High-quality, multilingual data services including RLHF annotation, AI red teaming, and safety testing to build safer and more capable AI systems.
Into23 provides the critical data backbone for developing and evaluating advanced AI models. Our services focus on generating high-quality, human-annotated data for RLHF, conducting adversarial AI red teaming to identify vulnerabilities, and performing rigorous safety testing. We specialize in creating diverse, multilingual datasets that enable your models to perform accurately and safely across global audiences.
We generate high-quality human preference data for instruction-following, helpfulness, and harmlessness, leveraging our expert annotators to refine model behavior.
Our dedicated teams simulate adversarial attacks to proactively identify and mitigate risks, biases, and vulnerabilities in your AI models before deployment.
With native-speaker annotators in over 75 languages, we collect and create culturally nuanced training data for a truly global AI performance.
We perform detailed evaluations of model outputs for accuracy, relevance, and safety, providing structured feedback to guide your development cycles.
Our annotators possess deep expertise in fields like finance, law, and medicine, ensuring your training data has the required technical accuracy.
Leveraging our ISO-certified processes and proprietary platform, we deliver high-volume, consistent data annotation to meet your project timelines.
We work with you to define data requirements, annotation standards, and project goals, creating detailed guidelines to ensure annotator alignment.
A dedicated team of native-speaking, domain-expert annotators is selected and trained on your specific guidelines, followed by calibration exercises.
Our teams generate and annotate data, including preference pairs, red team prompts, and safety labels, within our secure, scalable platform.
Every annotation passes through a rigorous QA process, including peer review, expert validation, and automated checks to ensure it meets our 98.7% agreement target.
Annotated data is delivered securely in your desired format. We establish a continuous feedback loop to refine guidelines and improve data quality over time.
A major AI developer partnered with Into23 to reduce harmful and biased outputs from their flagship language model. Our red team generated over 1.2 million adversarial prompts, identifying critical vulnerabilities. We then provided a high-quality dataset of 500,000 safety-aligned preference pairs created by our RLHF experts. This data was used to fine-tune the model, resulting in a 35% measured reduction in harmful content generation.
Reinforcement Learning from Human Feedback (RLHF) uses human preference data to fine-tune AI models to be more helpful, harmless, and honest. It is a critical step in aligning model behavior with human values and real-world quality standards.
We use a multi-layered QA process including annotator training, calibration rounds, peer review, expert validation, and automated checks. Our target of 98.7% inter-annotator agreement ensures data consistency across all projects.
Any AI model deployed in production can benefit, including large language models, chatbots, content generation tools, and enterprise AI assistants. Red teaming is especially valuable before major launches or market expansions.
Yes. While our 6 priority languages have the deepest annotator pools, we can source native-speaking annotators across 75+ languages for training data collection and annotation.
Our annotators are native speakers with domain expertise, not just bilingual generalists. They are trained on client-specific guidelines, calibrated for consistency, and managed through ISO-certified QA processes.
Get a custom quote for your AI training data project. Our team typically responds within 24 hours.