The short answer: fix the data layer first, then automate the visuals

If your product data lives in spreadsheets and your channel partners keep asking for corrected attributes, start with a PIM or PXM platform such as Akeneo or Salsify, or with Lily AI if the attributes themselves are the problem. If the bottleneck is visuals, pick by input: VNTANA when you already have CAD, Modelry when you only have photos, ALLSIDES when you have physical samples, Nfinite or Claid.ai when you have images that fail retailer rules. If you need a custom viewer, configurator or AI tool wired into your own systems, the Culver City studio at #5 publishes brackets from $10,000 to $250,000+. Whatever you pick, the catalog is now what buyers read instead of a rep: 67% of B2B buyers prefer a rep-free buying experience (Gartner, 2026 survey of 646 B2B buyers).

How this list was built: the criteria, and what was deliberately ignored

Every company was checked against its own website on September 20, 2026. Two filters had to pass. First, a product or service that removes manual work from a product catalog: enriching attributes, converting CAD, producing 3D models or images at volume, or checking that what is live meets a retailer's rules. Second, at least one named retailer, brand or manufacturer on the vendor's own site that uses it for that purpose.

Where a company does not publish a founding year, a headquarters, a minimum project size or a delivery timeline, the table says "not published." Most vendors here sell through a "request pricing" form; only two of the ten publish any figure at all.

Deliberately ignored: award badges, rankings from other listicles, review counts as a sorting signal, and self-reported ROI numbers; where a vendor's own customer results are quoted, they are labeled as such. The order is by where each vendor sits in the catalog pipeline, from product data to finished visuals, not by company size. Two are US-headquartered, one is Italian with a Brooklyn office, and the rest do not publish a headquarters. The data layer comes first for a reason: 69% of B2B buyers report inconsistencies between website information and what sellers tell them (Gartner, 2025 survey of 632 B2B buyers), and a 3D viewer on top of a wrong spec sheet makes that more visible, not smaller.

Comparison table: ten vendors, from product data to finished visuals

The ten entries: what each vendor does, and when to look elsewhere

1. The PIM that becomes the single source of product truth

Akeneo (akeneo.com) is a product information management and product experience platform: it centralizes product data, enriches it and syndicates it to sales channels. Its AI layer covers agentic product intelligence, AI channel mapping, generative text and image enrichment and AI extraction of supplier data. American Eagle, Staples and Tiffany & Co. appear among the customer logos on its site.

Pick Akeneo when nobody can say which spec sheet is correct and you sell through many channels with different attribute rules; every other vendor here reads from or writes to this layer. When not to pick it: a few hundred SKUs on one storefront do not justify a PIM rollout yet, and Akeneo publishes neither pricing nor a headquarters, so budget the first call for discovery, not a quote.

2. Product content distributed to retailers, marketplaces and AI shopping agents

Salsify (salsify.com) sells Salsify PXM, which centralizes product content and pushes it to retail, digital and agentic commerce channels. The AI layer, SalsifyIQ, includes a conversational assistant named Angie, content translation and optimization, and digital asset extraction. Samsonite, ASICS, Coca-Cola, L'Oréal and Bosch are listed as customers on its homepage.

Pick Salsify when the pain is downstream: the data is decent, but every retailer wants it in a different shape. The agentic-commerce angle matters if AI shopping assistants need to read your products. When not to pick it: Salsify moves images but does not make them. Pricing, timeline and headquarters are not published, so plan for an enterprise sales cycle.

3. Attribute enrichment measured against ad and search performance

Lily AI (lily.ai), founded 2015, runs an agentic engine it calls Lily Max that enriches retail product catalogs with attributes and schema, then measures the lift across Google Ads, Meta Ads, AI shopping assistants and onsite search. Coach, Kate Spade, HOKA, UGG, Vuori and Fabletics appear as customers with case-study results on its homepage; those results are the vendor's own data.

Pick Lily AI when you already have a PIM and the attributes inside it are thin: "blue dress" instead of the twenty descriptors a shopper or an AI assistant searches by. The 30-day trial on 500 products mentioned on its site is a low-risk test. When not to pick it: it is built around fashion, footwear and lifestyle retail, with nothing on its site about CAD, 3D or industrial products. No pricing or headquarters is published.

4. CAD files converted, optimized and published to web, AR and marketplaces

VNTANA (vntana.com), founded 2012 and based in Van Nuys, California, is an enterprise 3D digital asset management platform. It ingests Siemens NX, SolidWorks, AutoCAD, Revit, Creo, CATIA, STEP, IGES and JT files, converts and optimizes them automatically, and outputs GLB, USDZ, USD, FBX, OBJ and STL. Integrations include PTC Windchill, Centric PLM, Amazon, Home Depot, Lowe's and Bynder; the company is SOC 2 Type II certified. Bobcat, Kohler, Sony, Patagonia, adidas and Timberland appear as logos on its homepage.

