Multimodal Verification

AI increasingly reasons over images, scanned documents and mixed media, yet verification too often stops at text. Multimodal Verification extends the trust orchestration layer to these formats, checking that what a model reads from or generates about an image or document is consistent, grounded and within policy, rather than accepted uncritically.

The capability applies the same principles across modalities. A claim extracted from a document can be grounded against that document’s content, a description of an image can be cross-checked for consistency, and visual or document outputs can be evaluated against the same policies that govern text, so trust standards do not lapse when the format changes.

Results flow into the same trust decision as everything else. Signals from multimodal checks feed scoring, policy orchestration and human review through the Proofence Engine, so a workflow that mixes text, images and documents produces one coherent, explainable verdict instead of separate, incomparable assessments per format.

For document-heavy and visual domains, this closes a common gap where the riskiest content is also the least verified. By treating every modality as something to be checked, the layer keeps its guarantees intact across the full range of AI work an organization actually does.