Data entry ops · Production

super.AI IDP cuts Bureau Veritas nameplate data processing time by 75% and data entry costs by 80%

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
Nameplate photo captured
Trigger
Inspectors photograph equipment nameplates to capture essential data such as model numbers, serial numbers, and manufacturing dates.
▾ source quote
“Representatives photograph equipment nameplates to capture essential data – model numbers, serial numbers, and manufacturing dates”
2
IDP extracts nameplate data
Ai action
super.AI's Intelligent Document Processing (IDP) solution automatically extracts all nameplate data from photographic images.
▾ source quote
“Bureau Veritas implemented our Intelligent Document Processing (IDP) solution to accurately extract all nameplate data”
3
Data input to asset management system
Integration
Extracted data is input directly within the asset management system with a turnaround of a couple of hours.
▾ source quote
“We capture the photo and send to super.AI and it's pulled and viewed and captured and sent back to our system with a turnaround of a couple of hours, not more than a day.”
4
Continuous model improvement
Feedback loop
The solution continues to improve on its own as it internalizes each new batch of data, increasing cost savings over time.
▾ source quote
“continues to improve on its own as it internalizes each new batch of data – and in turn, increasing the cost savings delivered by the automation over time”
Reported 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.

Reported metrics
Processing time reduction75%
Data entry cost savingsmore than 80%
Cost savings80%
Reported stack
super.AIIntelligent Document Processing (IDP)
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Source
https://super.ai/case-studies/bureau-veritas
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Frequently asked questions

What did this team achieve with this AI workflow?

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 increas…

What tools did this team use?

super.AI, Intelligent Document Processing (IDP).

What results were reported?

Processing time reduction: 75%; Data entry cost savings: more than 80%; Cost savings: 80% (source-reported, not independently verified).

What failed first in this deployment?

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…

How is this data entry ops AI workflow structured?

Nameplate photo captured → IDP extracts nameplate data → Data input to asset management system → Continuous model improvement.

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