Buy SunTec India if you want an offshore team to absorb catalog work under your direction; buy Anglera if you want enrichment that cites every value to its source, follows buyer search behavior, and keeps running after go-live.
Both claim to enrich product data. This page is about where that claim stops.
SunTec staffs the backlog — dedicated, category-specific teams keying, cleaning and classifying under SLA, which is exactly what a labor model is good at absorbing — so what this comparison actually turns on is where the rules those teams apply end up living once the batch ships, and whether the next batch has to rediscover them.
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 SunTec India leaves on your desk.
Mines unstructured PDFs, spec sheets, supplier docs, web sources
PDFs, spec tables, drawings, manuals, images, sites
Analyst-led attribute scoping; custom attributes designed per category
Proposes fields your schema never had
Controlled vocabularies, value sets, attribute value normalization
Normalizes and governs allowed values, versioned
Taxonomy development plus UNSPSC, eClass, GS1, marketplace mapping
Auto-classifies; channel and marketplace mapping
No published source citation per enriched value
Every value cites its source doc and page
Audience noted in scoping; no persona-tailored output
B2B specifier and B2C shopper enriched differently
Scrapes competitor listings, reviews; delivered as datasets and feeds
Reviews, search, competitor rails, social — fed back
Writes product titles, descriptions, feature bullets
Original copy per persona and channel
AI on-model apparel visuals, 3D renders; mostly editing
Generates studio-grade imagery for photoless SKUs
Ongoing monitoring retainers; staffed teams, not autonomous system
Re-enriches on its own after go-live
Catalog audits, field-level completeness reports; no scoring dashboard
Scored against your standards; nothing publishes below bar
Data entry and integration into PIM, ERP, storefronts
Writes back to PIM, ERP, warehouse, commerce
REST API and webhooks for scraped feeds; no MCP
API, webhooks, and MCP servers
Managed service; their staff does 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.
Forty-one LED downlight SKUs rejected on Color Temperature: the submitted value "3K/4K/5K selectable" is not in the allowed set. Someone splits the file, hard-codes 3000K, and the items go live — as fixed-CCT downlights.
The fixture's selling point is a field-selectable CCT switch, and the catalog has one Kelvin field, so the feature can only be expressed by being misstated. Every channel that asks the question differently forces the same improvised call, and whoever handles it next quarter has no record of how it was made last time.
cct_selectable_options — multi-value, plus a companion cct_selection_methodKelvin values: 2700K | 3000K | 3500K | 4000K | 5000K | 6500K. Selection method: Fixed | Field-selectable switch | Tunable (0-10V) | Tunable (DALI)"Does the 180F final rinse come off the machine, or do I need a booster? My inspector asked and the spec sheet just says NSF." Three of the four answers below it are from other buyers, and two of them are wrong.
Sanitizing method decides whether the buyer also needs a booster heater, a 60A circuit, and a different install quote. It sits on page 2 of the manufacturer PDF and nowhere in the catalog, so it can't be filtered, can't be compared, and can't be checked before the truck arrives.
sanitizing_method — with dependent fields booster_heater_included and incoming_water_temp_required_fHigh-temp, integral booster | High-temp, external booster required | Low-temp chemical sanitizing | Dual-mode"12/2 mc aluminum armor" runs several dozen times a week. It returns steel-armor MC and two pages of flexible conduit. The refinement to "mc cable aluminum" returns the same thing, then the session ends on the search page.
Armor material is the whole reason for the query — it drives weight, cutting, and what the contractor is permitted to pull — and it exists only as a line in the spec PDF. Typed into titles on whichever SKUs someone remembered, it never becomes a facet, which is why the results look arbitrary.
armor_material — governed, faceted, required for the MC/AC cable categoryInterlocked galvanized steel | Interlocked aluminum | Continuously welded and corrugated aluminum | Continuously welded and corrugated steelSunTec's catalog page states the problem plainly: "PIM platforms centralize data, but don't fix the quality." Agreed — and their answer to it is a labor one: "Dedicated Team Onboarding," "category-specific experts assigned," working under SLAs, NDAs and quality benchmarks, project-based or ongoing. That absorbs a backlog, which is real work that has to get done. The question a comparison should ask is where the judgment goes afterward. That 304 stainless is written **Stainless Steel 304** and never *SS304*, that a 3/4 in. NPT inlet is a governed value rather than a phrase in the description, that a drop-in dimension is measured to the rim and not the lip — those calls are the durable asset, not the rows they produced. Six months on, a marketplace adds a required field and forty suppliers ship new spec sheets. If the rules never landed in your schema, every call gets made again from scratch.
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 SunTec India stops.
How Schema Foundry works
SunTec India is a New Delhi-based offshore BPO and IT services firm founded in 1999, offering manual product data entry, catalog processing, and ecommerce data enrichment using a combination of human operators and third-party tools across 8,500+ clients in 50 countries.
Pricing: Custom quotes; project engagements typically range $1,000–$50,000; flexible hourly, unit-based, or lump-sum models. No public rate card.
SunTec India websiteTeams who want people, not a system: staffed offshore operators handling taxonomy mapping to UNSPSC/eClass/GS1, marketplace feeds, and one-off catalog cleanup on an hourly or per-SKU contract.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against SunTec India'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