3 Ways to Adapt a Foundation Model to Fit Your Specific Needs
Thinking about using a foundation model for your ML tasks? Here are three ways you can adapt them to fit your needs.
Complete platform for named entity recognition, sentiment analysis, and text classification. ChatGPT pre-annotation accelerates labeling while quality controls ensure accurate training data.
.webp)
Connect ChatGPT to automatically extract entities, classify documents, and analyze sentiment. Define custom prompts that match your annotation schema. Human reviewers validate and correct AI predictions, combining automation speed with expert accuracy. Reduce annotation time by 70% while maintaining quality standards.


Named entity recognition with nested entities and typed relations. Sentiment analysis with custom emotion taxonomies. Multi-label text classification with hierarchical categories. Document annotation for PDFs with text extraction. Span selection for any text highlighting task. Customizable interfaces adapt to your specific requirements.
SOC2, ISO 27001, and HIPAA certified platform. Choose cloud, on-premise, or hybrid deployment. Single sign-on integration, API access, and remote storage compatibility ensure your data stays secure while teams collaborate. Support for 100+ languages including right-to-left scripts.

You can also check out our documentation to learn more about our features. We're ready when you are.
Learn how Kili Technology has changed the way these teams train, fine-tune, and evaluate their models.
Stay up to date with fresh content from our team — tutorials, use cases, and ideas to help you train AI/ML models better.
Got questions about Kili Technology? Check out our FAQ. If you can't find it in this list, drop a question for our team.
Kili provides comprehensive text annotation capabilities including classification, named entity recognition (NER), sentiment analysis, and conversational data labeling. The platform supports LLM fine-tuning tasks including Reinforcement Learning from Human Feedback (RLHF) and Supervised Fine-Tuning (SFT) with expert annotators.
Kili's ChatGPT integration enables intelligent pre-annotation for text labeling tasks, allowing teams to leverage AI assistance while maintaining quality through expert human validation for domain-specific requirements.
Yes, Kili Technology's text annotation platform supports projects in multiple languages. The customizable interfaces and flexible ontologies allow teams to configure language-specific annotation schemas while maintaining consistency across multilingual datasets.
Kili implements inter-annotator agreement metrics specifically designed for text annotation, along with programmatic QA through customizable plugins. The consensus workflows ensure critical text data receives multiple reviews for validation, maintaining high quality for NLP model training.
Kili provides specialized workflows for LLM fine-tuning including RLHF and Supervised Fine-Tuning capabilities. The platform's system combines human expert feedback with LLM assessments, enabling teams to align models with specific domain requirements through iterative annotation and evaluation cycles.
Kili Technology offers enterprise-grade security with SOC2, ISO 27001, and HIPAA certifications. For handling confidential text data, the platform provides on-premise, hybrid, and air-gapped deployment options. Additional security features include single sign-on integration, role-based permissions, and comprehensive audit logs for compliance requirements.
Kili scales to support unlimited annotators for text projects. The platform's largest enterprise clients successfully manage text annotation projects with more than 100 concurrent annotators, supported by advanced collaboration features including role-based workflows, real-time progress tracking, and automated task distribution.