A task-specific MCP server and a vendor-native one returned byte-identical Google Ads data and scored the same on accuracy. Across 60 benchmarked runs the real difference was determinism: 3.4-4.5x fewer tool calls, 93.3% vs 66.7% answer stability, and 6.3% vs 47.6% run-to-run token variance.
At NVIDIA GTC 2026, Nexla and Nebius showcase a live multi-agent AI pipeline that turns video input into structured travel itineraries using scalable AI infrastructure.
AI systems fail when context doesn’t scale. This article explains the limits of context graphs, why static relationships break for enterprise AI, and what’s needed to deliver accurate, trustworthy AI outputs at scale.
AI is shifting data engineering from code-heavy ETL to prompt-driven pipelines. Explore where LLMs fit, common pitfalls, and how Nexla makes AI-ready data workflows practical.
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
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.
The modern data stack has failed. The Fivetran–dbt merger highlights tool sprawl, rising costs, and integration complexity, forcing data leaders to rethink their infrastructure strategy. Choose wisely.
Poor data management can cost organizations 15–20% of revenue. Reusable, scalable data products help—but only if they’re consistent and reliable. A Common Data Model (CDM) standardizes and structures data, ensuring accuracy, scalability, and long-term value.
Most data teams waste time fixing brittle pipelines instead of driving insights. See how AI-powered transformation and Nexla’s Common Data Model cut manual work and ensure scalable pipelines.
Discover how Apache Iceberg separates storage from compute to improve modern data lake performance. See how Nexla supports CDC into Iceberg and also uses Iceberg as a source to streamline data integration and analytics.
