Retail · E-commerce · Marketplaces

Clean data for commerce AI

Product attribute tagging, catalog data, image annotation, and document OCR, structured, consistent, and auditable.

The Data Problem

Commerce AI lives or dies on structured product data

Search, recommendations, and catalog systems need consistent, correctly attributed product data at scale, with QC that keeps every record trustworthy.

01

Product Attribute Tagging

Attribute extraction and normalization for catalog enrichment and marketplace compliance.

02

Image & Video Annotation

Bounding boxes, polygons, and labeling for product images, lifestyle shots, and video.

03

Document & Label OCR

Text extraction from receipts, labels, invoices, and packaging, structured and validated.

04

Visual Search Data

Attribute and category labeling that powers similarity search and discovery.

05

Review Moderation

Classification and moderation data for reviews, Q&A, and user-generated content.

06

Validation & QA

Multi-tier QC with batch-level reporting and label-level traceability.

Why Xyntriq

Consistency your catalogs can build on

Guideline-Driven

Annotation follows your taxonomy and attribute schemas, not guesswork.

Scalable Teams

Domain-matched annotators that scale from pilot to enterprise volume.

Auditable Outputs

Every batch ships with a quality report; every label is reviewable.

NDA & MSA FriendlyUdyam-Registered MSMEGST-Compliant InvoicingIndia Data ResidencyConsent-First DataAuditable QA

Prove it on your own catalog

Send a small batch of your product data, we'll return labeled samples and a fixed-scope quote.

Request a Sample Batch