Adding $500K in MRR Through Opportunity Scraping from Brand Catalogs
A $50M/year brand-direct distributor had thousands of SKUs and pricelists but no way to evaluate 400,000 products. We built an engine that surfaced 500 new high-volume SKUs.
// The Starting Point
The Access Was Never the Problem — The Volume Was
This client is a $50M/year distributor who buys direct from major brands in personal care, household, and OTC. He already sold thousands of SKUs on Amazon. From the outside, there was nothing to fix.
But the brands he carried each list thousands of SKUs, and their full pricelists run far deeper than what's actually listed. Across all his vendors, that's roughly 300,000–400,000 products to evaluate — and it's not static.
Done by hand, the initial pass alone would take an estimated 8,000 man-hours. That's not a staffing problem you solve by hiring — it's economically impossible at that scale. So the real opportunities stayed invisible.
// The Diagnosis
Data Overload at 400,000 Products
We mapped out the scale of the evaluation challenge.
Too large to evaluate by hand
400,000 products, refreshed continuously. At ~8,000 hours to start, manual evaluation was never going to happen.
Overlap & duplication noise
The same brands appeared across multiple vendors, and the same items at different prices. Useful analysis had to resolve the best price per item.
Already-selling overlap
He already sold most of these brands. The system had to detect gaps against his current catalog to avoid wasted effort.
Constant refresh
New vendors and sheets arrived all the time. A one-time analysis would be stale within weeks; this needed to be a living pipeline.
// The Strategy
Combining Software, AI, and Human Validation
We built a process to ingest the firehose and surface only what matters.
Consolidate & normalize the data
We ingested every vendor pricelist and standardized the identifiers — UPC, ASIN, brand, pack size — so 400,000 messy rows became a single comparable dataset.
Resolve best price per item
For every item available from more than one vendor, the engine resolves the lowest landed cost.
Detect gaps against his catalog
We cross-referenced everything against what he already sells and stripped those out. What remained were genuine net-new opportunities.
Score opportunities
The engine scores each net-new item on demand, volume, competition, margin, and Buy Box viability. AI handles the scale; our team validates the top set.
Run it as a continuous pipeline
Every time new vendor sheets arrive, the engine re-runs. Fresh opportunities surface monthly — without the 500-hour manual cost each cycle.
// The Results
$500K in New Monthly Revenue Surfaced
High-volume products surfaced from the gap analysis and listed.
Added on top of the existing business, from access he already had.
Healthy margin on the new SKUs, screened in before listing.
What would have taken thousands of man-hours now runs as an automated pipeline.
Engagement Details
| Timeline | Initial build + ongoing monthly pipeline |
| Market | Amazon USA |
| Category | Personal care, household, and OTC |
| Brands | Brand-direct on national CPG names |
| Client Size | $50M/year distributor |
| Engagement | Reverse sourcing / catalog gap analysis (human + software + AI) |
Distribution Breakdown
Why These Metrics Matter To Growth
For a $50M distributor, finding vendors was never the constraint. The constraint was processing the sheer volume of products those vendors offer and knowing which handful — out of 400,000 — were worth shelf space.
The $400K–$500K in new monthly revenue didn't require new supplier relationships, new capital, or new categories. It came entirely from products he already had access to and had never had the means to surface.
Output Sample — Opportunity Scoring (Anonymized)
Detailed Breakdown
| ITEM | BRAND | BEST COST | LISTED? | EST. VOLUME | MARGIN | DECISION |
|---|---|---|---|---|---|---|
| [redacted] | Brand A | $X.XX | No | High | 13% | List |
| [redacted] | Brand A | $X.XX | Yes | — | — | Skip |
| [redacted] | Brand B | $X.XX | No | Medium | 9% | Hold |
| [redacted] | Brand C | $X.XX | No | High | 12% | List |
Sitting on vendor lists you can't process?
If your pricelists run into the hundreds of thousands of items, the winners you're missing are buried in there right now. We combine software, AI, and human review to surface them.
Request a Catalog Gap AnalysisHe didn't need more vendors. He needed to see what the vendors he already had were offering.