MEDIA & ENTERTAINMENT
Content AI data built for
audiences that know the difference.
Content recommendation and moderation AI fails when the annotation workforce cannot evaluate cultural context, editorial quality, or genre convention. We scale media annotation programs across 155+ locales with contributors who have direct media industry backgrounds.
500k+
Expert evaluators across 300+ domains
155+
Locales for content annotation
14+
Secure facilities with 24/7 global support
COMPLIANCE
GDPR Compliant
COPPA Compliant
DSA Aligned
ISO/IEC 27001:2013
SOC 2 Type II
ISO 9001:2015
THE DATA GAP
Where media AI programs break down.
Recommendation AI trained without cultural context produces results that feel wrong to local audiences. Moderation AI without editorial judgment over- and under-removes in ways that are hard to audit. Both failures trace to annotation workforces that can label content at volume but cannot evaluate it in context.
01
DATA GAP
Annotation without cultural or editorial context
Recommendation training requires annotators who understand genre conventions, cultural humor, and narrative structure in the target market. Without it, the training signal is technically labeled but contextually wrong, and the recommendation model learns a pattern that no local viewer would recognize.
Cultural Context
Genre
Recommendation
02
DATA GAP
Content moderation that lacks editorial standards
Broadcast-scale moderation requires annotators who can apply editorial judgment, not just match content against policy rules. Misclassification of age-appropriate content, genre-specific violence thresholds, or culturally sensitive material produces compliance failures in both directions.
Content Moderation
Editorial
Brand Safety
03
DATA GAP
Subtitle and localization AI trained on translated content
AI-generated subtitles and dubbed dialogue trained on translated English content cannot evaluate the naturalness of dialogue as produced in the target language. Native-speaker evaluation by annotators with media industry backgrounds is required to close this gap.
Subtitles
Localization
Naturalness
USE CASES
Use cases for media AI teams.
USE CASE
Content Classification and Multimodal Labeling
Genre classification, content type tagging, mood and tone labeling, and audience suitability evaluation across video, audio, image, and text content at streaming and broadcast volume.
Video
Image
Text
Multimodal
USE CASE
Recommendation Engine Training Data
Content preference annotation, content relationship mapping, and user intent labeling for personalized recommendation systems, including cold-start content evaluation and collaborative filtering signal validation.
Recommendation
Behavioral Data
Intent
USE CASE
Multilingual Subtitle and Dialogue Quality Evaluation
Native-speaker evaluation of AI-generated subtitles and dubbed dialogue for naturalness, timing accuracy, cultural appropriateness, and platform style guide compliance, covering 155+ locales with media-industry annotators.
Subtitles
Dialogue
155+ Locales
USE CASE
Broadcast-Scale Content Moderation
Human evaluation for content integrity workflows: age classification, violence threshold review, adult content detection, and brand safety compliance across video and user-generated content at broadcast volumes.
Content Moderation
Brand Safety
Volume
USE CASE
AI-Generated Content Evaluation
Editorial evaluation of AI-generated descriptions, summaries, and recommendations for factual accuracy, sourcing quality, structural integrity, and cultural appropriateness. Includes adversarial testing for content manipulation.
Model Evaluation
Editorial
Red Teaming
USE CASE
Content Search and Discovery Relevance
Query-result relevance judging for content search and discovery, including title, description, and metadata relevance across multiple languages and content genres.
Search Relevance
NLU
Multilingual
DATA TYPES
Media data types we annotate.
01
DATA TYPE
Video and Film Content
Feature films, series episodes, short-form video, live broadcast, and user-generated video annotated for genre, mood, content classification, scene-level events, and moderation review at broadcast volumes.
02
DATA TYPE
Audio and Music
Music tracks, podcast episodes, voice content, and broadcast audio annotated for genre, mood, lyrical content classification, and audio quality evaluation across 155+ locales.
03
DATA TYPE
Subtitle and Dialogue Text
Multilingual subtitle files, dubbed dialogue scripts, and closed caption content evaluated by native-speaker media professionals for naturalness, timing, and cultural appropriateness across global distribution markets.
04
DATA TYPE
Content Metadata and Descriptions
Titles, synopses, keyword tags, cast information, and recommendation metadata annotated and quality-evaluated for search relevance, discoverability, and multilingual content cataloging.
WHY WELO DATA
Four reasons media AI teams choose Welo Data.
DIFFERENTIATOR
Media industry professionals, not general-purpose annotators.
Content evaluation tasks requiring editorial judgment are staffed with journalists, editorial professionals, and experienced content moderators with media industry backgrounds. Annotator background is matched to the evaluation requirement for each program.
500k+
vetted contributors, media-credentialed
DIFFERENTIATOR
GDPR, COPPA, and DSA compliance for multi-jurisdiction content platforms.
Our compliance infrastructure is built for platforms operating across multiple regulatory jurisdictions simultaneously. ISO/IEC 27001 and SOC 2 Type II certifications apply to all data handling.
7
Welocalize ISO certifications plus COPPA, DSA
DIFFERENTIATOR
NIMO quality assurance at broadcast annotation volumes.
Large-scale content annotation programs require continuous identity assurance and behavioral quality monitoring across thousands of contributors. NIMO applies 130+ monitoring variables to detect guideline drift and annotation quality degradation before it affects model training.
130+
behavioral monitoring variables
DIFFERENTIATOR
27 years of Welocalize localization heritage behind every multilingual program.
Welo Data operates within Welocalize, which has 27+ years of multilingual media localization experience. That institutional knowledge of how content travels across languages and cultures is why we can scale media annotation programs rapidly across 155+ locales with credentialed media professionals.
27+
years of multilingual media expertise
COMMON QUESTIONS
What media AI buyers ask us.
Yes. We have operated content moderation and annotation programs at large scale, deploying thousands of contributors across 80+ languages for a single media client. NIMO maintains identity assurance and quality consistency at that scale, and our operational model covers multi-shift workflows for 24/7 moderation requirements.
Content evaluation and moderation programs are staffed with journalists, editorial professionals, media industry practitioners, and experienced content moderators. Annotator background is matched to the evaluation requirement for each program type.
We operate across 155+ locales for content annotation globally. Subtitle and dialogue evaluation is performed by in-country media professionals who assess naturalness and cultural appropriateness as a local viewer would.
We have ramped thousands of contributors across 80+ languages in under 2 months for media programs. Targeted single-language programs can mobilize within days from pre-screened contributor pools. Programs spanning 30+ languages typically reach full production capacity within 4 to 6 weeks from scoping.
Yes. We staff editorial and journalism professionals to evaluate AI-generated content for sourcing accuracy, factual grounding, structural integrity, and bias indicators. Programs are designed to comply with DSA requirements and align with each platform’s editorial standards.
WORK WITH US
Media AI data at the scale your platform requires.
500k+ expert evaluators. 155+ locales. 27 years of multilingual media expertise behind every program.