A quick look at how Graveiens AI partners with teams to deliver human data for AI models.
AI red teaming is adversarial stress testing for AI systems: trained people attack the model with prompts designed to bypass its safeguards, then record where it fails. It is not general capability evaluation, and it is not a compliance certificate. Graveiens AI supplies the adversarial data and the harm ratings that feed your evaluation and alignment pipeline.
Most red teaming is done in English, yet a model that refuses a harmful request in English will often comply once it is rephrased in Hindi, Bengali, Tamil, or Arabic. Machine-translating an English probe set does not catch this, because it misses the idioms, transliteration, and code-switching that real users and real attackers use. Only native speakers writing original probes surface it.
See how expert evaluation fits the training stackOriginal adversarial and jailbreak prompts authored in each target language, covering the jailbreak, prompt-injection, unsafe-instruction and harmful-content categories you define.
Responses graded against your safety taxonomy with severity levels and written rationale, so the labels are usable for both evaluation and alignment training.
Explicit-consent onboarding, metadata tagging and an audit trail on every program, backed by our four-stage QA workflow and ISO 9001:2017 quality management.
A single "we tested it" claim hides more than it reveals. We scope every engagement across five dimensions and deliver the result as a coverage map.
High-resource, mid-resource and low-resource languages are tested separately, because safety degrades as resource level drops.
Direct jailbreaks, prompt injection, roleplay and hypothetical framings, payload smuggling, and code-switching.
Single-turn probes and multi-turn conversations, since models grow more vulnerable over a longer exchange.
The taxonomy you define, graded consistently across every language.
Severity and rationale applied by native-speaker reviewers, so a fail in Tamil means the same as a fail in English.
Safety and red-team teams shipping into non-English markets who need coverage beyond English evaluations.
Independent evaluators and AI Safety Institutes commissioning multilingual red-team datasets.
Teams launching in India, the Middle East, Southeast Asia and other multilingual markets.
We align on target languages, attack classes, harm categories and your grading rubric before any probe is written.
Vetted native speakers author original adversarial and jailbreak prompts in each language.
Your model answers the probes, single-turn and multi-turn; native-speaker reviewers grade every response with severity and rationale.
Every label passes the four-stage review; you receive the labeled dataset plus a per-language coverage and findings report.
Best for fast, repeatable regression checks. Cheap and high volume, but English-centric and blind to cultural and code-switching attacks. Use them alongside human red teaming, not instead of it.
Best for products that truly ship in English only. Real human creativity and severity judgement, but blind to non-English failure modes.
Best for models shipping in multiple languages. Catches the safe-in-English-broken-elsewhere gap, and the labeled data is usable for alignment.
Tell us the languages your model ships in and the harms you care about, and we will scope a red-team pilot you can judge on your own model.
Book a pilot