Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
Key Takeaways Poor data quality is silently killing customer support AI initiatives, regardless of how much you spend on AI models or vendors Bad data poisons AI training, routing, deflection, and agent assist, making ROI impossible to achieve The solution is proper data inventory,...
RAG as a service provides Retrieval-Augmented Generation (RAG) technology as a managed solution, combining information retrieval and generative AI models to deliver accurate and relevant outputs. This service offers significant benefits such as improved response accuracy and timely access to...
Vector Solutions is a platform for enhancing training and development in organizations, education, and professional settings. It integrates tools for incident management, reporting, and improving learning outcomes. With Vector Solution, you can boost efficiency and safety while effectively...
Intro: The Rise of AI and Automation The rapid advancement of artificial intelligence (AI) technologies, particularly in the realm of generative AI, is ushering in a transformative era across various industries. As enterprises embrace these cutting-edge technologies and automate an increasing...
Bridging the Gap: Unlocking Business Value from Unstructured Data In today’s data-driven landscape, organizations grapple with a significant challenge: harnessing the immense potential locked within their unstructured data. While raw AI capabilities have advanced rapidly, translating these...
The Rush to Deploy Generative AI Nowadays, organizations across industries are scrambling to deploy generative AI. While some have already implemented generative AI projects into production at a small scale, many more are still in the proof-of-concept phase, testing out different use cases. A...
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating an external retrieval system. This allows the AI to ground responses in authoritative, real-world data, which mitigates hallucinations and extends an LLM’s knowledge base beyond its pre-training data. ...
The adage “Garbage In, Garbage Out” (GIGO) holds a pivotal truth throughout all of computer science, but especially for data analytics and artificial intelligence. This principle underscores the fundamental idea that the quality of the output is linked to the quality of the input. As...
Making sure your data is ready for AI agents is critical for the success of your projects. As an AI leader or tech strategist, you understand the importance of data accuracy and integrity in AI models. Well-prepared data leads to more reliable outcomes, higher customer satisfaction, and better...
The age of artificial intelligence in the enterprise is no longer a distant future—it’s a disruptive present. While many companies have dipped their toes into AI through isolated pilots and flashy demos, the time has come to ask the hard question: Can our AI strategy scale sustainably? That’s the...
As enterprises race to integrate AI agents into operations, many are discovering a hard truth: it’s not the models holding them back—it’s the mess. Specifically, the mess of unstructured data. While much of the excitement in enterprise AI focuses on models, tools, and interfaces, one fundamental...
The future of AI in the enterprise won’t be built on monolithic models—it will be orchestrated by systems of specialized agents working together like a digital workforce. That’s the central thesis behind the rapid rise of multi-agent systems, and it was a defining theme of the Shelf webinar, “AI...