Pick VNTANA when you have thousands of engineering files and no web-ready models, and the job is a repeatable pipeline. When not to pick it: with no CAD, it has nothing to convert. Licensing is custom, priced by storage and live models, with a free trial for standard formats; no figures are published.

5. Custom WebGL, immersive and AI engineering when the platforms do not fit

Todor3D is a custom-engineering studio founded in 2020, with 40+ engineers across three continents, 300+ delivered projects and 25 reviews at a 5.0 rating on Clutch. Its practice areas are 3D & Immersive, Custom Software Engineering and AI Solutions; the stack is WebGL, Three.js, React Three Fiber and WebAR/WebXR, with a presence in Culver City, California. Its homepage lists a closet configurator in the $10,000–50,000 bracket, delivered in four months, and a jewelry configurator in the same bracket.

Published brackets are $10,000–50,000, $50,000–100,000 and $100,000–250,000+, with timelines of 4–10 weeks, 3–6 months and 4–12 months. Pick the studio when the catalog job is the connective tissue: a viewer that reads from your PIM, a configurator that writes back to your ERP, or an AI tool that must run inside your own systems. When not to pick it: if a licensed platform already does what you need, custom code is the slower, costlier route, and a 2020 founding date means a shorter track record than the vendors here dating from 2011 or 2012.

6. 3D models produced from product photos, priced per model

Modelry (modelry.ai), a CGTrader brand founded 2011, turns product photos into 3D models at scale through a modeling service plus platform, and adds a DAM, AR and 360 viewers and virtual photography for ecommerce. Integrations cover Meta, Google and Snapchat. Fatboy appears in a Snap Lens AR case study on its site, alongside PUMA, Ashley Furniture, Staples and Lowe's.

Modelry is one of two vendors here with published pricing: product models from $42 each and lifestyle scenes from $250 per model (Modelry pricing page, 2026), with complex products quoted individually. Pick it when you have photographs, not CAD, and enough SKUs that per-model pricing beats hiring a modeler. When not to pick it: for configurable products, a photo-derived model gives you one variant, not a parametric one; turnaround time and headquarters are not published.

7. One master 3D asset that feeds every furniture image, 360 and AR view

Cylindo (cylindo.com), part of the Chaos group, describes itself as an AI visual commerce platform for furniture brands: one master 3D asset per product feeds images, 360 spins, AR, a modular designer, AI imagery and configuration analytics, delivered API-first. Its case-study page names Joybird, Anthropologie, Room & Board, Interior Define, Swiss Sense and Landscape Forms.

Pick Cylindo when you sell furniture with fabric, finish and module options and the photo budget cannot keep up with the variant count. When not to pick it: the platform is built for furniture, with no evidence on its site of use outside that category. Pricing is behind a "receive pricing" form, and neither a founding year nor a headquarters is published.

8. Finding the images that fail retailer rules, then generating compliant ones

Nfinite (nfinite.ai) is a visual-intelligence platform that audits product imagery across retailer channels, finds the gaps, generates compliant visuals at scale with 3D rendering and AI, and monitors digital-shelf quality afterward. The company says it has processed 10M+ SKUs. Lowe's, Amazon, E.Leclerc, Stanley Black & Decker and Makita appear as logos or case references on its homepage, with a home and DIY focus.

Pick Nfinite when you sell through many retailers and the recurring cost is finding which listings are missing or non-compliant. Baymard found 25% of e-commerce sites lack product images with sufficient resolution or zoom (Baymard Institute, 2025–2026 product-page benchmark), the gap this tool hunts. When not to pick it: single-channel sellers get little from the compliance layer. No pricing, founding year or headquarters is published.

9. AI product photography and editing through an API

Claid.ai, the product of Let's Enhance, Inc., has been building generative AI since 2018 and offers AI product photography and image editing: lifestyle scene generation, on-model photos, cutouts, enhancement and upscaling, through an app and an API. It reports 10,000+ customers and 200M+ images edited. Printify appears on its site with a 42% time saving on editing, and Rappi with 33% more restaurants onboarded; both are the vendor's own data.

Pick Claid.ai when you have a high volume of adequate photos and the job is background cleanup, consistent framing and lifestyle scenes; the API makes it a pipeline component. When not to pick it: it works from images, so it cannot produce AR-ready 3D or handle configurable products. Plan and API pricing pages exist, but the amounts were not captured in this check; no headquarters is published.

