Content related to How to Prepare Content for AI
Enhancing Insurance Fraud Detection through Graph-Based Link Analysis
A national agency overseeing insurance claims engaged EK to advise on developing and implementing graph-based analytics to support fraud detection. EK applied key concepts such as knowledge graphs, graph-based link analysis for detecting potentially suspicious behavior, and the underlying technology architecture required to instantiate a fully functional solution at the agency to address client challenges. Continue reading
How to Implement a Semantic Layer: A Proven Operating Model
As organizations invest in enterprise AI and knowledge intelligence, the semantic layer serves as a critical foundation for providing a consistent, contextual framework that connects data assets across multiple sources to enable shared understanding, interoperability, and more intelligent use of … Continue reading
Graph Solutions PoC to Production: Overcoming the Barriers to Success (Part I)
Part I: A Review of Why Graph PoCs Struggle to Demonstrate Success or Progress to Production This is Part 1 of a two-part series on graph database PoC success and production deployment. Introduction I began my journey with graphs … Continue reading
Semantic Layer Strategy for Linked Data Investigations
A government organization sought to more effectively exploit their breadth of data generated by investigation activity of criminal networks for comprehensive case building and threat trend analysis. EK engaged with the client to develop a strategy and product vision for their semantic solution, paired with foundational semantic data models for meaningful data categorization and linking, architecture designs and tool recommendations for integrating and leveraging graph data, and entitlements designs for adhering to complex security standards. Continue reading
Top Semantic Layer Use Cases and Applications (with Real World Case Studies)
Today, most enterprises are managing multiple content and data systems or repositories, often with overlapping capabilities such as content authoring, document management, or data management (typically averaging three or more). This leads to fragmentation and data silos, creating significant inefficiencies. … Continue reading
Enhancing Taxonomy Management Through Knowledge Intelligence
In today’s data-driven world, managing taxonomies has become increasingly complex, requiring a balance between precision and usability. The Knowledge Intelligence (KI) framework – a strategic integration of human expertise, AI capabilities, and organizational knowledge assets – offers a transformative approach … Continue reading
Expertise Augmentation for Full Lifecycle AI/ML Operations
The current job market for these unique positions is dire, and delays in hiring can translate directly to delays in your projects. In-house skill gaps in AI technologies can be a formidable obstacle in your organization’s technical evolution, often blocking … Continue reading
AI Model Quality Assessment & Governance Framework
Many AI initiatives fail because there’s no plan for scale and maintenance. Has your organization already deployed AI/ML models and you’re looking for proven ways to maintain it? Working with some of the world’s largest and most complex organizations, EK … Continue reading
Enterprise LLM Accelerator
Many organizations are looking for a reliable way to experiment with and understand the applications of generative AI such as ChatGPT and Copilot. These tools, built using complex ML components like LLMs and neural networks, offer a lot of promise … Continue reading
Tool Selection & Solutions Framework Development
We recognize that navigating the myriad of technology options and AI models available for enterprise-wide AI initiatives can be challenging. As a vendor-agnostic consultancy, we partner with and integrate a wide range of tools and AI solutions on the market. … Continue reading