Problem
What tends to go wrong.
AI systems can look reliable in happy-path demos while failing on edge cases, adversarial inputs, messy user behavior, or workflows the team forgot to test.
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teds services --service model-red-teaming
Model red teaming before users find the cracks.
We test LLM and multimodal systems for brittle behavior, unsafe outputs, prompt injection paths, data leakage risks, and weak evaluation coverage.
Solution
How we help.
We produce reproducible failures, severity notes, and practical fixes so your team can improve the system before launches, demos, or enterprise reviews.
Who it is for
- Teams shipping LLM or multimodal features
- Companies preparing demos, launches, or enterprise reviews
- Builders who need external adversarial judgment
What you get
- Reproducible failure cases
- Severity notes and likely causes
- Fixes, mitigations, and evaluation additions
How it works
- Map the system boundaries and intended behavior
- Run targeted adversarial tests and document failures
- Prioritize fixes, mitigations, and evaluation additions
Start a conversation
Tell us what you need to build, break, or explain.
teds proof --related
Proof lives in the work.
Related field notes show how we think through the problems behind this service.
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