AI Data Validation & Evaluation Services
Ensure your AI models are accurate, ethical, and reliable with expert human evaluation. LXT delivers scalable AI data validation services to assess both training data and model outputs – helping you build AI that earns user trust.
Why companies choose LXT for AI data validation
Global Coverage
Evaluation across 1,000+ language locales and diverse demographics.
Human-in-the-loop Accuracy
From training data validation to post-deployment model evaluation.
Multimodal Capabilities
AI data validation across text, speech, image, and video for comprehensive AI quality.
Fast, End-to-end Expertise
From training data validation to post-deployment model evaluation, with rapid onboarding and delivery.
Enterprise Security
ISO 27001 certified, SOC 2 compliant, with secure facility options.
Scalable Solutions
Custom pipelines designed for enterprise-scale AI data validation, from pilot programs to full global deployment.
LXT for AI data validation & evaluation
With more than a decade of experience supporting global AI leaders, LXT designs and delivers custom AI data validation and evaluation programs that ensure accuracy, fairness, and trust. From defining evaluation methodology to providing high-quality validated data, we help clients build AI that is reliable, explainable, and responsible.
Our AI data validation and evaluation services cover training data and model outputs across all major AI domains – delivered in over 1,000 language locales by a managed global workforce. Backed by our quality guarantee, every project meets or exceeds enterprise data standards.
Our AI data validation & evaluation services include:
Training data validation
Validate datasets before training: quality, bias, coverage, completeness, formatting.
Search relevance evaluation
Assess query intent, ranking, personalization for search and recommendation systems.
AI model evaluation
Human validation of outputs for accuracy, bias, safety, and cultural relevance. Includes: Output Accuracy & Relevance, Bias & Fairness, Safety & Compliance, Multimodal & Multilingual.
Human in the loop
Continuous validation and feedback cycles to keep AI models accurate after deployment.
AI data validation & evaluation services in action: Case studies
Secure AI Data Validation & Evaluation
Evaluating AI training data and model outputs often involves sensitive information – from proprietary datasets to user interactions. At LXT, security is built into every project.
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ISO 27001 certified & SOC 2 compliant – enterprise-grade protection at every stage.
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Secure facility option – evaluations conducted by vetted staff in controlled environments, without crowd access, for high-sensitivity projects.
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Confidentiality-first workflows – NDAs, strict access controls, and minimal PII exposure.
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Transparent QA – auditable processes that meet enterprise and regulatory compliance standards.
With LXT, you can validate and evaluate AI data at scale – without compromising on security or trust.
Why AI data validation & evaluation matters
For any AI system – from speech recognition to generative AI – reliable performance depends on the quality of its data. Validating both training data and model outputs is essential to ensure results that are accurate, ethical, and trusted by users.
Human feedback plays a critical role at every stage:
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Before training – validating datasets for completeness, quality, and bias.
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During tuning – refining outputs to align with cultural and contextual expectations.
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After deployment – continuously monitoring performance with human-in-the-loop evaluation.
By prioritizing data quality, diversity, and fairness, organizations can reduce bias, prevent harmful outputs, and deliver AI experiences that are safe, accurate, and user-focused.
To ensure that machine learning models produce content that is accurate, comprehensive and reflective of different styles and contexts, data quality and diversity should be prioritized. AI must also be evaluated to identify and mitigate bias, offensive language or inappropriate content, particularly with customer-facing applications. After launching a product, robust evaluation metrics and mechanisms for gathering user feedback are important to provide input for model improvement iteratively and allow for continuous improvement.