Human judgment. AI precision.
Trust & Safety that starts at the data layer. We don't just consult on safety — we operate it.
Safe platforms don't happen by accident. They are built deliberately, methodically, and at the data layer. Nextura.ai's Trust & Safety practice is rooted in our core strength: the ability to deploy expert human annotators, multilingual reviewers, and AI-assisted moderation pipelines at enterprise scale.
Human-in-the-loop moderation, built for scale.
AI can flag. Humans decide. We deliver the trained reviewer workforce and structured annotation pipelines that make moderation decisions defensible, consistent, and audit-ready.
Text & NLP moderation
Hate speech, harassment, misinformation, extremism, and adult content reviewed and labelled by trained specialists with language-specific cultural context.
Image & video review
Frame-by-frame video annotation and image classification for CSAM, graphic violence, nudity, and policy violations — with human sign-off on edge cases.
Audio & speech moderation
Transcription-assisted review of audio content — podcasts, live streams, voice messages — for harmful speech, coded language, and policy breaches.
Multilingual review
Native-language reviewers across 20+ languages including Arabic, Mandarin, Hindi, French, German, Spanish, Portuguese, and Southeast Asian languages.
Policy calibration
We help platforms operationalize community guidelines into scalable annotation rubrics, decision trees, and reviewer training materials.
Edge case escalation
Structured escalation paths for borderline and novel content categories — with documented reasoning for appeals and compliance.
Content types we moderate across
- Social media posts, comments, and direct messages
- Marketplace listings, product descriptions, and seller content
- User-generated video, short-form clips, and live stream segments
- App reviews, forum threads, and community discussion boards
- Advertising creatives, landing pages, and sponsored content
- Generative AI outputs — text, image, audio, and multimodal content
The data behind safe AI.
Every content moderation AI model depends on high-quality labelled data. We build datasets that train, fine-tune, and evaluate the safety classifiers powering your platform.
Toxicity & harm classification
Expertly labelled datasets covering hate speech, self-harm, extremism, and harassment with nuanced multi-label taxonomies matching your policy framework.
RLHF for safety alignment
Human preference data and reward model training datasets that align LLMs toward safe, helpful, and honest outputs — including red-teaming annotation.
Sensitive content benchmarks
Benchmark datasets for evaluating classifier performance on rare, high-stakes content categories with known ground truth and IAA scores.
Synthetic adversarial data
Augmented datasets including adversarial examples, jailbreak attempts, and policy-violating edge cases to stress-test safety models.
Annotation capabilities for safety AI
- Multi-label toxicity annotation with severity scoring (mild / moderate / severe)
- Intent classification — distinguishing satire, criticism, and coded hate from explicit violations
- Contextual annotation — same content labelled differently by platform context and audience
- Inter-annotator agreement (IAA) measurement and adjudication workflows
- Custom ontology development aligned to your platform's content policy
Fraud starts in data. So does the defense.
Detecting fraud at scale requires AI trained on accurately labelled behavioral signals. We build labelled datasets and human review pipelines across e-commerce, fintech, and social platforms.
Fake account detection data
Labelled datasets of bot accounts, sock puppets, and coordinated inauthentic behavior — annotated with behavioral features, network signals, and content patterns.
Review & rating fraud
Annotation of fake reviews, review brigading, incentivised ratings, and astroturfing — across e-commerce, app stores, and local business platforms.
Scam & phishing content
Labelled training data for scam detection — phishing messages, fraudulent listings, impersonation content, and social engineering patterns.
Compliant by design. Secure at the data layer.
Trust & Safety involves the most sensitive data categories — CSAM, medical disclosures, financial fraud, and PII at scale. We operate secure, compliance-aware annotation environments built for exactly this.
PII detection & redaction
Labelling of personally identifiable information across unstructured text, documents, and images — for anonymisation pipelines and regulatory compliance.
Secure annotation environments
Air-gapped or VPN-restricted workspaces for sensitive data — with role-based access, data residency controls, and full audit trails.
Compliance-aware operations
Workflows designed to meet GDPR, HIPAA, CCPA, and DSA requirements — with data handling agreements and regional residency options.
Reviewer wellbeing protocols
Structured exposure management, psychological support frameworks, and content rotation schedules for teams reviewing harmful content.
Fairness isn't a feature. It's how we label.
Biased training data produces biased safety models. Our annotation methodology is designed from the ground up to surface and mitigate data-level biases.
Bias auditing in annotation
Cross-demographic review of labelling patterns to identify systematic bias by language, dialect, cultural context, or identity group.
Diverse annotator panels
Intentional sourcing of annotator demographics matching the diversity of your user base — cultural nuance captured, not flattened.
Transparency documentation
Datasheets, model cards, and annotation guidelines as deliverables — giving your AI governance team the evidence base for responsible deployment.
Red-teaming & adversarial review
Specialist annotators who probe AI safety systems for failure modes — jailbreaks, prompt injections, and policy bypass — before adversaries do.
Every platform has a different definition of harm.
We configure moderation workflows, annotation taxonomies, and reviewer training to the specific policy environment of your industry.
Social media & UGC platforms
Real-time moderation queues, viral content prioritisation, and community standard enforcement across text, image, and video at consumer scale.
E-commerce & marketplaces
Product listing review, seller verification, counterfeit detection, and review integrity — protecting both buyers and brand reputation.
Gaming & virtual worlds
In-game chat moderation, avatar content review, virtual goods fraud, and toxic behaviour annotation for immersive and competitive platforms.
Fintech & digital payments
Transaction narrative review, identity document verification annotation, and fraud signal labelling for payment and lending platforms.
Healthcare & wellness
Safe messaging guideline enforcement, self-harm content review, and medical misinformation annotation — with HIPAA-compliant data handling.
AI & LLM developers
Safety alignment data, red-teaming annotation, and RLHF preference labelling for foundation model and fine-tuning teams.