Data Engineering

Developers built real AI apps in hours with Express.dev. See how hackathon teams turned messy data into production-ready solutions.

See how Nexla’s Org Intelligence turns every new data connection into smarter, faster, AI-ready enterprise data products.

Discover why context graphs fail at scale and how semantic structure delivers reliable runtime context for enterprise AI agents.

Enterprise AI agents fail when the context behind their decisions is incomplete, stale, or conflicting. Context engineering ensures agents receive accurate, permission-aware runtime context for reliable decisions.

Explore how a multimodal AI pipeline built with NVIDIA models, Nebius infrastructure, and Nexla orchestration converts social media travel videos into structured itineraries.

Essential checklist for validating AI-ready data before building LLM pipelines. Learn the 10 critical steps ML teams must follow to ensure quality, freshness, and compliance.

Context engineering is the systematic practice of designing and controlling the information AI models consume at runtime, ensuring outputs are accurate, auditable, and compliant.

A research-backed framework for evaluating LLM-generated data transformations. Learn how datasets, sandboxed execution, and automated judging reveal failure patterns and model performance across real-world data engineering tasks.

Explore how Express.dev makes AI agents capable of generating rich, interactive UI for structured data workflows. From XML-driven forms to real-time validation and OAuth flows, generative UI turns chat into a truly collaborative experience.

While it is true that AI offers enormous opportunities for innovation and success, its reliance on personal data raises urgent concerns about privacy, ethics, and governance

A podcast for data leaders looking for real conversations on AI adoption and data architecture, hosted by Nexla CEO Saket Saurabh.

After years of solving data variety, we built Express, a conversational data engineering platform that turns complex data work into simple, prompt-driven pipelines making data engineering accessible to everyone.