Blog Archives
Initiative-Specific AI Model Starter-Kit
There is no single AI solution that works in every situation, especially when considering the constraints of real-life problems, data types, and organizational diversity. We developed our Initiative-Specific AI Model Starter-Kit based on the experience and lessons learned from supporting … Continue reading
Women’s Health Foundation – Semantic Classification POC
A humanitarian foundation focusing on women’s health faced a complex problem: determining the highest impact decision points in contraception adoption for specific markets and demographics. Two strategic objectives drove the initiative—first, understanding the multifaceted factors (from product attributes to social influences) that guide women’s contraceptive choices, and second, identifying actionable insights from disparate data sources. The key challenge was integrating internal survey response data with internal investment documents to answer nuanced competency questions such as, “What are the most frequently cited factors when considering a contraceptive method?” and “Which factors most strongly influence adoption or rejection?” This required a system that could not only ingest and organize heterogeneous data but also enable executives to visualize and act upon insights derived from complex cross-document analyses. Continue reading
Unlocking Knowledge Intelligence from Unstructured Data
Introduction Organizations generate, source, and consume vast amounts of unstructured data every day, including emails, reports, research documents, technical documentation, marketing materials, learning content and customer interactions. However, this wealth of information often remains hidden and siloed, making it challenging … Continue reading
EK’s Joe Hilger, Lulit Tesfaye, Sara Nash, and Urmi Majumder to Speak at Data Summit 2025
Enterprise Knowledge’s Joe Hilger, Chief Operating Officer, and Sara Nash, Principal Consultant, will co-present a workshop, and Lulit Tesfaye, Partner and Vice President of Knowledge and Data Services, and Urmi Majumder, Principal Data Architect, will present a conference session at … Continue reading
Enterprise AI Architecture Series: How to Inject Business Context into Structured Data using a Semantic Layer (Part 3)
Introduction AI has attracted significant attention in recent years, prompting me to explore enterprise AI architectures through a multi-part blog series this year. Part 1 of this series introduced the key technical components required for implementing an enterprise AI architecture. … Continue reading
EK’s David Hughes Interviewed on The Data Exchange Podcast by Gradient Flow
David Hughes, Principal Data & AI Solution Architect at Enterprise Knowledge (EK), was recently interviewed by Ben Lorica on Gradient Flow’s The Data Exchange podcast to discuss “How BAML Transforms AI Development.” The Data Exchange is an independent podcast series focused … Continue reading
Leveraging Institutional Knowledge to Improve AI Success
In an age where organizations are seeking competitive advantages from new technologies, having high-quality knowledge readily available for use by both humans and AI solutions is an imperative. Organizations are making large investments in deploying AI. However, many are turning … Continue reading
Understanding the Role of Knowledge Intelligence in the CRISP-DM Framework: A Guide for Data Science Projects
In today’s rapidly advancing field of data science, where new technologies and methods continuously emerge, it’s essential to have a structured approach to navigate the complexities of data mining and analysis. The CRISP-DM framework–short for Cross-Industry Standard Process for Data … Continue reading
From Enterprise GenAI to Knowledge Intelligence: How to Take LLMs from Child’s Play to the Enterprise
In today’s world, it would almost be an understatement to say that every organization wants to utilize generative AI (GenAI) in some part of their business processes. However, key decision-makers are often unclear on what these technologies can do for … Continue reading
Data Governance for Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for injecting organizational knowledge into enterprise AI systems. By combining the capabilities of large language models (LLMs) with access to relevant, up-to-date organizational information, RAG enables AI solutions to deliver context-aware, accurate, … Continue reading