Retail & E-commerce AI Data Annotation

Retail & E-commerce

Data that helps shoppers find what they're looking for

Synnth labels the product, image, and review data behind visual search, recommendation engines, and catalog automation — so retail AI matches intent to inventory accurately, at scale.

Overview

Retail AI runs on volume and specificity: millions of SKUs, constantly shifting catalogs, and product attributes that need to be precise enough to power search, recommendations, and visual matching. Synnth combines high-throughput image annotation with domain-tuned NLP labeling to keep catalog and customer-facing AI systems accurate as inventory scales.

What we do

Data collection & annotation across every AI modality

From sourcing raw data to delivering production-ready labeled datasets — Synnth covers the full pipeline across the four core data types that power modern AI.

Data collection

Controlled product photography sourcing
Demographically diverse model and lifestyle imagery
Customer review and Q&A text collection
Multilingual product catalog and listing data
Visual search query and result-pair sourcing

Annotation

Product image classification and attribute tagging (color, material, style, category)
Bounding boxes and segmentation for visual search and try-on models
Catalog deduplication and matching
Customer review sentiment and aspect-based sentiment annotation
Product description and listing NLP tagging
Fraud and fake-review signal labeling

Why Synnth for retail & e-commerce

Every Synnth project follows the same structured pipeline, regardless of data type, language, or volume. That consistency is not bureaucratic — it is the mechanism through which we maintain quality at scale.

High-throughput without accuracy trade-offs. Catalog-scale annotation runs through the same human-in-the-loop QA pipeline as our smaller projects — a 99.2% QA pass rate holds whether you’re labeling 10,000 SKUs or 10 million.

Attribute taxonomies built for your catalog. We build custom attribute schemas around your actual product hierarchy instead of forcing your catalog into a generic template.

Sentiment that goes beyond positive/negative. Aspect-based sentiment annotation ties feedback to specific product attributes (fit, quality, shipping, price) — the granularity recommendation and quality-control models actually need.

Multilingual catalog support across 40+ languages, for teams localizing search and recommendations into new markets.

Delivery formats

2,000+ expert annotators, matched to your domain

JSON, CSV, COCO for imagery, or direct integration with your PIM/catalog schema via API.

Ready to build AI on data you can trust?

Whether you need a small pilot batch or an ongoing production annotation pipeline — tell us what you’re building and we’ll tell you exactly how we can help.

FAQs

Common questions

Everything you need to know before starting a AI Data collection or annotation project with Synnth.

💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.

Can you handle high-volume catalog annotation on tight timelines?

Yes. Pilot batches deliver in 48–72 hours, and enterprise catalog projects run on dedicated SLAs with a project manager assigned to ongoing volume.

Do you build custom product attribute taxonomies?

Yes. We work with your team to define attribute schemas matched to your specific product hierarchy rather than applying a generic template.

Can you annotate reviews for sentiment tied to specific product features?

Yes, we support aspect-based sentiment annotation that links sentiment to specific attributes like fit, quality, or shipping experience.

Get started

Start your AI Data collection or annotation project today

Tell us your use case, action taxonomy, environment, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.