Buy Modelia if apparel on-model imagery is the whole job — it is genuinely deep there and cheap to start. It does not touch product data, so pair it with Anglera, or use Anglera if imagery is one line item in a wider catalog problem.
Both claim to enrich product data. This page is about where that claim stops.
Modelia's unit of work is a picture of the garment; the practice question is what the row behind that picture says once it's live, and who changes it the next time the fabric, the grade, or the colourway does.
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 Modelia leaves on your desk.
Imagery tool; ingests garment photos, not spec documents
PDFs, spec tables, drawings, manuals, images, sites
No product data schema; image generation only
Proposes fields your schema never had
No attribute values to normalize; images only
Normalizes and governs allowed values, versioned
No SKU classification or channel category mapping
Auto-classifies; channel and marketplace mapping
No enriched data values requiring source citation
Every value cites its source doc and page
Model age, ethnicity, size selectable; no attribute tailoring
B2B specifier and B2C shopper enriched differently
No review, search, or competitor signal ingestion found
Reviews, search, competitor rails, social — fed back
Generates images and video; no product text
Original copy per persona and channel
Core product: on-model, pose, background, video generation
Generates studio-grade imagery for photoless SKUs
Generate on demand; no autonomous regeneration after launch
Re-enriches on its own after go-live
No catalog quality or completeness scoring
Scored against your standards; nothing publishes below bar
Shopify app publishes images; no PIM or ERP
Writes back to PIM, ERP, warehouse, commerce
Public API from $35/mo plus webhooks; no MCP
API, webhooks, and MCP servers
Self-serve software; your team runs the generations
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.
"The model is clearly tall — at 5'2\" this midi is a maxi on me, it dragged. Returning." Three other reviews on the same SKU say a version of the same thing.
The listing shows the garment on a body but never states the body. Length is being inferred from the image, which means it is inferred wrong for anyone who isn't the model's height, and there is no field a shopper could have filtered on instead.
garment_length_hps_to_hem_in, graded per size — plus model_height_cm and model_size_worn carried on the asset itselfInches to one decimal, measured high-point-shoulder to hem, one value per size in the grade. Silhouette label from a closed list: mini, above-knee, knee, midi, tea, maxi."nickel free glasses frames", "titanium frames sensitive skin", "monel vs acetate" — steady weekly volume, all landing on the collection page, because no product page contains the words.
Frame material lives in the supplier's spec sheet and, visually, in the photography. It isn't modelled as a field, so it can't be faceted, filtered, or answered by the page the query lands on.
frame_material, temple_material, nickel_release_compliant (with EN 1811 test reference)Closed list: acetate, TR-90, stainless steel, monel, titanium, beta-titanium, aluminium, wood. The nickel claim is a boolean backed by an EN 1811 report ID — not a phrase in the copy.412 SKUs rejected — `material_composition: value "cotton/poly blend" not recognised; fibre percentages must total 100`. A further 90 rejected on `sleeve_length_type` empty.
Both values existed — as prose in the description ("soft cotton-poly blend, easy everyday tee") and as something plainly visible in the on-model shot. Neither is parseable. The marketplace parses fields, and rejects the SKU when the field is prose or blank.
material_composition[] (fibre + integer percent, summing to 100), sleeve_length_type, neckline_typeFibre names to ISO 1833 generic terms — cotton, polyester, elastane, viscose, lyocell, wool — percent as integer, sum enforced at 100 before push. sleeve_length_type mapped to the marketplace's own enum: sleeveless, cap, short, elbow, three-quarter, long.Modelia is a visual production tool. Its own described workflow: drag in a garment photo, describe the model in text, press generate, download the result. Flatlay-to-model, repose, accessory try-on for shoes and glasses, background changing, stills into short video — the output is a picture. That is what it is built for, and it is a real job. Generating is a discrete act: you do it when you want imagery. The attribute row behind the listing runs on a different clock. A mill substitutes a 4% elastane poplin for the 3% one mid-season. The grade moves half an inch at the hem. A colourway is added and the old one sells through. None of that shows in a photograph. Six months on, the imagery looks current and `material_composition` still reads "stretch cotton". Marketplace feeds, facets and site search read the field.
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 Modelia stops.
How Schema Foundry works
Modelia is a Madrid-based AI platform that generates fashion product imagery and video without photoshoots. Its tools turn garment flatlays, mannequin shots, and existing model photos into on-model images, then extend them with pose variation, outfit styling, background generation, consistent characters, upscaling, and short video. It ships a Shopify app and a public REST API of six image endpoints. Founded in 2024 by Iván Rodríguez and René Haas; raised a €1.03M seed in June 2026 led by Next Tier, and reports processing 300,000+ SKUs in six months for brands including Desigual, AWWG, and Fútbol Emotion.
Pricing: Fully public and self-serve. Starter free (20 credits/mo, watermarked, non-commercial). Project $12 one-time (50 credits, commercial rights). Basic $35/mo (250 credits, public API, batch). Pro $85/mo (750). Business $300/mo (3,000). Credits roll over on paid tiers; costs are per-action (5s video 25 credits). No published enterprise tier: high-volume pricing and custom SLAs are contact-only.
Modelia websiteFashion and apparel brands whose bottleneck is photoshoot cost and speed, wanting deep on-model, pose, and video generation with fine model-demographic control at a $35-$300/mo self-serve price.
We'd rather tell you here than in month three of an implementation.
Capability verdicts reviewed against Modelia'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