# kili-technology.com > AI-optimized mirror of kili-technology.com containing 22 pages totalling 4,568 words of clean markdown content, structured data, and semantic HTML. Original source: https://kili-technology.com/. Last updated: 2026-06-15T18:49:52.670Z. Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG). ## Homepage - [Build high-quality, trustworthy datasets for powerful AI](/content/site-root.html): Build high-quality and trustworthy datasets to train, fine-tune, and evaluate your AI models. Complete with a robust annotation tool built for collaboration at scale, quality-first workflows, and secure deployment. (1,340 words) ## Articles & Blog Posts - [blog/index.html](/content/blog/index.html) (1 words) - [Let's meet and discuss](/content/events/index.html) (522 words) - [Evaluate our tool](/content/book-a-demo/index.html) (584 words) - [Privacy Policy](/content/legal-notice/index.html) (18 words) - [Our customer stories](/content/case-studies/index.html) (350 words) - [Kili Technology](/content/pricing/index.html) (896 words) - [Contact our team](/content/contact/index.html) (605 words) - [Why Kili](/content/why-kili/index.html) (18 words) - [About](/content/about/index.html) (18 words) - [AI Benchmarks 2026: Top Evaluations and Their Limits](/content/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough.html): AI benchmarks saturate while production failures grow. This guide maps every major 2026 evaluation category and explains why human expert review still wins. (18 words) - [Human-in-the-Loop, Human-on-the-Loop, and LLM-as-a-Judge for Validating AI Outputs](/content/blog/human-in-the-loop-human-on-the-loop-and-llm-as-a-judge-for-validating-ai-outputs.html): What's the difference between LLM-as-a-judge, HITL, and HOTL workflows? We cover this and provide practical tips for each application in our latest guide. (18 words) - [Kimi K2.6: What This Open-Weight Model Actually Means](/content/blog/data-story-kimi-k2-6/index.html): Kimi K2.6 is the first open-weight model to beat GPT-5.4 on SWE-Bench Pro. Architecture is identical to K2.5; the gains live in the training recipe. (18 words) - [How to build high-quality datasets for Insurance AI](/content/blog/how-to-build-high-quality-datasets-for-insurance-ai.html): Discover the significance of high-quality datasets for Insurance AI and how they drive reliable and effective AI systems in the insurance industry. Learn about annotation best practices, tools, and the impact on customer experience. (18 words) - [Open-Sourced Training Datasets for Large Language Models (LLMs)](/content/blog/9-open-sourced-datasets-for-training-large-language-models.html): We share 17 open-sourced datasets used for training LLMs, and the key steps to data preprocessing. (18 words) - [Active Learning for Object Detection](/content/blog/active-learning-for-object-detection/index.html): Kili's annotation platform is designed to quickly get a production-ready dataset. Let's see how active learning is applied to object detection. (18 words) - [Few-shot Learning methods for Named Entity Recognition](/content/blog/few-shot-learning-methods-for-named-entity-recognition.html): Few shot Named Entity Recognition learning methods have been developed to be able to generalize to new tasks with few labeled examples. We will review several recent methods and see how they are different from traditional learning. (18 words) - [Agentic AI Benchmarks Guide: What They Are, How They Work, and Why They Aren't Enough](/content/blog/agentic-ai-benchmarks-guide-what-they-are-how-they-work.html): This guide explains what agentic AI benchmarks measure, how the major 2026 evaluation boards work, and why a high leaderboard score is a weak predictor of production performance. It documents benchmark gaming and a measurement imbalance toward technical metrics, then sets out how teams should evaluate AI agents using layered, human-calibrated methods. (18 words) - [A Guide to the FineWeb2 Dataset: How It's Built, Filtered, and Used for Training LLMs](/content/blog/fineweb2-dataset-guide/index.html): Explore the FineWeb2 dataset: 20TB of multilingual pre-training data covering 1,000+ languages. Learn how its filtering pipeline builds better LLMs. (18 words) - [Data Story: A Deep Dive into Qwen 3's Data Pipeline](/content/blog/data-story-qwen3/index.html): This article breaks down Qwen3's technical report through its data processing pipeline, and then extends the same reasoning to Qwen3 Max Thinking. (18 words) - [Understanding DeepSeek R1—A Reinforcement Learning-Driven Reasoning Model](/content/blog/understanding-deepseek-r1/index.html): Explore the groundbreaking advancements of DeepSeek R1, a reinforcement learning-driven reasoning model. Learn about its distillation into smaller models, ensuring cost-effective, efficient reasoning capabilities across various domains. (18 words) - [DeepSeek V3.2 Explained: How Data, RL, and Sparse Attention Shape Performance](/content/blog/data-story-deepseek-v3-2/index.html): A deep technical breakdown of DeepSeek V3.2, examining how training data, synthetic pipelines, sparse attention, and post-training RL shape reasoning and performance. (18 words) ## Resources - [Full Page Index](/index.html): Browse all cached pages with rich metadata - [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines - [XML Sitemap](/sitemap.xml): Machine-readable sitemap for crawler discovery - [Robots.txt](/robots.txt): Crawler directives