10. Automated 3D scanning of physical samples into relightable digital twins

ALLSIDES (allsides.tech), formerly Covision Media, is based in Bressanone-Brixen, Italy, with an office in Brooklyn, New York. It builds automated 3D scanners that produce photoreal, relightable digital twins of physical products for ecommerce, AI training datasets and generative 3D, with PBR output at 8K, 4K or 2K. Zalando, adidas and NVIDIA provide quotes or testimonials on its site, alongside Nike and Google logos.

Pick ALLSIDES when you have physical samples but no CAD, and the product's surface, fabric or texture is what sells it. When not to pick it: scanning captures what exists, so it cannot produce variants that were never manufactured, and a scanner is a capital decision, not a subscription. Pricing, founding year and team size are not published.

How to choose for your own project: six questions that separate a pipeline from a demo

Every vendor above can show a good demo. These questions are about what happens on SKU number 4,000, and they should go to every shortlisted vendor in writing.

1. Where does the product data live after go-live, and who owns the schema? If the answer is "in our platform," ask for the export path and format.
2. What input do you need from us per SKU, and what happens when it is missing? CAD, photos, samples and spreadsheets each imply a different vendor; the failure mode is the ten percent of products that arrive incomplete.
3. What is the per-unit cost at our volume, and does it change after the first thousand? Only Modelry and the studio at #5 publish figures; every other vendor should be able to write one down for your SKU count.
4. How large is one delivered asset, and on what device did you test it? The median web page already carries about 1 MB of images (HTTP Archive Web Almanac 2024, 1,054 KB desktop), so a 3D asset needs compression, not just conversion.
5. Which retailer or marketplace specs do you validate against, and how often are they updated? A vendor that checks compliance once is a project, not a pipeline.
6. What did the last three customers of our size launch, and how long did it take? Logos are not timelines; ask for a reference call.

A budget check helps too. Clutch's 2026 pricing guide puts most US software firms at $50–$99 per hour and typical project costs at $10,000–$49,000 (Clutch, data updated September 2026); GoodFirms' survey of 100+ software companies puts a custom AI-powered MVP at $50,000–$125,000 (GoodFirms, September–October 2025). A custom quote well outside those bands deserves a question.

FAQ: what buyers ask before automating a product catalog

What is product catalog automation?

Product catalog automation is software or a service that reduces the manual work of creating, enriching, checking and distributing product data and visuals. It spans four layers: a PIM or PXM that holds the data, AI tools that fill in attributes, 3D or image pipelines that produce visuals from CAD, photos or scans, and compliance tools that check what is live on each channel. Most companies buy the layers from different vendors and connect them.

How much does it cost to create 3D models for a product catalog?

Published outsourced pricing starts at $42 per product model and $250 per lifestyle scene (Modelry pricing page, 2026), with complex items quoted individually. Generative tools are nominally far cheaper, at roughly $0.09–$0.16 per generation (Tripo pricing page, 2026), but that output usually needs retopology and UV cleanup before it can drive a configurator, so the two numbers are not comparable. Custom pipelines that connect models to your systems are quoted per project, in brackets such as the $10,000–$250,000+ published by the studio at #5.

Can AI generate product photos instead of a photoshoot?

Yes, for a defined set of tasks: background replacement, lifestyle scenes, on-model images and upscaling from existing photos, which is what Claid.ai and similar tools sell through an API. What image-based AI cannot do is show a configuration that was never photographed or produce an AR-ready 3D asset; for that you need CAD conversion, photo-to-3D modeling or scanning. Vendor results, such as Claid.ai's reported 42% editing time saving at Printify, are the vendor's own data.

Do we need a PIM before adding 3D and AI to the catalog?

Not always, but you need a single source of truth somewhere, and beyond a few hundred SKUs across several channels that usually means a PIM or PXM. Adding 3D or AI enrichment on top of conflicting data spreads the conflict faster, and buyers notice: 69% report inconsistencies between website information and what sellers tell them (Gartner, 2025 survey of 632 B2B buyers). Small single-storefront catalogs can start with a governed spreadsheet and a 3D pipeline.

Does 3D or AR content actually change what shoppers do?

The published evidence is single-brand case data, so treat it as directional. Shopify reports that Rebecca Minkoff shoppers who viewed a product as a 3D model were 44% more likely to add it to cart and 27% more likely to order (Shopify blog, 2020–2021 cohort, Shopify's own merchant data). A steadier reason to invest: 45% of B2B buyers used AI during a recent purchase (Gartner, 2026 survey of 646 buyers), and AI assistants read structured data and assets, not brochures.

How long does catalog automation take to implement?

None of the platform vendors on this list publish a timeline, so ask each one for the last three comparable launches. The only published ranges here are custom-engineering brackets of 4–10 weeks for a small scope, 3–6 months for a mid-size build and 4–12 months for a large one. Run a paid pilot on a few hundred SKUs before committing the whole catalog to any vendor.