a desnk with a 15-step pipeline graphic for orchestrating enterprise AI brilliance with agentic RAG systems allowing to dock data sources like pipes and enabling user specific datasources within the enterprise.
by Tim

Enterprise AI Excellence: How to do an Agentic Enterprise RAG

Revolutionize enterprise AI with agentic RAGs. This guide explores a 15-step pipeline and offers insights for enterprise AI implementation.
read more
The title image is a visually engaging graphic (1200x628 pixels, per Search Engine Journal) depicting a stylized, simplified version of the data ingestion pipeline flowchart from the article. It features a clean, dark blue background (#005580) with a central flowchart of 6 steps, arranged vertically, using transparent rectangles with black borders for steps (e.g., “Source Crawling,” “Data Preprocessors”) and white document shapes for outputs (e.g., “Chunked Data”). Black ellipses highlight processing stages (e.g., “Data Embedding”), connected by black arrows with arrowheads. Dashed containers list extensions (e.g., “Vector Embedding Generation,” “Filter Specification”) in Arial font (12px for steps, 11px for extensions). A subtle overlay of interconnected nodes and data flow lines (representing polyglot databases like vector, SQL, graph) spans the background, with icons for text (document), images (photo), and JSON (code brackets). The article title, “Scalable Polyglot Data Ingestion Framework for AI-Driven Search Ecosystems,” is overlaid in bold, white Arial font (24px) at the top, with a tagline “Enabling Vector, SQL, and Graph Indexing” in smaller text (16px) below.
by Tim

Polyglot Knowledge RAG Ingestion Concept for Enterprise Ready AIs

Explore a scalable polyglot data ingestion framework for AI-driven search ecosystems, supporting vector, SQL, and graph indexing. A flowchart details 6 steps for preprocessing and embedding, enabling robust RAG search.
read more
Healthcare and AI integration featuring a futuristic hospital scene with a robot assisting a doctor, showcasing advanced medical technology and AI-driven patient care.
by Tim

Unleashing RAGs from Vector Search Shackles in Healthcare

An in-depth analysis of the intricate challenges of vector-only Retrieval-Augmented Generation (RAG) pipelines, spotlighting these issues […]
read more
by Tim

NLWeb’s AI Demystified: How an Example Query is Processed in NLWeb

Explore the NLWeb search prototype’s query processing pipeline in this detailed NLWeb example, showcasing how it handles "Find vegetarian recipes for Diwali" with prior context. Learn from flowcharts and insights drawn from the NLWeb GitHub repository, illustrating the system’s use of AI, Schema.org, and vector search to deliver precise results.
read more
Kraken illustration of NLWeb, featuring the Kubernetes (K8s) logo prominently displayed. The octopus-like creature with eight tentacles is set against a deep blue underwater background, symbolizing NLWeb's strength and versatility in web development and tech solutions. Perfect for searches related to NLWeb, Kubernetes, and innovative technology.
by Chris

NLWeb Deployment in Kubernetes GitOps Style with FluxCD

NLWeb is revolutionizing web development by integrating advanced machine learning and AI, as showcased in recent insights. Picture NLWeb as a powerful kraken, its tentacles infused with the Kubernetes (K8s) logo, symbolizing the robust infrastructure behind these AI-driven solutions. NLWeb delivers dynamic, intelligent websites that adapt and evolve, offering unparalleled user experiences.
read more
Augmented reality view of a tech workspace featuring the Markdown document "How JSON-LD and Schema.org Can Improve RAGs and NLWeb" displayed as holographic panels. A glowing brain hologram with pulsating synapses floats in front of a laptop screen, with radiant lines connecting the brain to the content, symbolizing AI-driven understanding. The NLWeb logo and vector search visuals overlay the screen, while a knowledge graph hologram connects a notebook labeled "Project X" on the desk to digital entities. This futuristic AR scene illustrates transforming markdown to JSON-LD for AI training data, enhancing structured data for NLWeb, and creating a digital AI twin.
by Tim

How Markdown, JSON-LD and Schema.org Improve Vectorsearch RAGs and NLWeb

Learn how JSON-LD and Schema.org enhance RAG and NLWeb with structured data. Discover howto use markdown for AI training data, boosting SEO, and creating a digital AI twin.
read more
Cover image for the article 'Guide: Exposing Enterprise Data with Java and Spring for AI Indexing (for NLWeb)' featuring the Java and Spring logos prominently displayed alongside AI and NLWeb branding elements. The design includes a graph database visualization with interconnected nodes, symbolizing knowledge graphs and semantic data. A modern, professional aesthetic with a blue and white color scheme highlights the integration of Schema.org datatypes, JSON-LD, and OrientDB for enterprise data solutions. The background incorporates subtle binary code patterns, emphasizing AI-driven indexing and the semantic web for NLWeb’s conversational interfaces.
by Tim

Guide: Exposing Enterprise Data with Java and Spring for AI Indexing (for NLWeb)

Discover how to expose enterprise data for AI indexing with Java and Spring using the jsonld-schemaorg-javatypes library for NLWeb. Learn to leverage Schema.org, JSON-LD, and OrientDB for semantic search, knowledge graphs, and interoperability, with sustainable Fair Code licensing.
read more
NLweb the next semantic web
by Chris

Guide: How to Use NLWeb to Unleash AI-Powered Websites

Discover how NLWeb, Microsoft’s open-source protocol from Build 2025, transforms websites into AI-powered knowledge hubs. This comprehensive guide covers setup, data optimization with the A-U-S-S-I framework, Azure deployment, and chatbot integration. Explore use cases for news agencies and blockchain AI agents, code generation for logistics and licensing, and NLWeb’s future in internationalization and voice search. Learn its strengths, challenges, and potential to redefine web interactions.
read more
by Tim

Reimagining Open Source: Innovative Solutions in the Age of Exploitation

The software industry confronts a defining moment. Open Source Software, as delineated by the Open Source Initiative (OSI), has long been a cornerstone of technological advancement, enabling collaborative triumphs like the Linux kernel. Yet, its open-access ethos harbors a persistent flaw: exploitation. Can we stop it?
read more
by Tim

How NFTs and AI can solve Public Transport challenges

Analysis and predictions of occupancy in public transport are essential in order to use vehicles intelligently […]
read more
iunera square logo together with the cloud iunera square logo together with the cloud
© 2025 iunera GmbH & Co KG