Insights, tips, and product updates

Unlock the full potential of satellite imagery with advanced approaches to Earth Observation data labeling.

Discover how collaborative approaches to geospatial data can overcome the 47% AI project failure rate in agriculture. Learn from real-world case studies and expert insights from our Geospatial World Forum 2025 presentation on transforming satellite imagery into actionable farming intelligence.

Despite impressive advances in natural language processing, systematic analyses of LLM reasoning abilities remain limited. Our comprehensive benchmark study implements a multi-dimensional evaluation framework across eight prominent models—OpenAI's O1, OpenAI's O3 Mini, Claude 3.5, Claude 3.7, Mistral, Qwen2.5, DeepseekR1, and QwQ-32B—to provide unprecedented insights into their reasoning strengths and limitations.

The quality of annotated geospatial data directly impacts the success of machine learning models, computer vision applications, and automated feature extraction. For geospatial data scientists and GIS professionals working with satellite imagery, aerial photographs, and other remote sensing data, selecting the right annotation tool is crucial for achieving accurate results and efficient workflows.

Learn about challenges, deployment options, and best practices for data security and privacy. This article delves into the complexities of safeguarding unstructured data during labeling and provides a comprehensive guide for data scientists, ML engineers, and AI project directors.

This article recaps what we learned in our most recent webinar, where we delve into the intricacies of fine-tuning Large Language Models (LLMs) and their importance in artificial intelligence and machine learning.

This article uncovers the hurdles that businesses often face when it comes to annotating data and explores the avenues available to overcome them. Let's dive in!

Machine learning models are only as good as the data used to train them. The only thing your company needs to drive AI success is a large amount of high-quality, well-labeled training data. To help you achieve excellent-quality training datasets, we'll share the knowledge we've gained on the subtle art of choosing an adequate data-labeling platform.

Geospatial data analysis requires accurate, high-quality annotations to extract meaningful insights. Kili Technology’s geospatial annotation tool enhances efficiency with AI-powered labeling, seamless GIS integration, and scalable workflows for high-resolution imagery.

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.

Explore LLM reasoning limitations in logical deduction and ongoing efforts to enhance these capabilities through chain-of-thought reasoning, coding, and other novel techniques.

Many companies around the world are currently trying to make the creation & deployment of AI as easy and efficient as possible. Among them, two contestants stand out: Kili Technology and LatentAI. Let's dive into how to use them to your advantage!

Unaware users might unknowingly rely on these AI-generated answers, leading to adverse outcomes, especially in fact-driven fields such as law, healthcare, and finance. In this article, we delve into LLM hallucination, discussing real-world examples, causes, and mitigation strategies.

Discover how to tackle the biggest challenges of satellite imagery and geospatial data annotation. Learn which tools are available to you.

We recently released our red teaming study diving into adversarial prompt injection techniques and the harm categories that LLMs are most vulnerable to. Now, let's have a look at the new and existing studies that serve to combat these adversarial prompting techniques.

Meta's Segment Anything Model 2 (SAM 2) represents a major leap forward in visual segmentation technology. Built on an innovative streaming architecture and trained on the largest video segmentation dataset to date, SAM 2 delivers superior accuracy with 3× fewer user interactions compared to previous approaches. This article explores how SAM 2's memory-equipped design and three-phase data collection methodology translates into practical applications through platforms like Kili Technology.

Today, Kili Technology addresses the fundamental challenges of computer vision data labeling with the integration of Meta's Segment Anything Model 2 (SAM2) into our annotation platform, bringing unprecedented efficiency to both geospatial and video annotation tasks.

Ensuring the safety of large language models (LLMs) across languages is crucial as AI becomes more integrated into our lives. This article presents our red teaming study at Kili Technology, evaluating LLM vulnerabilities against adversarial prompts in both English and French. Our findings reveal critical insights into multilingual weaknesses and highlight the need for improved safety measures in AI systems.

This article explores the latest developments in AI safety, including regulatory frameworks, risk management strategies, and technical approaches to mitigate potential harms. From red teaming and toxicity detection to reinforcement learning from human feedback, we delve into the multifaceted efforts to create AI systems that are not only powerful, but also reliable, ethical, and aligned with human values.

Explore the latest techniques in red teaming large language models (LLMs) and crafting adversarial prompts. Learn how to evaluate and improve AI safety through automated testing methods, benchmark datasets, and robust defense strategies.