Logistics

Quality assurance tasks—like judging whether a package was delivered safely—often require subjective judgment. Centaur’s consensus workflows reduce bias and surface ambiguous edge cases.

Assess delivery success/failure in photo or video

Classify the type and severity of product damage

Capture scene context: lighting, obstruction, visibility

AI models require structured data from unstructured documents, such as bills of lading, customs forms, or delivery slips. Centaur helps QA these outputs and supplements with human-reviewed annotations when models fail.

Parse complex, handwritten, or poorly scanned documents

Flag exceptions and inconsistencies in OCR output

Support multi-language forms across regions

When route planning systems underperform, AI teams need labeled ground truth to learn from real-world results. Centaur.ai helps annotate traffic delays, decision errors, and system blind spots.

Tag the cause of failure from route logs or dashcam footage

Classify conditions like congestion, construction, or GPS drift

Enable training of adaptive routing models