Insights, tips, and product updates

Explore the FineWeb2 dataset: 20TB of multilingual pre-training data covering 1,000+ languages. Learn how its filtering pipeline builds better LLMs.

Intelligent Document Processing (IDP) minimises human errors by automating data entry. Learn more about what IDP is, how it works and its benefits for modern enterprises.

This article breaks DeepSeek V3.2 down end-to-end—from continued pre-training to specialist distillation to mixed RL to evaluation—focusing on how training data is built, curated, and used as a control surface for model behavior, reasoning capabilities, and model performance.

This is a mega article breaking down Meta's extensive work and documentation on the data engine to build SAM 3.

Whether you're building training data for a cutting-edge autonomous system or developing retail analytics, video annotation is the foundation of computer vision success. The right combination of skilled annotators, efficient video annotation tools, and robust processes will help you create the accurate video annotations your AI models need to perform in real world applications.

This ultimate guide covers all the important aspects of image annotation: what is meant by image annotation? How do you annotate an image? What are the different annotation types? What is an image annotation tool? Find out what image annotation is all about, and how it can improve your business with expert AI data.

Learn how modern data labeling combines automated labeling and expert HITL workflows to embed subject-matter expertise throughout the AI lifecycle, improving data quality, scalability, and model performance in production.

What is data labeling in 2026? Learn how high-quality labeled data, human-in-the-loop workflows, and automation drive reliable, scalable AI performance across industries.

Enhance your geospatial imagery annotation workflows with Kili’s latest platform updates, including external layer integration, clearer image borders, flexible layer reordering, and improved team visibility for faster, more accurate geospatial data labeling.

Organizations see AI succeed in tests and fail in production. This article explains why—uncovering evaluation gaps, model specialization, and the rise of agentic workflows.

This article synthesizes insights from Kili Technology's recent geospatial AI roundtable discussion featuring industry experts from government, consulting, and technology sectors. Our panelists shared real-world implementation experiences, revealing critical gaps between AI promises and production realities while providing actionable guidance for organizations evaluating their geospatial AI strategies.

This article examines the modern pipeline for satellite imagery processing, exploring how the integration of AI-assisted annotation tools like SAM2, foundation models like DINOv2, and intelligent data processing platforms are changing earth observation workflows across industries.

As more industries rely on geospatial data and advance geospatial AI, new and persistent challenges present themselves. These become blockers towards production-ready AI and wastes time, money, and effort. Here's what to consider when building geospatial datasets for AI training.

Check out our latest updates for our geospatial projects interface.

Learn how to build reliable geospatial training data for AI from Enabled Intelligence's expertise. Discover geospatial annotation best practices, quality assurance workflows, and technical solutions for scaling satellite imagery labeling projects while maintaining 95%+ accuracy across defense, climate research, and commercial applications.

We're proud to support the upcoming AI and Geospatial Dialogue series by Geoawesome - a geospatial community initiative deep diving into the advancements of AI in the geospatial space.

Discover how geospatial foundation models are revolutionizing Earth observation. Learn about key models, applications, challenges, and implementation strategies.

Geospatial data annotation requires far more than drawing bounding boxes on satellite imagery. As machine learning engineers develop advanced geospatial intelligence, annotation quality has become the critical differentiator for production model performance. This article explores advanced techniques that transform geospatial data labeling into a sophisticated discipline, covering multi-modal fusion, automation workflows, and validation methods for reliable AI systems.

We are excited to introduce our new Geospatial Projects. Designed specifically with geospatial standards and workflows in mind, this new project type unlocks richer capabilities and a more tailored experience, allowing users to fully harness the potential of geographic data.

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