Data entry ops · Production

super.AI automates nameplate data extraction for global TIC company with 99.98% accuracy

The problem

A global TIC services company manually transcribed key information from asset and equipment nameplate photographs, resulting in a 7% error rate for serial number transcriptions alone and high labor costs that could not scale to meet customer workload.

Workflow diagram · grounded in source
1
Nameplate photographed
Trigger
Key information from asset and equipment nameplates is photographed.
▾ source quote
“key information from asset and equipment nameplates was photographed”
2
Data upload via API
Integration
The company uploads large amounts of data quickly and efficiently via API.
▾ source quote
“upload large amounts of data quickly and efficiently via API”
3
AI extracts nameplate data
Ai action
Super.Extract automatically pulls relevant details such as manufacturer name, model number, and serial number from nameplates.
▾ source quote
“By leveraging Super.Extract, super.AI's no-code solution for automated data extraction, the company can now pull relevant details such as manufacturer name, model number, and serial number from nameplates automatically”
4
Human workers validate output
Human review
AI and human workers are combined to achieve 99.98% data accuracy.
▾ source quote
“Combined AI and human workers to achieve 99.98% data accuracy”
5
Results fetched automatically
Output
Results are fetched automatically via the API integration.
▾ source quote
“Integrated via API to upload data programmatically and fetch results automatically”
Reported outcome

The company achieved 99.98% data accuracy processing more than 100,000 data points, with an estimated economic impact greater than $5M per year from labor cost savings and enhanced data processing capacity, plus 2X faster customer onboarding.

Reported metrics
Serial number transcription error rate (before)7%
Data accuracy achieved99.98%
Estimated economic impact per yeargreater than $5M per year
Data points processed100,000+
Show all 6 reported metrics
serial number transcription error rate (before)7%
data accuracy achieved99.98%
estimated economic impact per yeargreater than $5M per year
data points processed100,000+
accuracy improvement6x
deployment time to production6 weeks
Reported stack
Super.Extract
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Source
https://super.ai/case-studies/automating-nameplate-data-extraction-test
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The company achieved 99.98% data accuracy processing more than 100,000 data points, with an estimated economic impact greater than $5M per year from labor cost savings and enhanced data processing capacity, plus 2X fa…

What tools did this team use?

Super.Extract.

What results were reported?

Serial number transcription error rate (before): 7%; Data accuracy achieved: 99.98%; Estimated economic impact per year: greater than $5M per year; Data points processed: 100,000+ (source-reported, not independently verified).

How is this data entry ops AI workflow structured?

Nameplate photographed → Data upload via API → AI extracts nameplate data → Human workers validate output → Results fetched automatically.

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