AI annotation & evaluation
Model safety and legal-risk evaluation
Evaluate model responses against user instructions and project rules. Results can include safety labels, specific legal references, rationales, quoted evidence and links between evidence and applicable conditions, with reviewer disagreements retained.
Request a sample ↗What to confirm before requesting a quote
- Coverage
- Regions, languages, date range and available record count
- Fields and attachments
- Field definitions, example records and whether original documents or media are required
- Quality and updates
- Deduplication and missing-value rules, sample-based acceptance criteria and update frequency
- Permitted uses
- Internal research, model training, customer-facing products or redistribution
These are project scoping questions, not a statement that every data type includes all coverage or permissions.
01 / WHAT’S INCLUDED
What’s in the dataset
- User instructions, model responses and stable sample IDs
- Human safety decisions and risk categories
- Legal references, rationales and aligned source evidence
- Disagreements, revisions and agreed review records
02 / USE CASES
Where it can help
- Model safety and instruction-policy evaluation
- Domain-specific human feedback and risk datasets
- Safety-policy iteration and regression checks
03 / DELIVERY
Choose the data you need
Receive Excel, JSON or JSONL in your schema, preserving links between labels, references, evidence and individual results.
Use a pilot to calibrate edge cases, QA and disagreement handling before agreeing batches and acceptance.
See a sample format ↗Assess the fit for your project
Link each risk judgment to specific text, rules and applicable conditions.
How do safety labels relate to legal findings?
Safety labels follow the project policy; legal references separately identify conditions and textual evidence. An unsafe label is not a finding that a crime occurred.
Can a project require manual or independent reviews?
Scope can require manual work, independent judgments or an agreed assisted workflow, retaining individual results and disagreements. The project rules determine permitted AI use.
Sources and usage
Define safety policy, legal conditions, reviewer independence and permitted AI use for the project. Content risk and findings about real-world unlawful conduct are assessed separately.
If you plan to train a model, use the data in a customer-facing product or share it with third parties, mention this when requesting a quote so we can check source permissions and restrictions.
Public access does not establish permission for collection, training or redistribution. Naming a platform does not imply affiliation, agency or endorsement.
Read about data sources and rights ↗Want to review a sample first?
Tell us your project objectives and we’ll help you assess and find a relevant sample.