Retail Reality Check
- Inaccurate titles, missing attributes, and non-compliant imagery sink discoverability and margin. Eighty-five percent of U.S. shoppers abandon a cart if product data is unclear, while marketplaces fine brands for bad taxonomy. Retail
Master PDX infuses machine learning, computer vision, and human micro-review into a closed-loop enrichment factory that can bulk-upgrade ten million SKUs in ninety days. Cleanse & Normalize
- NLP pipelines de-duplicate listings, extract units from unstructured blurbs, and correct brand aliases using a lexical graph tuned for GS1 + Google Shopping. A validation engine flags conflicts against supplier feeds and regulatory lists like California Prop 65. Error rates drop below 0.2 %–five times better than industry average. Attribute Expansion
- For fashion, vision models infer sleeve length, pattern, and neckline. For CPG, sensors read nutrition panels at 99 % OCR accuracy, auto-filling calories and allergens. The system appends sustainability badges–Fair-Trade, Organic, Carbon Neutral–verifying claims via blockchain supply-chain attestations. SEO & Discovery Boost
- A query-intent mapper generates long-tail keywords, meta descriptions, and alt-text, raising organic search click-through 35 %. Structured Schema.org markup feeds rich snippets, while ADA-compliant alt-tags improve accessibility scores. Governance Fabric
- Every transformation logs in an immutable ledger with version diffs; rollback is one click. Category managers approve attribute suggestions in a JIRA-style queue; active-learning loops retrain models on acceptance patterns, compounding accuracy. Operational
Impact
- A big-box retailer cut new-item setup from nine days to twenty-four hours, avoided $6 M in marketplace fines, and saw basket uplift of 4.8 %. A B2G e-catalog fed by PDX met Section 508 and Buy American requirements automatically, winning a five-year federal supply schedule.
