Buy HabileData if you need trained operators to clean, classify and upload a catalog at ~$5/hour. Buy Anglera if enrichment has to reflect how buyers actually search, cite every value to a source, and run continuously.
Both claim to enrich product data. This page is about where that claim stops.
With an offshore catalog team the question was never whether the batch gets keyed — it's what happens between batches, when the spec you handed over in March stops matching what buyers are asking in August.
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 HabileData leaves on your desk.
Staff key from PDFs, scanned catalogs, spec sheets, scraped sites
PDFs, spec tables, drawings, manuals, images, sites
Fills client-defined fields; does not propose new attributes
Proposes fields your schema never had
Normalizes attributes to client spec; no versioned pick lists
Normalizes and governs allowed values, versioned
Product classification plus Amazon/Shopify category mapping, staffed
Auto-classifies; channel and marketplace mapping
Multi-stage QA review; no value-level source citations
Every value cites its source doc and page
No B2B versus B2C persona tailoring in materials
B2B specifier and B2C shopper enriched differently
Scrapes competitor prices and listings; delivered as separate datasets
Reviews, search, competitor rails, social — fed back
Writes keyword-rich product titles and descriptions for listings
Original copy per persona and channel
Background removal, retouching, ghost mannequin; no image generation
Generates studio-grade imagery for photoless SKUs
Retained teams run scheduled catalog updates, not autonomous re-enrichment
Re-enriches on its own after go-live
Accuracy claims and catalog audits; no ongoing health dashboard
Scored against your standards; nothing publishes below bar
Teams upload into Shopify, Amazon, Magento, client systems
Writes back to PIM, ERP, warehouse, commerce
API offered as scraping delivery option; no webhooks, no MCP
API, webhooks, and MCP servers
Their offshore staff do the work end-to-end; 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.
Buyer questions keep landing on the same thing — whether it works with a Lutron Caséta dimmer — and reviews report the strip strobing at low output on the dimmer already in the wall.
Dimming behaviour is decided by the driver's phase-control type, but the listing carries only wattage, colour temperature and length. The answer is sitting in the spec sheet — there's nowhere to put it.
dimming_protocolTRIAC (forward phase) | ELV (reverse phase) | 0-10V | DALI | PWM | Non-dimmableShoppers pair the query with "nsf" and "hdpe", get the unfiltered category back, retype the pair with the terms reversed, and leave. The certification is named in description copy on some SKUs and nowhere on others.
Certification lives as prose on the PDP, not a governed value, so it can't be faceted, filtered, or checked for consistency across the line. Buyers are typing the field name they wish existed.
sanitation_certificationNSF/ANSI 2 (food equipment) | NSF/ANSI 51 (food-zone materials) | NSF/ANSI 4 | Not certifiedRows bounce with "invalid value" on assembled height. The supplier's sheet reads 28-1/4", the template wants decimal inches, and the same line went through last quarter because someone converted it by hand in the staging file.
The fix lived in one person's head for one batch, not in the schema. Fractional inches, unit suffixes and range values all arrive from suppliers as free text, and each upload re-litigates the conversion instead of inheriting a rule.
assembled_height_inDecimal inches, numeric only, two decimal places; fractional-inch and metric source strings normalised on ingest (28-1/4" → 28.25; 718 mm → 28.27)HabileData is an outsourcing firm. Its catalog management page describes per-SKU or hourly engagements with dedicated catalog managers — keying product data, mapping categories, tagging attributes into the template you supply — delivered through online tools into your ecommerce system or back as offline documents. That is capacity to execute a spec, faithfully, at volume. The spec is the perishable part. A batch of hospitality SKUs gets keyed against the fields named at kickoff. Six months on, buyers are asking about NSF category, dimming protocol, whether the caster locks. No column exists for any of it. Nothing keyed is wrong — the attribute that would answer the question was never requested, so it was never keyed. Catalogs go stale at the schema, not the cell. Deciding which attributes a category now needs, and filling them, is the work Anglera does alongside whoever keys the batch.
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 HabileData stops.
How Schema Foundry works
HabileData is an India-based BPO and data services company that provides manual offshore product data entry, catalog management, data enrichment, classification, and cleansing services for eCommerce retailers, manufacturers, and distributors. Teams of human operators process and standardize SKU data at per-hour or per-SKU rates.
Pricing: From ~$5/hour for data entry; also per-SKU and per-project rates for catalog uploads and multi-channel listing. No public list pricing — custom quotes based on volume and complexity. Free trial offered.
HabileData websiteTeams that want people to own the whole job: keying specs out of scanned catalogs and spec sheets, mapping to Amazon/Shopify categories, and uploading listings, at a per-hour or per-SKU rate.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against HabileData'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