AI Training Data

Model-ready data means the dataset is cleaned, structured, labeled, documented and quality-checked so a model team can use it with little extra preparation. We remove duplicates, fix formatting, apply agreed labeling instructions and run human or expert review on difficult cases. Delivery includes training, validation and test splits plus documentation. A first usable dataset is often ready in 3 to 5 weeks depending on volume and labeling complexity.

Pricing is based on data volume, labeling complexity and how much review or expert judgment each record needs. Simple classification labeling costs less than nuanced LLM preference or instruction data requiring domain experts. We typically quote a fixed price for an initial dataset batch, then offer ongoing refresh and expansion as a separate recurring engagement. This keeps early costs predictable while you validate quality.

Yes, the workflow adapts to the model task, whether that is classic machine learning labeling or LLM instruction, preference and evaluation data. We agree the data type, source rights, review process and quality target with your model team first. Reviewers follow written instructions with example cases to keep labels consistent. Evaluation sets are always built separately from training data to avoid contamination.

Label accuracy is controlled through written instructions, reviewer calibration and measured agreement rates between reviewers. Difficult or disagreed cases go to a second reviewer or a domain expert for a final decision. We report label agreement and disagreement rates alongside every delivery so your team can judge dataset reliability. This turnaround is typically reported weekly during active labeling.

You provide the task definition, any existing raw data or documents, and access to source systems if new collection is needed. If you already have labeled examples, we use them to calibrate instructions and reviewers. Sensitive fields are identified and handled under agreed rules before labeling begins. No special tooling is required from your side since we manage the labeling workflow.

Yes, ongoing dataset maintenance is a common next step after the first delivery proves useful. We refresh source data, add new and harder examples, update labels as instructions evolve, and release versioned dataset updates. Each version comes with a change log and quality report so your model team can track what changed. This keeps training data current as your product or model needs shift.