Industry solutions · generative AI training data services
Generative AI
Custom instruction data, RLHF preference datasets, red-teaming, benchmarking and expert evaluation for language and multimodal AI products.
Industry reality
Data quality depends on the failure modes of generative ai.
A useful dataset must represent how the product will actually be used—not just reach a large row count. We design the workflow around domain constraints, model risk and measurable acceptance.
Discuss your use case ↗Common challenges
What makes this data difficult.
Reviewer disagreement on tone, helpfulness and correctness
Fast-changing model failure modes after each release
Need for stable regression benchmarks
High-value use cases
Programs designed around product outcomes.
01
Instruction tuning datasets
02
Pairwise response ranking
03
Rubric-based model evaluation
04
Safety and policy red-teaming
05
Domain expert benchmarking
Typical deliverables
More than a folder of files.
The exact specification is project-dependent, but delivery should make provenance, quality, format and limitations understandable.
Evaluation rubric and reviewer guide01
Preference or scored response dataset02
Disagreement and quality analysis03
Versioned benchmark set04
Dataset card and known limitations05
Recommended capabilities
Build the operating model around the use case.
01Evaluate
AI evaluation & RLHF
Human preference, rubric-based evaluation and red-team datasets for improving model behavior and reliability.
Explore service ↗02LabelData annotation
Human annotation workflows with clear taxonomies, calibrated reviewers and measurable acceptance criteria.
Explore service ↗03VerifyDataset QA
Independent audits for label quality, leakage, imbalance, duplication, provenance and documentation quality.
Explore service ↗Frequently asked questions
Questions about generative ai data.
Can you create RLHF preference datasets?+
Yes. We scope pairwise ranking, multi-response selection, rubric scoring and reviewer rationale workflows around the target behavior.
Do you work with multimodal generative AI?+
Yes. Evaluation can combine text with images, documents, audio or video depending on the model interaction.
Build for the real environment
Plan a generative ai data program.
Tell us the model objective, current failure mode and available data. We will help structure the pilot and acceptance criteria.
Start a conversation ↗