data_entry_ops · services · workflow
super.AI automates nameplate data extraction for global TIC company with 99.98% accuracy
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.
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 photographed
Key information from asset and equipment nameplates is photographed.
Tools used
Super.Extract
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.
Results
Time saved6 weeks
Volume7%
Cost replacedgreater than $5M per year
Grounding & classification
Source type: vendor customer story
23 fields verified against source quotes, 1 dropped as unverifiable.
computer visiondata extractionfailure mode describedhuman review describedmetric backedproduction runtime claimedsource backedtools describedworkflow describedprofessional servicesaccuracy improvementcost reductioncycle time reductionthroughput increasevendor customer storydata entry opsquality assurancedocument to record