super.AI IDP cuts Bureau Veritas nameplate data processing time by 75% and data entry costs by 80%
Bureau Veritas inspectors photographed equipment nameplates and had to painstakingly enter model numbers, serial numbers, and manufacturing dates into asset management systems. A prior OCR solution improved accuracy but still required inspectors to manually select fields and nudge the tool, yielding no meaningful efficiency gain. The process took hours and caused momentum loss between onsite visits.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Nameplate photo captured
Inspectors photograph equipment nameplates to capture essential data such as model numbers, serial numbers, and manufacturing dates.
Tools used
super.AIIntelligent Document Processing (IDP)
Outcome
After implementing super.AI's IDP solution, Bureau Veritas achieved a 75% reduction in nameplate data processing time, more than 80% cost savings on data entry, 3X faster data processing, 150X processing scale increase, and $9M saved annually from reduced churn.
What failed first
Bureau Veritas implemented an OCR solution to capture data from serial plate photographs, but it fell short on efficiency — inspectors still had to manually select specific data fields and nudge the tool, keeping the process tedious despite the accuracy benefit.