Ecommerce ops · Production

Profitero achieves 99.1% tag accuracy and reduces data processing costs with super.AI

The problem

Profitero needed a scalable and fast way to tag large e-commerce datasets but was limited to 2 internal resources for data processing, creating latency bottlenecks and quality constraints.

Workflow diagram · grounded in source
1
Product data submitted for tagging
trigger
“With tens of thousands of data points, the company was looking for a scalable and fast way to tag its data sets”
2
NER categorization and brand identification
ai_action
“We helped the customer categorize products and identify the brands from product items with our NER data program”
3
Product matching
ai_action
“The customer wanted to check whether two product listings were of the same product or not”
4
Quality instructions and training
validation
“We worked with them to define step-by-step instructions to reduce the likelihood of errors. We automatically built a training to make sure that our data processing team understood the task and could perform it effectively”
5
Processed data delivered
output
“we delivered the processed data to the customer ahead of the time”
Reported outcome

super.AI delivered 99.1% tag accuracy in the proof of concept, enabling Profitero to increase data processing volume, deliver results ahead of schedule, and gradually reduce costs.

Reported metrics
Tag output accuracy99.1%
Cost reductiongradually reduce the costs
Delivery speedahead of the time
Data processing volumeincrease the amount of data they were processing
Reported stack
super.AINER
Source
https://super.ai/case-studies/ecommerce-analytics-platform-builds-neural-network-with-super-ai
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

super.AI delivered 99.1% tag accuracy in the proof of concept, enabling Profitero to increase data processing volume, deliver results ahead of schedule, and gradually reduce costs.

What tools did this team use?

super.AI, NER.

What results were reported?

Tag output accuracy: 99.1%; Cost reduction: gradually reduce the costs; Delivery speed: ahead of the time; Data processing volume: increase the amount of data they were processing (source-reported, not independently verified).

How is this ecommerce ops AI workflow structured?

Product data submitted for tagging → NER categorization and brand identification → Product matching → Quality instructions and training → Processed data delivered.

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