data_entry_ops · services · workflow
super.AI automates nameplate data extraction for global TIC company achieving 99.98% accuracy
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.
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 image upload via API
The company uploads large amounts of nameplate image data quickly and efficiently via API.
Tools used
Super.Extract
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.
Results
Time saved6 weeks
Volume99.98%
Cost replacedgreater than $5M per year
Source
https://super.ai/case-studies/certification-company-scales-capacity-and-accuracy-with-super-ai
Grounding & classification
Source type: vendor customer story
24 fields verified against source quotes, 1 dropped as unverifiable.
computer visiondata extractiondocument aifailure mode describedhuman review describedmetric backedproduction runtime claimedtools describedvendor confirmedworkflow describedprofessional servicesaccuracy improvementcost reductioncycle time reductionthroughput increasevendor customer storyback office opsdata entry opsdocument to record