Where AI Meets Human Precision
Train transformative LLMs and SLMs with the agility and quality needed for scale.
Innovative Operations Solutions for AI Model Training
A few of the most requested use cases from Invisible.
1
Supervised Fine-Tuning (SFT) as a Service
Tailor pre-trained models to specific tasks by fine-tuning them on labeled datasets for enhanced performance.
2
Evaluation as a Service
Assess and benchmark AI models, including testing, validation, and performance evaluation.
3
Multilingual Training
Produce datasets in multiple languages to train AI models for multilingual understanding and content generation.
4
Labeling
Assign specific tags or categories to data elements to facilitate accurate supervised learning and model training.
5
Annotation
Provide detailed contextual information or metadata to data points, enriching datasets to improve model understanding and performance.
A Secret Recipe We’re Willing to Share
We combine AI with a global team of human experts, on-demand to ensure a fast, flexible, and strategic solution. Amplified by generative AI and hundreds of integrations, our blend of human intuition with automation-driven efficiency removes those bottlenecks that legacy systems can’t.
Without Humans, Your AI Is Just Code
AI researchers need an expert partner to overcome labor, cost, complexity, and ethical challenges posed by human-in-the-loop training.
1
Scaling Human Judgment
Researchers need human-in-the-loop training and evaluation data, but can’t reconcile the practicalities of internal cost and resource allocation.
2
Real-World Application
AI developers are struggling to take the leap from high-performing lab models to universally applicable, real-world solutions.
3
Ethical AI
The race towards more complex AI systems is hindering developers from embedding ethical governance and adversarial resilience.
4
Lack of Agility
Deploying teams quickly across evolving research needs requires uniquely flexible labor that’s expensive to manage in-house and difficult to find in a BPO.
High-Quality Human Data at Any Volume
We train models for precision, differentiated capabilities, and helpfulness across real-world scenarios.
Access Global Expertise, On-Demand
Access a network of hundreds of advanced AI trainers, from trained operators to PhDs across domains, for rich and diverse preference data to power precision across generalized and specialized models.
Service Aligned with Research Goals
Our outcome-driven service model is tailored to your research goals and success metrics, augmented by a unique agility to pivot that enables experimental freedom.
Elevating AI with Human Insight
From structured labeling to complex RLHF tasks, our platform capabilities cover the gamut of training and data formats.
Supervised Fine-Tuning
Produce a specific, labeled demonstration dataset to improve a pre-trained model's performance on specialized tasks, aiming for enhanced accuracy and relevance in outputs.
RLHF
Train models to optimize actions based on evaluative feedback at scale, maximizing the model's ability to achieve specified goals effectively and efficiently.
Human Evaluations
Assessing a model's outputs to judge their quality, relevance, and appropriateness, aiming to ensure the model meets specific performance standards and user expectations.
Adversarial Training
Deliberately challenge a model with difficult or deceptive inputs to improve its resilience and accuracy against potential manipulation or unforeseen use cases.
Why AI Innovators Trust Invisible
See how we support the world's leading AI developers leverage Invisible to support complex research with high-quality, scalable human training data.
Get the Human Data Loop Whitepaper
See the culmination of 18 months of research into how AI research teams can get better ROI on human data operations and accelerate innovation at the same time.
We’re Committed to Responsible AI
Our goal is to transform our clients’ goals into reality through responsible and aligned AI development. To ensure we uphold the highest standards of ethical innovation, we stand behind these practices for responsible AI.
See the Full Policy1
Accountability and Responsibility
Ensure clear accountability and responsibility in AI development and outcomes, in keeping with client goals. Combine rigorous internal policies for model application and training with collaboration on safety and potential harms to secure higher quality data, ensure trainer safety, and manage complex challenges effectively.
2
Ethical Use and Integrity
Develop and use AI applications in a manner that respects human rights, promoting the welfare of all individuals and a safe working environment. This includes the ethical sourcing and enrichment of data used to develop AI systems.
3
Transparency and Explainability
Maintain transparency through our AI practices and policies to improve and apply AI systems. Emphasize the careful management of high-quality inputs to align model outputs with an intended or assumed goal.
4
Fairness and Non-Discrimination
Assist clients in achieving their goals, while giving careful consideration to avoid mistreatment or discrimination.
5
Responsible Partnership
Work with partners committed to responsible AI and human wellness, seeking those with a shared dedication to monitoring and evaluating AI applications while collaboratively adapting to challenges as they arise.
6
Privacy and Data Protection
Ensure high standards of data privacy and security, so that data utilized is collected, stored, and processed with the consent of all parties and in accordance with all relevant legal requirements.
7
Safety and Reliability
Prioritize the safety and reliability of AI systems throughout their lifecycle, ensuring they perform as intended and are free from known vulnerabilities. Based on client objectives, implement practices that ensure safety for our trainers and maintain reliability through consistent, data-driven methods.
8
Collaboration and Engagement
Collaborate actively with a diverse range of stakeholders to gather perspectives on ethical AI development and deployment. Aim to advance these principles together, aligning with partner goals for better understanding and cooperation.
9
Continuous Learning and Improvement
Foster a culture of continuous improvement and ethical innovation by encouraging an environment where sharing, learning, feedback, and the latest advancements in AI ethics are integrated into all aspects of our work.
10
Training and Awareness
Provide training for all workers involved in the AI lifecycle on ethical AI principles, the responsible use of AI technologies, and the importance of following these guidelines. Training is regularly updated to reflect current ethical standards and practices.
11
Culture of Compliance
Encourage a culture of openness where ethical considerations are discussed and there are clear processes to report issues.
Data Safety Shouldn’t Be Your Problem
We provide enterprise-grade data security, privacy, and compliance so you can focus on the bigger fish.
What We Have to Say
Stay up to date with industry insights from our experts.
Launch Transformative AI.
Speak to our team to discover how you can fast-track scale, speed, and efficiency in AI research.
Request a Demo
