Kiln is an end-to-end AI development workspace that lets teams build, refine, and ship custom models without wrestling with complex infrastructure. Designed for developers, data scientists, and product teams, Kiln streamlines every step of the lifecycle: synthetic data generation, dataset management, collaborative review, fine-tuning, evaluation, and deployment. With Kiln, you can quickly generate high-quality synthetic datasets to cover edge cases, sensitive scenarios, or low-volume domains, all without exposing real user data. A visual, no-code interface lets non-ML experts explore prompts, label examples, and curate training data, while advanced users can plug in existing pipelines, APIs, and model endpoints. Kiln supports iterative fine-tuning so you can adapt open-source or proprietary foundation models to your specific use case, improving accuracy and reliability over time. Built-in experiment tracking and side-by-side comparisons make it easy to see which configuration performs best in real-world tasks. Collaborative workflows allow engineers, PMs, and domain experts to comment on examples, approve changes, and keep datasets versioned and auditable. Whether you’re building AI copilots, internal tools, or production-grade model APIs, Kiln reduces the friction between experimentation and deployment. Ship higher-quality models faster, with better control over data, performance, and governance—all from a single, intuitive interface.
Product teams prototype an AI assistant for customer support by generating synthetic conversations, curating edge cases, and fine-tuning a base model before connecting it to live data.
Developers adapt an open-source LLM to internal documentation by importing existing knowledge bases, generating labeled Q&A pairs, and tracking model performance across releases.
Data science teams build domain-specific models for healthcare or finance using synthetic data to cover sensitive scenarios while maintaining privacy and compliance.
Engineering teams maintain an AI code copilot by continuously collecting feedback examples, updating training datasets, and running regression tests on new model versions.
Startups quickly stand up production-ready AI APIs by iterating on prompts, training specialized models, and deploying the best-performing configuration from one interface.