protocols.
Configure annotation platform, labeling templates, and QA criteria.
Annotate 2–5% sample; calibrate annotators with gold-standard review.
Full-scale labeling by specialist teams with real-time dashboards.
Multi-tier review: automated checks + expert human audit.
Export in your format (COCO, Pascal VOC, CSV, JSONL) via API or secure transfer.
Ready to label your next dataset? Whether it's a 10K pilot or a multi-million asset pipeline, we'll scope it precisely and deliver on time.