Make your LLMs safer, fairer, and enterprise-ready
Brim Labs helps businesses detect, mitigate, and manage risks in large language model (LLM) systems, from hallucinations and bias to prompt injection and compliance failures. We build guardrails that turn advanced models into trusted tools.
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Using retrieval grounding, output filtering, and evaluation pipelines.
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Securing models against prompt injection, misuse, and system jailbreaks.
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Aligning LLM behavior with internal policies and external regulations.
LLM Training Solutions
Hallucination Control Systems
- Ground outputs using vector search or live data (RAG)
- Score confidence and truthfulness in model responses
- Identify uncertainty triggers and add fallback logic
Prompt Security & Injection Prevention
- Sanitize user inputs before prompt execution
- Use scoped memory and access controls
- Design prompt flows resistant to manipulation
Bias & Fairness Audits
- Test model outputs across sensitive attributes
- Use adversarial inputs to expose inconsistencies
- Flag and reduce output skew across user groups
Toxicity & Content Safety Layers
- Apply toxicity detection and moderation APIs
- Filter explicit, offensive, or politically risky content
- Add tone control and sensitivity classifiers
Compliance & Policy Guardrails
- Define allowable output boundaries
- Map behaviors to legal, ethical, and corporate standards
- Document mitigation strategies for external audits
Building Trust into Every Token
Even the most advanced LLMs can fail without the right safety net. At Brim Labs, we embed risk mitigation at every layer of the AI stack, so your models not only perform well, but behave responsibly.Whether you're launching customer-facing tools or internal copilots, our safeguards help you deliver AI that's aligned, safe, and built for scale.
LLM Risk Mitigation
Technologies we use
