Content related to Transforming Tabular Data into Personalized, Componentized Content using Knowledge Graphs in Python
Knowledge Cast – Lasse Andresen, Founder & CEO of IndyKite
Enterprise Knowledge COO Joe Hilger speaks with Lasse Andresen, founder and CEO of IndyKite Inc., the first system of intelligence built on a live context graph. The result is agentic AI that can operate across platforms with precision and deliver … Continue reading
Expert Analysis: What is Enterprise AI-Ready Content?
Scaling Your AI Pilot with the Right Contextual Foundations There’s a rush to build AI solutions: recommendation engines, chatbots, analytics dashboards, and virtual agents. But chasing shiny tools, without understanding the full picture can be risky. The organizations that truly … Continue reading
What is the Difference Between a Semantic Layer and a Context Layer? When to Use a Knowledge Graph vs. a Context Graph
Before AI became part of everyday conversations, most enterprise knowledge and data projects had a somewhat straightforward goal: to create a “single source of truth.” In theory, this meant that everyone in the company could look at the same search … Continue reading
Leveraging a Semantic Layer for Research Curation and Conversational Experiences
The Challenge A global philanthropic organization focused on health programs struggled to fully leverage knowledge from semi-structured and unstructured documents. Specifically, within a health-related funding program, researchers lacked access to key qualitative data from end-user surveys and transcripts. Consequently, they … Continue reading
Building the Semantic Layer: Scaling Enterprise Intelligence at a Global Investment Firm
The Challenge A global investment firm with a $330 billion dollar portfolio and 50,000+ employees struggled with fragmented data. Investment professionals were losing critical time hunting for assets across disconnected systems. Detailed deal records were scattered as a mix of … Continue reading
GraphRAG in the Enterprise
Retrieval-Augmented Generation (RAG) is a commonly utilized pattern for grounding large language models in enterprise data. Instead of solely relying on a model’s training, RAG collects relevant information from internal sources, documents, knowledge bases, and other systems; it then uses … Continue reading
Optimizing Historical Knowledge Retrieval: Extracting Knowledge by Making Connections
The Challenge From POC to Production A Federally Funded Research and Development Center (FFRDC) faced significant challenges with low-quality or incomplete metadata for managing and cataloging scientific reports, hindering researchers’ ability to parse repositories and efficiently discover relevant content. As … Continue reading
What Are Explainable AI Knowledge Portals and Why Do You Need Them?
Knowledge Portals first came into prominence in the early 2000s. These initial portals were a collection of static links to information on everything from HR systems to company policies and communications. It was a single location to access the systems … Continue reading
Graph Database Evaluation for a Financial Services Firm
A financial services firm built a mature graph data ecosystem, but the graph database they selected originally did not scale as multiple business-critical solutions relied on graph data. As application and business teams across multiple stakeholder groups expanded usage … Continue reading
Taxonomy Alignment for SDG Tagging
In the session “Utilizing Taxonomies to Meet UN SDG Obligations“ co-presented at KMWorld 2025 on November 17th, Enterprise Knowledge’s Ben Kass and ASHA’s Mike Cannon discuss how they structured taxonomies within an ongoing auto-tagging implementation to serve content management and … Continue reading