Buy Acelerar if you need a one-time cleanup of a fixed catalog and would rather hand it to an offshore team than run software; buy Anglera if your catalog changes and enrichment has to keep up.
Both claim to enrich product data. This page is about where that claim stops.
Product data is a practice, not a project — so next to a dedicated team logging into Seller Central and doing the keystrokes, the question isn't whether this batch of listings gets finished, it's whether the rule that decided `5000K` over "Daylight" is written down anywhere by the time the next drop lands.
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 Acelerar Technologies leaves on your desk.
OCR plus manual keying from PDFs, scans, spec sheets, photos
PDFs, spec tables, drawings, manuals, images, sites
Human taxonomy design; no signal-driven discovery of new attributes
Proposes fields your schema never had
Master attribute taxonomy; units unified, size synonyms collapsed manually
Normalizes and governs allowed values, versioned
Maps to Google, Amazon browse tree, UNSPSC, GPC, eBay
Auto-classifies; channel and marketplace mapping
Operational audit trails only; no per-value source citations
Every value cites its source doc and page
No persona-tailored enrichment; SEO keywords only
B2B specifier and B2C shopper enriched differently
Competitor listings and keyword research inform manual enrichment
Reviews, search, competitor rails, social — fed back
Product description writing; keyword-rich titles, descriptions, bullets
Original copy per persona and channel
Background edits, cropping, resizing, sourcing; no image generation
Generates studio-grade imagery for photoless SKUs
Monthly retainers with scheduled audits; changes applied on request
Re-enriches on its own after go-live
Quality audit reports per engagement; no ongoing health dashboard
Scored against your standards; nothing publishes below bar
Staff key data into Shopify, Magento, Akeneo, NetSuite directly
Writes back to PIM, ERP, warehouse, commerce
No public API, webhooks, or MCP; consumes client feeds
API, webhooks, and MCP servers
Dedicated offshore teams do 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.
Walmart's item-quality report flags a run of LED shop lights as incomplete: three SKUs carry color temperature as `5000K`, two as "Daylight", four say "cool white" in the title only, and the rest are blank.
The shelf's own Color Temperature filter is how this category gets shopped, and the range is scattered across four ways of saying the same thing plus a hole.
Correlated Color Temperature (K)Integer Kelvin from a governed set — 2700 / 3000 / 3500 / 4000 / 5000 / 6500 — with marketing names (warm white, cool white, daylight) mapped in, not stored"240w usb c cable 2m" and "100w pd braided" run repeatedly with no results, on a catalog that stocks both — the wattage sits in body copy as "supports fast charging up to 240W (EPR)".
Buyers shop this category by the number. It's prose, so it isn't a facet, isn't a filter, and doesn't match the query.
USB Power Delivery Rating (W)USB-IF tiers: 60 / 100 / 140 / 180 / 240 W; cables without an e-marker recorded as `Not PD-rated` rather than left blankThe same caster line comes in three shapes: one vendor writes `3" swivel, 110 lb`, the next writes `Ø75mm, 50kg dynamic`, and a third quotes "300 lb capacity" — which turns out to be the set of four.
Load capacity looks populated on every row and can't be compared across any of them, so a "holds up to 300 lb" filter would silently mis-sort the third vendor by 4x.
Load Capacity per Caster (lb)Pounds, single caster, manufacturer's rated dynamic load; metric converted, per-set figures divided by wheel count and flagged for reviewAcelerar's e-commerce enrichment runs a four-step sequence — audit, research, enrichment, delivery — worked by a dedicated pre-trained team with an account manager as the single point of contact, and the company advertises team deployment in seven days with temporary scaling for seasonal demand. Its enrichment page describes standardizing supplier specs into consistent formats and units, completing empty fields like material, dimensions, weight and compatibility, and placing search keywords. That is a real answer to throughput. Anglera's argument is about what the engagement hands back. The standardized rows are one deliverable; the rule that produced them should be another — Correlated Color Temperature is an integer from a governed set, per-caster load is pounds dynamic — written as something inspectable and re-runnable, so the next drop meets the same test without anyone re-deciding 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 Acelerar Technologies stops.
How Schema Foundry works
Acelerar Technologies is an India-based BPO and offshore outsourcing firm that provides product data enrichment, cleaning, classification, and upload services for ecommerce businesses, combining AI-powered extraction (OCR) with human operator verification, handling projects from 100 to 100,000+ SKUs.
Acelerar Technologies websiteTeams with a bounded, one-off enrichment project — OCR and manual keying from messy PDFs and scans, taxonomy mapping to Google/Amazon/UNSPSC, staff keying results into your PIM.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Acelerar Technologies's public documentation on July 17, 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.
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