A feedback-driven research hub where real LLM issues meet systematic investigation — turning production failures into actionable safety insights for developers, researchers, and model providers.
Real problems with large language models show up in real use. We're building a research platform where people share what goes wrong, and we work to understand why—and how to prevent it.
LLMs make mistakes. They hallucinate. They contradict themselves. They respond unsafely. Sometimes they fail when it matters most.
These failures happen in production, not in demos. Users encounter them. Developers build around them. Businesses face real consequences.
The models improve, but without systematic feedback from real-world use, we're fixing problems in the dark. We need a clear path from issue to insight to improvement.
This isn't about blame. It's about understanding.
A feedback-driven research hub.
You submit issues you've actually encountered: hallucinations, incorrect outputs, unsafe responses, inconsistencies. Real problems from real use.
Our research team studies each issue. We run controlled experiments to find root causes. We document what we learn.
We share practical prevention strategies. What works. What doesn't. How to catch problems early.
We share aggregated, anonymized insights with model providers. Non-sensitive patterns that help them improve their systems.
This is research, not marketing. Privacy-first. Focused on making AI safer for everyone.
Describe what happened. When. How. Include any relevant context. Simple.
Controlled experiments. Systematic investigation. We dig into why it happened, not just that it happened.
Practical strategies. Evidence-based recommendations. Documentation that actually helps.
Patterns and trends, anonymized. Shared with model providers to help improve systems at the source.
This is an ongoing cycle. Each issue teaches us something new.
Who notice when AI gets things wrong.
Who build with LLMs and need to understand failure modes.
That depend on reliable AI systems.
Who want to advance safety through real-world data.
If you care about making AI safer and more reliable, this is for you.
When models confidently produce false information. Why it happens. How to detect it. How to reduce it.
When the same prompt produces different answers. When context is lost. When behavior changes unpredictably.
Situations where models respond unsafely. Boundaries that get crossed. Responses that shouldn't happen.
Real-world failures with real consequences. When AI systems break in ways that matter. What we can learn from them.
These aren't theoretical categories. They're problems people face every day.
We're pre-launch.
Our research framework is being prepared. Our feedback portal is coming soon. Our team is ready to dig in.
We're not claiming results we haven't achieved. We're not promising solutions we haven't built.
We're building something thoughtful. Something useful. Something that could make a real difference.
And we'd like you to be part of it from the start.
If this resonates with you, join the waitlist.
You'll be among the first to know when we launch. You'll help shape the platform through early feedback. You'll be part of a community working toward safer AI.
We respect your inbox. No spam. Just updates when it matters.
We're not selling anything. We're not building a product to monetize. This is research for the benefit of everyone.
Your data is yours. We anonymize. We aggregate. We protect what you share.
Evidence over opinions. Experiments over assumptions. Understanding over quick fixes.
Not demos. Not benchmarks. Real problems. Real solutions. Real impact.
These principles guide everything we do. They always will.