Data entry ops · Production

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

The problem

A global TIC company's core asset management process required manually transcribing data from equipment nameplate photographs, producing a 7% error rate on serial number transcriptions and an inability to handle growing customer workload in-house.

Workflow diagram · grounded in source
1
Nameplate image upload via API
Trigger
The company uploads large amounts of nameplate image data quickly and efficiently via API.
▾ source quote
“The company is able to upload large amounts of data quickly and efficiently via API”
2
AI nameplate data extraction
Ai action
Super.Extract automatically pulls manufacturer name, model number, and serial number from nameplate images with nearly perfect accuracy.
▾ source quote
“pull relevant details such as manufacturer name, model number, and serial number from nameplates automatically with nearly perfect accuracy”
3
AI and human worker validation
Validation
Combined AI and human workers achieve 99.98% data accuracy.
▾ source quote
“Combined AI and human workers to achieve 99.98% data accuracy”
4
Automated result retrieval
Integration
Data is uploaded programmatically and results are fetched automatically via API.
▾ source quote
“Integrated via API to upload data programmatically and fetch results automatically”
Reported outcome

The automated solution achieved 99.98% data accuracy (a 6x improvement), processed over 100,000 data points, generated more than $5M estimated annual economic impact from labor cost savings and enhanced capacity, and delivered 2x faster customer onboarding.

Reported metrics
Data accuracy99.98%
Accuracy improvement6x
Estimated economic impactgreater than $5M per year
Data points processed100,000+
Show all 6 reported metrics
data accuracy99.98%
accuracy improvement6x
estimated economic impactgreater than $5M per year
data points processed100,000+
serial number error rate (before automation)7%
deployment time to production6 weeks
Reported stack
Super.Extract
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Source
https://super.ai/case-studies/certification-company-scales-capacity-and-accuracy-with-super-ai
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Frequently asked questions

What did this team achieve with this AI workflow?

The automated solution achieved 99.98% data accuracy (a 6x improvement), processed over 100,000 data points, generated more than $5M estimated annual economic impact from labor cost savings and enhanced capacity, and…

What tools did this team use?

Super.Extract.

What results were reported?

Data accuracy: 99.98%; Accuracy improvement: 6x; Estimated economic impact: 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 image upload via API → AI nameplate data extraction → AI and human worker validation → Automated result retrieval.

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