A data platform for AI agents must do 7 things: connect, abstract, govern, deliver, act, observe, secure. Use this checklist to evaluate any vendor or stack.
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 partnership eliminates data integration complexity for AI search and RAG applications. The Vespa connector delivers zero-code pipelines from 600+ 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.
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.
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.
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.
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.
