There is undeniable potential in generative AI and large language models, but these tools alone come with significant gaps and challenges. AI often needs additional technologies to create guardrails around data security and response accuracy.
Matthew Payne //
04 Nov 2024
The eGain Knowledge Hub is a rich, "whole-product" AI knowledge solution that has created transformational value at speed and scale for Global 1000 companies and government agencies alike.
Anand Subramaniam //
04 Nov 2024
AllegroGraph is designed to seamlessly integrate with LLMs, providing the most secure and scalable AI solution for enterprises. AllegroGraph offers a comprehensive solution platform including Large Language Models (LLMs), Vector generation and storage, Graph Neural Networks, Graph Virtualization, GraphQL, Apache Spark graph analytics, and Kafka streaming graph pipelines.
Jans Aasman //
04 Nov 2024
KnowledgeLake's solutions are designed to improve data accuracy, reduce operational costs, and enable faster decision-making, particularly within industries that handle large volumes of content, such as financial services, government, and healthcare.
04 Nov 2024
AI continues to disrupt the knowledge management space and experts in the field predict that it's a trend that still hasn't reached its full potential, yet. In 2025 there's more room for improvement.
Stephanie Simone //
06 Dec 2024
One thing is clear: The widespread adoption of GenAI will not lead to fewer knowledge jobs, but rather, it will pave the way for their growth and evolution.
Egor Kraev //
04 Nov 2024
What organizations that depend on dynamic documents really need are solutions that work seamlessly across all platforms. This way, organizations can eliminate compatibility issues and reduce most—if not all—of the costs that are associated with restricted formats.
Huy Tran //
04 Nov 2024
The need for comprehensive data management will always be important, and there are many other benefits of digital transformation, but CIOs don't need to delay GenAI projects until the completion of a giant data centralization effort. By adopting a more flexible approach that incorporates GenAI and next-generation BI tools, businesses can navigate the complexities of modern data ecosystems while driving innovation and maintaining a competitive edge in an AI-driven world.
Saurabh Abhyankar //
09 Sep 2024
We can talk about these relationships as links. They're not expressed in blue underlined text, and you can't click on them. But they are the relationships among words that matter in any particular circumstance. They are the relationships that give words meaning. And as in life, those meanings are multiple and contextual. Without those relationships, there is no language.
David Weinberger //
04 Nov 2024
Today's AI has many different flavors and architectures, along with massive amounts of memory and processing capacity. We could probably make better use of this computational power by looking at how we can improve the quality of our queries and, as a result, make better quality decisions.
Art Murray, D.Sc. //
04 Nov 2024
We are in the earliest stages of Agentic AI, and, much like the early days of RPA and GenAI, there's a lot of excitement but also a lot of uncertainty. While the potential benefits are enormous— streamlined operations, lower costs, fewer human errors—there are equally important concerns about job displacement, bias in AI decision making, and a lack of transparency in how these systems operate.
Alan Pelz-Sharpe //
04 Nov 2024
The citizen developer movement was heralded as a revolution. Like most revolutions, things have sometimes gone differently than planned. The logic is sound, empowering those who know the business best to build the tools and systems needed to do their job. Ah, if only things were that simple …
Alan Pelz-Sharpe //
09 Sep 2024