super.AI accelerates stealth startup's web scraping 10x with automated data extraction
A stealth software startup building a product database relied on a manual external vendor delivering only 30-50 products per day at 20-50% accuracy, with poorly structured data and inconsistent language. The team spent countless hours on menial tasks and could not find a partner capable of delivering high accuracy at scale.
The startup tried in-house approaches and multiple other vendor partnerships before reaching out to super.AI, all of which failed to scale the process adequately.
super.AI increased daily throughput 10x from 30 to 300 products, improved accuracy to 90% (from 20-50%), and accelerated time to launch by 10x — within 2 months the customer requested 5x more workload from the platform.
Frequently asked questions
What did this team achieve with this AI workflow?
super.AI increased daily throughput 10x from 30 to 300 products, improved accuracy to 90% (from 20-50%), and accelerated time to launch by 10x — within 2 months the customer requested 5x more workload from the platform.
What tools did this team use?
super.AI.
What results were reported?
Time to launch acceleration: 10x; Daily throughput: 10x (from 30 to 300 products per day); Output accuracy: 90%; Workload increase requested by customer: 5x (source-reported, not independently verified).
What failed first in this deployment?
The startup tried in-house approaches and multiple other vendor partnerships before reaching out to super.AI, all of which failed to scale the process adequately.
How is this data entry ops AI workflow structured?
Product URL intake → Keyword-based feature detection → Structured output delivery.