Verifiable ground-truth multi-modal AI pipelines.
We map the delta between LLM outputs and immutable data registries. Our independent auditing framework tracks error-rate drift across text, audio, and video feeds.
The validation pipeline.
Standardized Ingestion
Delta Mapping
Registry Logging
We capture multi-modal outputs from major LLMs in real-time under standardized evaluation prompts to ensure baseline neutrality.
Comparing model outputs against verified databases to calculate the exact error-rate delta and trace source drift.
We log verified failures and hallucinations to our public, machine-readable registry to assist platforms in training safer models.
Validation across four vectors.
Text Auditing
Image Integrity
Audio Verification
Video Consistency
Auditing LLM outputs for hallucination drift against verified textual corpuses.
Detecting synthetic generation artifacts and deepfakes via pixel-delta analysis.
Verifying voice clones and synthetic speech against acoustic ground truths.
Analyzing frame-by-frame temporal consistency to flag manipulated media.
System FAQs
Technical specifications and operational protocols for researchers and trust & safety integrations.
How is ground truth established?
What is model drift?
Can enterprise platforms automate validation?
We aggregate cryptographically signed data feeds, official public records, and peer-verified scientific datasets to establish our baseline registries.
Model drift occurs when an LLM's output accuracy degrades over time due to model updates. We log these shifts daily.
Yes. Our pipeline is machine-readable. Trust and safety teams query our API to check live veracity scores programmatically.