The Vital Role of Data Integration and Engineering in GenAI Adoption
From GenAI prototypes to production: the contributions of integration engineers in model management, vector pipelines, RAG workflows, GPT quality control, & LLM governance.
Agentic Data Integration
Agentic integration is the practice of building the data and pipeline layer specifically for AI agents, so agents can discover, request, and act on enterprise data conversationally instead of through hand-coded pipelines.
Nexla’s Agentic Integration pillar spans Express.dev, Agentic RAG, and Helix Context Layer, so agents get grounded, governed data instead of hallucinating from ungoverned raw sources.
Harnessing OpenAI’s Latest No-Code GPT Models: A Data Training Primer
With OpenAI’s unveiling of customizable, no-code GPTs for specialized applications, the question arises: How can…
4 Ways Generative AI is Transforming Data Engineering
Generative AI (GenAI) is a type of artificial intelligence that can generate new data, such…
Enhancing LLMs with Private Data: A Comprehensive Tutorial using Nexla, Pinecone & OpenAI
Operationalizing Large Language Models (LLMs) is the next big opportunity in AI. Any organization…
The Data Layer Your AI Is Missing
Connect, contextualize, and govern enterprise
data across 1000+ systems in real time.
