data_entry_ops · saas · workflow
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.
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 · Product URL intake
The workflow takes a product URL as the starting point to check for evidence of specific features.
Tools used
super.AI
Outcome
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 failed first
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.
Results
Time saved10x
Volume10x (from 30 to 300 products per day)
Grounding & classification
Source type: vendor customer story
19 fields verified against source quotes, 1 dropped as unverifiable.
data extractionproduct catalogfailure mode describedmetric backedproduction runtime claimedtools describedvendor confirmedworkflow describedsoftwareaccuracy improvementcycle time reductionemployee productivitythroughput increasevendor customer storydata entry opsextract classify route