Accuracy
No manual check required.
AI product data case study · ERP for ecommerce inventory
300,000 parts, 9 days, 98% accuracy.
A backlog no one could justify staffing for manually — solved by an automated tool that keeps running on every new part.
Prior operating experience led by Built-N-AI founder Casey Jackson. Company identity is omitted.
Results at a glance
98%AccuracyNo manual check required
300,000Parts backfilledFull database, in 9 days
$625,000Equivalent labor avoided25,000 hours at $25/hour
9 daysFull database backfillAfter ~1 week tuning for accuracy
Same-dayMarketplace syncEvery update pushed to all listings
OngoingRuns for new partsFully automatic, permanently
The challenge
A backlog nobody was ever staffed to fix.
Parts existed in the ERP with only 4–5 attributes each, and no reliable way to fill in the rest at scale. This wasn’t a slow process before — it was a problem that had never been solved, so no team was ever built to attempt it manually.
Doing it by hand would have taken about 5 minutes per part number to research and populate the missing attributes. Across the full database, that math never justified staffing the work.
The solution
A tool that researches, validates and scores every field.
We built an AI-driven Dolibarr tool that cross-references OEM sites, eBay, Amazon and other spec sources for each part, dedupes and validates the data across multiple sources, and returns every field with a confidence score on a review dashboard.
Under the hood it’s comparing embeddings — numeric fingerprints of each part’s spec sheet — to find and merge the closest match across sources, the same way a search engine matches a blurry query to the right result.
After about a week of tuning for AI drift and false positives, it reached 98% accuracy — high enough that no manual check was required. It was then run against the full database.
What it returns per part
- Cross-referenced OEM, eBay, Amazon and other spec sources
- Deduped and validated across multiple sources
- A confidence score per field — the same mechanism used in the AI Intake tool
- Auto-pushed to every marketplace listing the same day
300,000 parts backfilled in 9 days.
Equivalent to about 25,000 hours — roughly $625,000 — of manual research labor at $25/hour and 5 minutes per part. Work this company was never able to justify staffing for by hand.
What happens next
Permanent, not a one-time cleanup.
The same trigger system that ran the backfill keeps running: every new part gets the same research, validation and confidence scoring, and every update pushes to marketplace listings automatically. Part creation through marketplace listing update is now fully automatic.
Before & after
The product-data problem stopped compounding.
Before
Parts carried 4–5 attributes; nothing filled in the rest
After
Every field researched, validated and scored automatically
Before
No team ever staffed to do this manually
After
300,000 parts backfilled in 9 days
Before
Manual research would run ~$625,000 across the database
After
Fully automated, 98% accuracy, no manual check needed
Before
Marketplace listings updated separately, if at all
After
Every update auto-pushed to all listings the same day
Before
One-time cleanup at best
After
Runs automatically for every new part, permanently
Business impact
A backlog, solved and closed.
Parts backfilled
Across the full database in 9 days.
Labor avoided
Work never staffed for manual execution.
Marketplace sync
Every update, every listing.
Ongoing automation
Runs for every new part going forward.
Founding Client Program — 3 spots
Prove it on your data before you commit.
This workflow was built and proven inside real operations, not sold before it was tested. The first three external clients get founding-client pricing in exchange for a case study and testimonial.
Step 1 — Pilot
$2,000
A 10,000-part sample backfill on your own database, proving accuracy before running the full catalog. Credited toward the full build if you move forward.
Step 2 — Full build (founding-client rate)
$14,000 base
Covers up to 300,000 parts (+$1,000 per additional 50,000 parts), plus the same tool kept running automatically on every new part going forward. Founding-client pricing for the first 3 external clients only.
Key takeaway
Solving a problem nobody had staffed, not speeding one up.
This wasn’t an existing process made faster — it was a backlog that had never been addressed because the manual cost never penciled out. Automating it made the math irrelevant.
$625,000 in equivalent labor, done in 9 days — and it keeps running.