Anglera + Pattern (formerly Productsup)
Pattern/Productsup is a distribution engine — it gets your data to the right channels in the right format. But it distributes whatever content you feed it. If your product descriptions are thin, keyword-blind, or written from the supplier's perspective rather than the buyer's, Pattern faithfully syndicates that weak content to 2,500 channels at scale. Anglera fixes the content before Pattern ships it: enriching every SKU against real buyer signals — how shoppers search, compare, and decide — then writing clean, scored, conversion-ready copy back to your PIM. Pattern carries the package; Anglera makes sure what's inside the package is worth buying.
What Pattern (formerly Productsup) does
Pattern (formerly Productsup) is a Product-to-Consumer (P2C) commerce platform that ingests product data from any source, transforms and formats it using rule-based and AI-assisted tools, and syndicates it to 2,500+ channels including Amazon, Meta, Google, and retail data pools. Their core strength is automated feed management and multi-channel distribution at enterprise scale.
Pricing: Enterprise subscription, custom pricing based on number of products, feeds, channels, platform modules, and seats. No self-serve pricing published.
Pattern (formerly Productsup) vs Anglera, side by side
| Pattern (formerly Productsup) | Anglera | |
|---|---|---|
| Primary job | Distribute and format product data across 2,500+ channels and marketplaces | Enrich and score product content against buyer signals, then write it back to the PIM |
| Where it sits in the stack | Between your PIM/DAM and your sales channels (outbound syndication layer) | Alongside your PIM as the enrichment layer — upstream of syndication |
| Buyer-signal enrichment | Rule-based transformation and reformatting; no buyer-intent intelligence built in | Core capability — every attribute is evaluated against how real buyers search, compare, and decide before it is written back |
| Content quality | Syndicates content as-is from source; quality depends entirely on what enters the platform | Audits, enriches, and scores content for completeness, search readiness, and conversion before distribution |
| Manual effort | Reduces manual channel-mapping effort; content creation and enrichment remain a separate upstream problem | Eliminates manual enrichment work — AI does the research, writing, and QA automatically against each SKU |
| Time to value | Enterprise implementation timelines vary; complexity scales with number of channels and data sources | ~30-day implementation; enriched, buyer-ready SKUs begin returning to the PIM in the first sprint |