Any organization building or deploying AI systems, such as LLMs, copilots, or automation, can benefit from AI data services for AI performance optimization. These solutions are especially valuable for improving model accuracy, safety, and performance in real-world use cases.

Improved accuracy, relevance, and consistency of AI outputs, along with reduced risk and stronger user experiences.

—Chatbot and copilot response evaluation

—Multilingual performance and localization validation

—Model comparison and performance testing

Yes. We support text, image, video, and audio workflows, including both data annotation and model evaluation.

Yes. AI systems require continuous evaluation and human feedback to maintain quality, adapt to new use cases, and prevent performance drift over time.

They reduce risk by ensuring AI outputs are accurate, safe, and aligned with your brand, while improving efficiency and user trust.

Lionbridge combines global scale, multilingual expertise, and human-in-the-loop evaluation to deliver high-quality AI data solutions across complex, real-world use cases.

We go beyond basic annotation to support modern AI workflows, including model evaluation, human feedback, and multilingual performance at scale.

We provide structured wellness programs, including 24/7 psychological support, to protect contributors working with sensitive content.

Structured and labeled data help AI models learn patterns, reduce ambiguity, and produce more accurate outputs. It also supports evaluation and validation, ensuring models perform reliably in real-world use cases.

Supervised learning is a method where models are trained on labeled data to learn the relationship between inputs and expected outputs. It’s commonly used for tasks like classification, prediction, and structured AI workflows.

Improve data quality, apply human evaluation and feedback, and continuously test model outputs. Ongoing monitoring and refinement help ensure consistent performance and better real-world results.

Labeling or categorizing data to help an AI model better understand it. Data annotation is fundamental for ensuring AI models can make predictions based on annotated data. The quality and accuracy of data annotation significantly influence AI model training and, thus, performance. Services include:

• Content Classification
• Image or Video Annotation
• Named Entity Recognition

Aggregation of relevant, high-quality data to train and test AI models. Data can be in various formats and comes from sources, including databases, social media, sensors, user interactions, text, images, audio, and video. Collecting diverse and representative data ensures your AI system understands and responds accurately to a wide range of inputs. This makes it more efficient and effective. Services include:

• Audio Dataset
• Video Datasets
• Text Datasets
• Transcription
• Taxonomy Development
• Intent Utterance Creation
• Text-to-speech and speech-to-text

Ensures the results generated by AI models and LLMs are accurate, relevant, and culturally appropriate. We thoroughly review AI responses to validate alignment with goals and required standards. Validation enhances overall quality, and makes AI systems more reliable, effective, and trustworthy for your users. Services include:

• Intent development and review
• Model output validation and ranking
• Diversity and inclusion testing
• Output fact and relevance testing
• Search, product, and ad relevance
• Cultural enhancements
• Geolocation validation and relevance

Optimize your capacity and time with our workforce management solutions. Whether you need a work-from-home base or secure location, Lionbridge offers the resources, management, and data you can depend on. Services include:

• AI product testing
• Secure facilities
• Computational linguistics
• Data & ML engineering
• Global community resourcing
• Subject matter speculation

Creation and refinement of an AI model’s ability to understand, generate, and manipulate language. Fine-tuning the LLM to enhance performance, inclusivity, accuracy, and relevance. It requires expertise in natural language processing and data engineering. Services include: 

• Multilingual prompt engineering
• Retrieval-Augmented Generation (RAG) pattern support
• Diversity and inclusion testing
• Local market optimization
• Model review and assessment
• Output fact and relevance-checking