RETAIL & E-COMMERCE
Search and recommendation
data built for buyers, not annotators.
Relevance judgment requires annotators who actually shop in your markets. We provide native-speaker evaluators in 155+ locales, product domain expertise matched to category, and quality infrastructure that has delivered at 160M+ tasks annually.
500k+
Expert evaluators across 300+ domains
155+
Locales for search relevance judging
>90%
Quality on scaled programs
COMPLIANCE
GDPR Compliant
PCI DSS Aligned
ISO/IEC 27001:2013
SOC 2 Type II
ISO 9001:2015
ISO/IEC 27701:2019
THE DATA GAP
Where retail AI programs break down.
Search relevance and recommendation AI that underperforms costs revenue on every session. The failure usually traces back to the same source: relevance judgments from annotators without genuine shopping context in the target market, and recommendation training data built on English-language behavioral signals that do not generalize across cultures.
01
DATA GAP
Relevance judges without market shopping context
A query result that is relevant in one market is irrelevant in another. Native-language retail context, familiarity with local product conventions, and understanding of regional shopping behavior cannot be substituted with translated guidelines. Without it, relevance judgments are systematically wrong in non-English markets.
Search Relevance
Native-Speaker
Market Context
02
DATA GAP
Product catalog annotation at the wrong depth
Attribute extraction and category classification require annotators who understand regional product conventions and sizing standards. Generic annotators produce inconsistent taxonomies that degrade search ranking and recommendation precision.
Product Catalog
Attribute Extraction
Category Classification
03
DATA GAP
Recommendation models trained on single-language behavioral data
Recommendation engines built on English-language signals fail to personalize for non-English markets where user intent, query phrasing, and product preference patterns differ at the category level.
Recommendation
Multilingual
User Intent
USE CASES
Use cases for retail AI teams.
USE CASE
Search Relevance Judging
Graded relevance evaluation by native-speaker retail domain experts across 155+ locales. Covers text, voice, and image search, with relevance scales calibrated to each platform’s ranking objectives.
Search Relevance
Graded Evaluation
155+ Locales
USE CASE
Product Catalog Annotation and Attribute Extraction
Category classification, attribute tagging, listing quality scoring, and duplicate detection across product catalogs. Covers structured and unstructured data including titles, descriptions, specifications, and product imagery.
Product Catalog
Attribute Extraction
Quality Scoring
USE CASE
Recommendation Engine Training Data
User preference annotation, click-through intent labeling, and content relationship mapping for recommendation system training, including collaborative filtering signal validation and context-aware recommendation evaluation.
Recommendation
Behavioral Data
Intent Labeling
USE CASE
Multilingual Customer Intent and NLU
Intent recognition and entity annotation for customer-facing conversational AI, search auto-complete, and virtual assistants across 155+ locales, capturing regional synonyms, colloquialisms, and product terminology as they exist in each market.
NLU
Conversational AI
155+ Locales
USE CASE
Visual Search and Product Image Annotation
Product image classification, visual attribute extraction, fashion attribute tagging, and visual similarity labeling for visual search and AI-powered product discovery features.
Image
Visual Search
Attribute Tagging
USE CASE
Marketplace Listing Quality and Compliance Review
Evaluation of listing quality, policy compliance, and content integrity across marketplace platforms at high volumes, with consistent SLA delivery.
Quality Review
Policy Compliance
Scale
DATA TYPES
Retail data types we annotate.
01
DATA TYPE
Product Catalog Data
Structured and unstructured product listings including titles, descriptions, attributes, categories, and imagery, annotated for search relevance, recommendation training, and catalog quality at marketplace scale.
02
DATA TYPE
Search and Query Data
User search queries, auto-complete logs, and click-through sequences labeled for intent, relevance grades, and behavioral signal extraction across 155+ locales and regional market conventions.
03
DATA TYPE
Customer Interaction and Review Text
Customer reviews, question-and-answer threads, chatbot logs, and support transcripts annotated for sentiment, intent, entity extraction, and conversational AI training across global retail markets.
04
DATA TYPE
Visual Commerce Data
Product imagery, lifestyle photography, and user-generated content annotated for visual search, attribute classification, fashion AI, and multimodal recommendation system training.
WHY WELO DATA
Four reasons retail AI teams choose Welo Data.
DIFFERENTIATOR
Native-speaker evaluators who shop in your markets.
Our relevance judging workforce is matched to target markets by language and shopping behavior, not assigned by availability. Every evaluator applies relevance judgments from the perspective of an actual buyer in that market.
500k+
vetted evaluators, market-matched
DIFFERENTIATOR
PCI DSS aligned, GDPR compliant, ISO 27001 certified.
Our data handling infrastructure meets the compliance requirements of global commerce operations across EU, APAC, and North America. SOC 2 Type II certification applies to all retail programs.
7
Welocalize ISO certifications plus SOC 2 Type II
DIFFERENTIATOR
NIMO quality assurance at enterprise scale.
Identity assurance and behavioral quality monitoring across thousands of contributors is the difference between consistent relevance judgments and noise. NIMO applies 130+ monitoring variables per contributor throughout every production program.
130+
behavioral monitoring variables
DIFFERENTIATOR
Search relevance that is local in every market, at global scale.
We operate in-country relevance judging across 155+ locales. Every locale is staffed with evaluators who understand regional product terminology, pricing signals, and shopper intent as they exist in that language, built from the ground up rather than translated.
155+
locales, native-speaker evaluators
COMMON QUESTIONS
What retail AI buyers ask us.
155+ locales with native-speaker evaluators who have genuine retail domain knowledge in each market. We do not translate English annotation guidelines. Each locale is built with in-country evaluators who understand local product conventions, pricing signals, and shopping behavior.
Yes. We have managed programs at 160M+ tasks annually, scaling to 11,000+ remote evaluators across 65+ locales for one program, while meeting quality and capacity SLAs every month without exception.
Pre-screened native-speaker evaluators for standard language markets can be mobilized within days. Programs spanning 10+ new language markets typically reach live production within 4 to 6 weeks from scoping.
Our NIMO platform monitors 130+ behavioral quality variables per contributor throughout production. Automated gating catches low-effort annotation and guideline drift before tasks enter training pipelines. Calibration sessions and independent quality review maintain inter-annotator agreement across distributed teams.
Yes. We annotate product imagery, lifestyle content, and user-generated visual content for visual search, attribute classification, fashion AI, and multimodal recommendation systems.
WORK WITH US
Search and recommendation data built for your markets.
Native-speaker evaluators in 155+ locales. Quality infrastructure built for global commerce at scale.