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Every project begins with a discovery session with your team to understand your data objective, the type of data required, the intended use of the dataset, the expected scale, and your quality requirements. This ensures the dataset we produce directly supports your technical and business goals.

Many clients know the problem they want to solve but haven't defined the dataset structure. We work with you to determine the data collection strategy, annotation schema, taxonomy or ontology, sampling methodology, and quality control framework — ensuring the dataset is usable, consistent, and scalable.

Most projects begin with a pilot dataset, which allows both teams to validate annotation guidelines, dataset structure, quality benchmarks, and workflow efficiency. Once approved, the project scales to full production. This approach significantly reduces risk on both sides.

Our distributed workforce and project management system allow us to scale from small pilot datasets to millions of records. We adjust team size and workflow structure based on dataset size, complexity, and delivery timeline — and we can scale further as your model evolves.

Once requirements are clear, we define deliverables together — including dataset size, annotation specifications, data format, quality metrics, and delivery milestones. All deliverables are agreed upon and documented before production starts. Timelines depend on dataset size, data availability, and annotation complexity.

Datasets can be delivered in any format required by your workflow, including JSON, CSV, XML, SQL database dumps, and custom structured formats. We adapt to the format required by your data infrastructure or AI pipeline.

We manage the entire workforce lifecycle — recruiting specialized annotators, onboarding and training, project supervision, performance monitoring, and quality control. Clients do not need to hire or manage annotators themselves.

Quality is ensured through a multi-layer process: expert-designed annotation guidelines, annotator training and calibration, automated validation checks, multi-review workflows, statistical sampling, and disagreement resolution. Quality metrics and validation reports can be provided alongside each dataset.

We can follow your existing guidelines, refine them for consistency, or help develop entirely new annotation frameworks — whichever ensures smooth integration with your model training pipeline.

Pricing is customized for each project based on the type of data, annotation complexity, required expertise, dataset size, and delivery timeline. A clear project quote is provided after defining the scope and deliverables together.

Getting started is simple: contact us with your project requirements, we define the data collection and annotation strategy together, a pilot dataset is delivered for validation, and then the project moves to full production with regular progress updates throughout.