Data Integration

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.

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.

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

Nexla and Vespa.ai partner to simplify real-time enterprise AI search, connecting 500+ data sources to power RAG, vector retrieval, and AI apps.

Nexla and Vespa.ai partnership eliminates data integration complexity for AI search and RAG applications. The Vespa connector delivers zero-code pipelines from 500+ sources to production-grade vector search infrastructure.

Reusable data products unify databases, PDFs, and logs with metadata, validation, and lineage to enable join-aware RAG retrieval for reliable GenAI applications.

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.

Raw feeds without context create endless rework. This metadata-first blueprint shows how to turn changing source feeds into governed, reusable data products with automated validation, lineage, and GenAI-ready contracts.

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.

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