Introducing 
Express — Go from prompt to pipeline in minutes.
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.
Reusable data products unify databases, PDFs, and logs with metadata, validation, and lineage to enable join-aware RAG retrieval for reliable GenAI applications.
In episode six of DatAInnovators & Builders Podcast, Fred Gertz explains how swarm intelligence solves NP-hard routing and scheduling problems in seconds—without training data or LLMs.
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.
In episode five of DatAInnovators & Builders Podcast, GrowthX founder Marcel Santilli explains the delegation test for AI and why poor context, not weak models, is the real reason AI initiatives fail to scale.
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.
In episode four of DatAInnovators & Builders Podcast, BigID’s Stephen Gatchell explains the data governance gap blocking AI production, why unstructured data breaks legacy models, and how data product frameworks enable scale.
In the News: betanews.com: In this Q&A, Saket Saurabh explains why context engineering is key to reliable, compliant, and intelligent enterprise AI workflows.
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.
In this third episode of DatAInnovators & Builders Podcast, BigPanda’s Alexander Page shares how his team designs AI agents that internalize corrections, evaluate tool use, and scale reliably in production.
