CERT provides an AI grounding verification tool that detects hallucinations in large language model outputs in real time, embedding the check directly into the generation pipeline to reduce latency and resource use. The API integrates with existing LLM workflows, offering confidence scores to flag or correct inaccurate statements before they reach end users.
Funding
Funding not disclosed
Founders
Product
Problem
Large language models can produce statements that are factually incorrect or fabricated, known as hallucinations, which undermines trust in AI-generated content and can lead to misinformation.
Solution
CERT offers an AI grounding verification tool that detects hallucinations directly within a language model’s output. The system analyzes generated text in real time to assess factual consistency without invoking a separate verification model, thereby minimizing computational cost. By integrating the verification step into the generation pipeline, developers can automatically flag or correct inaccurate statements before they reach end users. This approach enables continuous monitoring of output quality while preserving the performance characteristics of the primary model.
Target Audience
Primary users are developers and product teams building applications that rely on large language models and need to ensure factual accuracy of generated content.
Features
- Real-time hallucination detection embedded in the generation workflow
- Grounding verification performed without a secondary model, reducing latency and resource usage
- API integration that can be added to existing LLM pipelines with minimal code changes
- Confidence scoring to prioritize which outputs require human review or correction
- Compatibility with major LLM frameworks and deployment environments