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.

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

Governed self-service data embeds metadata controls, quality guardrails, and access policies. This enables business users to explore and transform data in no-code while preventing metric drift.

Agentic RAG systems fail when data is fragmented, stale, or inconsistent. Learn how AI-ready data products with standardized schemas, governance, and retrieval metadata enable reliable, scalable RAG applications.

Customer API and CSV feeds create engineering bottlenecks. Learn how to standardize raw customer data into governed, reusable data products using Common Data Models—eliminating custom integrations and scaling onboarding.

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.

Nexla brings 500+ pre-built data connectors to Microsoft 365 Copilot, enabling organizations to easily integrate internal and third-party data for smarter AI workflows.

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.