Buy Rely Services if you need people to key supplier data into your systems and want 40-60% off in-house labor cost. Buy Anglera if you need enrichment driven by buyer signals, cited to source, and re-run continuously without headcount.
Both claim to enrich product data. This page is about where that claim stops.
Rely Services is a BPO firm whose eCommerce data entry line takes source material a client already has — a manufacturer's website, a physical or digitized catalog, supplier spreadsheets — and keys the resulting records into Shopify, Amazon, Magento or eBay, with offshore delivery their page describes as "ready for you the next business day"; the question a catalog owner still has to answer is what governs the values between one batch and the next, when the manufacturer revises the datasheet and a supplier file spells the same value four ways.
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 Rely Services leaves on your desk.
OCR and keying from PDFs, scanned catalogs, manufacturer sites
PDFs, spec tables, drawings, manuals, images, sites
Works to client-supplied templates; no new attribute proposals
Proposes fields your schema never had
Analysts reformat to client standards; no versioned pick lists
Normalizes and governs allowed values, versioned
Staff list SKUs into categories, subcategories, marketplace structures
Auto-classifies; channel and marketplace mapping
No value-level citations; QC accuracy claims only
Every value cites its source doc and page
No persona-tailored attribute or language variation described
B2B specifier and B2C shopper enriched differently
No review, search, or competitor signal loop into enrichment
Reviews, search, competitor rails, social — fed back
In-house writing team drafts titles and descriptions
Original copy per persona and channel
Image sourcing and editing; no generated product photography
Generates studio-grade imagery for photoless SKUs
Retained team updates catalogs on client instruction, not autonomously
Re-enriches on its own after go-live
Internal QC accuracy rates; no catalog health tracking
Scored against your standards; nothing publishes below bar
Staff key data directly into Amazon, Shopify, Magento
Writes back to PIM, ERP, warehouse, commerce
No public API, webhooks, or MCP server found
API, webhooks, and MCP servers
Their offshore staff perform the work end-to-end
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.
Shoppers type `nema 5-15p to c13 10ft` and `c14 right angle` into the search box and land on a 900-result power cord listing page they then abandon.
Cord SKUs carry length in the title, but the two connector ends only exist inside the description paragraph — so nothing can be filtered, faceted, or checked against the next batch.
plug_end_connector and device_end_connector — two separate fields, one per end of the cordIEC 60320 and NEMA designations only: C5, C7, C13, C14, C15, C19, C20, NEMA 1-15P, 5-15P, 5-15R, 5-20P, L6-30PThree questions ask whether a 15.6-inch Dell Precision in a rubber bumper case actually fits the sleeve marketed as "fits 17 inch laptop"; two three-star reviews say the sleeve is about 2 cm too shallow.
The listing states the screen size the bag is merchandised for, not the internal sleeve cavity — the number a buyer needs is nowhere in the record, so every re-key reproduces the same claim.
laptop_sleeve_internal_dimensions_mm — height x width x depth, three valuesMillimetres, integers, measured cavity only; nominal screen-size strings such as "17 inch" are not valid entries in this fieldThe same SKU line arrives as "Med-Dark" on the wholesale tab, "Medium/Dark" in the PDF sell sheet, "Full City+" on the bag artwork, and "Vienna" in the origin notes.
Whichever spelling the source document happened to use is what gets keyed, so roast becomes a free-text field that cannot be faceted and drifts every time the file is reissued.
roast_levelLight | Medium-Light | Medium | Medium-Dark | Dark | French, with a mapping table held in the schema: Cinnamon and City-minus to Light, City to Medium, Full City to Medium-Dark, Full City+ to Dark, Vienna to Dark, Italian to FrenchRely Services staffs the keying: source material in, listing fields out, offshore, with work their page describes as ready the next business day. That is a service you buy by the batch, and a batch inherits whatever vocabulary the source document happened to use. Anglera is not a PIM and does not replace one — it works alongside Akeneo, Salsify, or a Shopify metafield set — but the rule lives in the record rather than beside it: `roast_level` accepts six values plus a mapping table, and a cord carries two connector fields instead of one description paragraph. When the roaster reissues the price file in October and "Full City+" appears again, the mapping decides. The next batch inherits that decision instead of re-arguing it.
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 Rely Services stops.
How Schema Foundry works
Rely Services is a US-managed, India-delivered BPO firm founded in 1997 that provides manual data entry, catalog management, product data enrichment, and eCommerce content services for retailers and distributors. Human operators source, key, and format product attributes at scale, targeting 99.5% accuracy with promised cost savings of 40–60% versus in-house teams.
Pricing: Custom quote only; no public pricing. Markets 40–60% cost reduction versus in-house labor.
Rely Services websiteTeams with a defined attribute template and marketplace listings who want OCR, keying, taxonomy work, copywriting, and direct entry into Amazon, Shopify, or Magento handled by staff.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Rely Services'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