Buy DIGI-TEXX if you need people to hand-key and QA data your schema already defines. Buy Anglera if you need attributes discovered, cited to source, tuned to how buyers search, and written back to your PIM in ~30 days.
Both claim to enrich product data. This page is about where that claim stops.
Product data is a practice, not a project — DIGI-TEXX is built to staff a settled specification at volume, so the open question is what reads the catalog in the months between batches, when the spec you scoped stops matching the products.
Mine every spec from every source.
Every value traced to a document you can open. The catalog is only as honest as what it was built from.
Aim the catalog at the buyer who actually buys.
Grounded data still loses if it answers questions nobody asked. Alignment is what turns specs into conversion.
Product data is a practice, not a project.
Markets move, suppliers reissue, buyers change what they ask for. A catalog that is right in March is wrong by August unless something is watching.
Scored against public documentation. Grouped by the three acts — so you can see which ones DIGI-TEXX leaves on your desk.
OCR mines supplier PDFs, scanned catalogs, legacy product records
PDFs, spec tables, drawings, manuals, images, sites
Populates client-defined fields; no new attribute discovery
Proposes fields your schema never had
Attribute mapping, validation rules, cleanup; no versioned value sets
Normalizes and governs allowed values, versioned
Teams categorize SKUs and map marketplace categories
Auto-classifies; channel and marketplace mapping
Multi-level QA and audits; no value-level source citations
Every value cites its source doc and page
No persona-specific attribute or language variants documented
B2B specifier and B2C shopper enriched differently
SEO-friendly copy; no live review or search-signal loop
Reviews, search, competitor rails, social — fed back
Specialists write SEO-friendly titles and product descriptions
Original copy per persona and channel
Retouching and AI-artifact cleanup; does not generate images
Generates studio-grade imagery for photoless SKUs
Ongoing staffed engagements; updates driven by client requests
Re-enriches on its own after go-live
QA accuracy checks and periodic audits; no catalog health scoring
Scored against your standards; nothing publishes below bar
Teams upload and sync into Shopify, Magento, Amazon
Writes back to PIM, ERP, warehouse, commerce
Bespoke API integration on request; no public docs, no MCP
API, webhooks, and MCP servers
1,300+ staff deliver the work; people, not software
Anglera owns the work; review is a guardrail
“Buyer signals” is the emptiest phrase in this category, so here is the literal thing. Each of these is an observation from a live market, the gap it exposes, and the field that gets created as a result.
"Gorgeous floor. Nobody told me it couldn't go over the basement slab — boards cupped by month two. The box says 'engineered' like that settles the question."
Grade and subfloor suitability lives in the manufacturer's installation PDF, not in a field. The scoped spec covered species, plank width, wear layer and finish — everything except where the plank is allowed to go. Merchandising has no 'basement-safe' filter to build, and returns are absorbing the answer instead.
installation_grade_level, paired with subfloor_compatibility (multi-select)Grade: Above grade / On grade / Below grade. Subfloor: Concrete slab, Plywood, OSB, Existing resilient, Radiant-heated."nsf undercounter", "nsf 7 prep table", "is this nsf listed" — the token "nsf" bolted onto queries that already name the product, then a bounce.
The unit is NSF-listed and the certificate sits in the spec-sheet PDF, but sanitation listing was never a column, so the query returns nothing and the buyer reads that as no. This is a health-inspector gate, not a nice-to-have: the operator can't put the unit in the kitchen without it.
sanitation_listingNSF/ANSI 2 (food equipment), NSF/ANSI 4 (commercial cooking), NSF/ANSI 7 (commercial refrigeration), ETL Sanitation to NSF standard, Not listedThe 150W LED high bay's control line goes from "0-10V dimming standard" to "0-10V standard; DALI-2 driver on request," and a -D2 model suffix appears that was never in the batch file.
A changed attribute and a new sellable variant both land after the queue drained. Live listings still read 0-10V only, so DALI-2 jobs route elsewhere, and the -D2 SKU has no parent to hang from. The spec was accurate when it was scoped and is wrong now; the revision arrived with no batch running to catch it.
dimming_protocol (multi-select), with a driver_variant SKU relationship0-10V, DALI-2, DMX, TRIAC (leading-edge), ELV (trailing-edge), Non-dimmableDIGI-TEXX's e-commerce line is scoped as data entry: their page describes adding, updating and managing product information — titles, descriptions, prices, images, SKUs, categories — plus product categorization, attribute mapping, inventory synchronization and multi-level validation, with flexible scaling for seasonal demand and large catalog updates, against Shopify, Amazon, WooCommerce, Magento and eBay. It is a model built to run a settled specification at volume: you hand over the queue and the fields to fill. Catalogs move in the interval after the queue drains. A supplier issues a Rev C datasheet and the driver option changes. A marketplace makes a field required that wasn't in scope. Shoppers filter on something nobody defined a column for. Those changes arrive between engagements rather than inside one, and surface the next time someone opens the sheet. Anglera works the interval — catching the change, proposing the field, filling it against the same governed vocabulary the last batch used.
Values are normalized into a governed, versioned set of allowed values — so a filter works, and keeps working after the next import.
3/4 in0.75"3/4"19mm3/4 inchDN200.75 in (DN20)Six suppliers, six spellings, one physical size. Filters only work once they agree.
BlkblackBLACK MATTEMatte BlkRAL 9005Black — MatteFree text makes a colour filter useless. A governed value makes it a facet.
SS316316 StainlessStainless Steel 316A4 StainlessStainless Steel — 316 / A4Same alloy, four vocabularies, plus a trade name. Buyers search all of them.
Filling the fields you defined has an invisible ceiling: a catalog can hit 100% complete and still miss the attribute that loses the sale, because completeness is measured against a schema someone drew years ago. Schema Foundry reads competitor listings, buyer searches, review complaints and your supplier docs, and proposes the fields you never defined — which is where DIGI-TEXX stops.
How Schema Foundry works
DIGI-TEXX is a Vietnam-based (German-founded) BPO company with 1,300+ staff that provides offshore manual services for product data entry, catalog enrichment, data cleansing, and ecommerce content — primarily for retailers and brands needing high-volume, labor-driven data processing.
Pricing: Custom quotes only — hourly, per-record, or per-project models available; no public pricing.
DIGI-TEXX websiteRetailers with a defined schema and high-volume manual work — OCR from scanned catalogs, SKU categorization, marketplace mapping, SEO copy, image retouching — done by a staffed offshore team.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against DIGI-TEXX's public documentation on July 14, 2026. Vendors ship quickly — if something here is out of date, tell us and we'll correct it.
Bring one category and your supplier files. In 30 minutes you'll see it enriched — complete, structured, and consistent enough to launch on — plus the attributes your schema didn't have yet.
Book a demo