AI models don't know what they don't know. They fabricate. We provide the expert human fact-checkers who verify every claim, citation, and logical step to ground your LLMs in verifiable reality.
"The Eiffel Tower is located in Berlin and was constructed in 1925."
The Eiffel Tower is located in Paris, France, and was constructed from 1887 to 1889. The model has hallucinated both the location and the date.
Semantic definitions of our hallucination detection and fact-checking methodologies.
Not all hallucinations are equal. We classify fabrications by severity to prioritize your safety pipelines.
Small factual errors, such as incorrect dates, misattributed quotes, or minor statistical deviations. Low risk, but degrades user trust over time.
The generation of highly realistic but entirely fake URLs, DOIs, or book titles. Highly problematic for academic, legal, and medical AI applications.
Logical self-contradictions or the invention of safety-critical facts (e.g., medical dosages or legal precedents). Poses immediate liability and safety risks.
How do we stop a probabilistic text generator from lying? Through rigorous, expert-driven data loops.
We don't just flag errors; we generate high-fidelity correction data that teaches your model to understand the boundaries of its own knowledge.
Human experts parse model outputs to isolate distinct factual claims requiring verification.
Experts cross-reference claims against trusted, proprietary, and real-time databases.
False claims are paired with verified "ground truth" corrections and qualitative feedback.
This dataset trains the model to recognize uncertainty and refuse to answer, eliminating hallucinations.
Deploy elite fact-checkers. Eliminate fabrications. Ship trustworthy models.