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Service

Red-Teaming & Evaluation

Corpshore AI runs AI red-teaming and safety evaluation, adversarial testing, jailbreak discovery, hallucination detection, content moderation, and compliance evaluation against real-world and policy risks.

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35+
languages, native testers
97%+
accuracy via three-tier QA
18+
countries for cultural context
50-70%
cost advantage vs US-domestic
What we deliver

Scoped, staffed, and QA-gated

  • Adversarial prompt and jailbreak discovery
  • Hallucination and factuality evaluation
  • Content moderation and policy testing
  • Model evaluation against real-world risk
Jailbreak testingHallucination evalPolicy red-teamBias testingSafety scoring
Red-Teaming & Evaluation at Corpshore AI
Pain points we solve

The problems this service addresses

Automated benchmarks miss the failure modes human creativity finds, so a model can pass evals and still break in front of real users.
English-only red-teaming misses jailbreaks and unsafe behavior that appear only in a specific language or cultural context.
Findings arrive as vague observations rather than reproducible reports, so engineers cannot triage or confirm a fix.
Testing is not mapped to your written policy, so results are hard to act on for compliance and product teams.
Hallucination and factuality are asserted rather than measured, so there is no prioritized view of where the model fabricates.
Coverage does not scale to the model's risk and markets, so high-risk domains and languages go under-tested.
Case scenarios

Where teams use this work

Illustrative examples of how this service fits real programs. They are representative use cases, not named clients.

A chatbot launching in multiple markets

A team needs native, in-region testers to probe safety and policy failures in each launch language, because many jailbreaks and unsafe behaviors surface only in a specific language or cultural context.

An enterprise model under a content policy

A team needs red-teaming mapped to its written policy and risk taxonomy, with each finding tied to the clause it violates, so results are actionable for compliance and produce evidence for safety documentation.

A retrieval-augmented assistant

A team needs fact-sensitive and adversarial prompts scored for accuracy and grounding against reliable references, so it gets a measured view of where and how often the assistant fabricates.

A model ahead of a public release

A team needs structured jailbreak and misuse discovery with severity, category, and reproduction steps, so engineering can triage the highest-risk findings before users do.

How we deliver

From scope to delivery, end to end

Step through the stages of a red-teaming & evaluation engagement.

1. Scope model, policy, and risk

Take your model access, written policy, priority risk areas, and target languages, and return a team size, coverage plan, and timeline before testing begins.

Stage 1 of 5
What to expect

How the engagement runs

  • It starts with your model access, written policy, priority risk areas, and target languages, from which Corpshore returns a team size, coverage plan, and timeline.
  • Native, in-region teams probe safety and policy failures in the languages your users actually use.
  • Testing maps to your policy and risk taxonomy, so each finding ties to the clause or category it violates.
  • Findings come as structured, reproducible reports with severity, category, and step-by-step reproduction, so engineers can re-run and confirm fixes.
  • You provide model access, your policy, and priority risks; Corpshore provides testers, native-language coverage, scoring, and reporting.
  • Hallucination and factuality are scored against references and categorized, so you get a prioritized view rather than a pass or fail.
Insights

What doing this well requires

  • Red-teaming complements automated evaluation by using human creativity to find failure modes benchmarks do not anticipate.
  • Safety is language and culture specific; a jailbreak that fails in English can succeed in another language, which is why native testers are essential.
  • A finding is only useful if it is reproducible, because an engineer needs to re-run it to confirm the fix closes the loop.
  • Mapping each finding to a policy clause turns red-team output into compliance evidence rather than an anecdote.
  • Measuring hallucination by domain lets a team prioritize the highest-risk areas instead of treating factuality as one undifferentiated score.
97%+
accuracy via QA cascade
35+
languages, native in-region
15,000+
seats across 18+ countries
50-70%
cost advantage vs US-domestic
Quality

Every unit passes a three-tier QA cascade

Tier 1

Annotator + peer review

Trained in-region annotators label to a versioned taxonomy. Every unit gets a structured peer check before it moves.

Catches ~80% of errors
Tier 2

Expert QA lead

Domain QA leads audit sampled and flagged work, resolve edge cases, and feed corrections back into annotator guidance.

Catches ~15% more
Tier 3

Programmatic + consensus

Automated consistency checks, gold-set benchmarking, and consensus scoring gate the batch before delivery.

Locks in 97%+ accuracy
FAQ

Red-Teaming & Evaluation, answered

Adversarial prompt and jailbreak discovery, hallucination and factuality evaluation, content moderation and policy testing, and model evaluation against real-world risk. The work targets the failure modes that matter for your deployment rather than a generic checklist.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

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