Content Science & AI
Managing massive amounts of content can be tough. Moreover, discovery and search pose new challenges given the increasing information overload. AI advancements like Large Language Models (LLMs) as implemented by ChatGPT offer new ways to extract and maximize content value in an innovative way.
Customers we advised on Content Science & AI
Unsure about LLMs and GPTs? We hear you.
70% of all content in your organization is unstructured. How can you make the most of it? We know managing unstructured content can be overwhelming. Without a clear approach to content governance, you face efficiency issues that turn processes into time-consuming and costly tasks. Moreover, your customers struggle to find the right information on your digital channels.
You might think a Large Language Model (LLM) is the fix, right? But LLMs often lack domain knowledge and can deliver inaccurate information, falling short of providing a truly qualitative experience. That’s where Content Science comes in. It is the practice of enriching and optimizing your unstructured content, ensuring your customers get accurate and up-to-date information through AI-enabled capabilities like smart search or chatbots.
Benefits of applying Content Science in your organization
Techniques like Retrieval Augmented Generation (RAG) are not a magic bullet. Without a centralized content governance process and additional content cleanup, enrichment or classification, the output of Generative AI models often falls short to enable high-end production use cases.
Content Science is the answer to this challenge. It focuses on harnessing your unstructured content to its fullest potential by optimizing its lifecycle for AI use cases, ensuring your content is managed effectively and used to its greatest advantage.
Prepare your content for AI use cases
Introduce a Content Pipeline to augment your content before using it in an AI context.
Embed up-to-date domain knowledge
Provide LLMs with extra context by grounding them in your organizational knowledge.
Enforce more accurate LLM outputs
Reduce hallucinations by limiting LLM scope to your own private set of content and introducing proper guardrails.
Discover Content Pipelines
Content teams often struggle with content that is siloed, unstructured, and scattered across various sources. A Content Pipeline allows you to apply the discipline of Content Science by transforming raw content into structured, annotated, and accessible information, significantly enhancing the performance of applied AI models.
A Content Pipeline complements your RAG setup by collecting and connecting diverse data sources. It begins by cleaning and filtering the content to remove noise and irrelevant information. Then, classification and annotation processes organize the content into meaningful categories and add valuable metadata. Constructing a knowledge graph embeds these structured insights, creating rich, interconnected data representations.
Typical use cases for Content Pipelines
Content Pipelines enables qualitative use cases across various business scenarios, such as:
- Customer Support Automation: Improve AI chatbots and virtual assistants with accurate, up-to-date information for better customer service.
- Knowledge Management: Organize and structure large volumes of internal documents, making it easier for employees to find and use information.
- Content Personalization: Deliver tailored content to users based on their preferences and behavior, boosting engagement and conversion rates.
- Regulatory Compliance: Ensure all content meets industry regulations by systematically categorizing and tagging it for easy retrieval and audits.
- Product Information Management: Structure product data for e-commerce platforms, ensuring consistency and accuracy across all channels.
At Amexio Fuse, we know that defining initial business cases can be tough. That’s why we offer support in this crucial first step, helping you package a Content Pipeline as part of a larger Content Science strategy. While we focus on helping you build a comprehensive Content Science strategy, we can also help to define a first concrete pilot use-case that immediately demonstrates business value.
Ready to set up your Content Pipeline?
Let's meet up and discuss the first steps on how to kickstart a Content Science initiative in your organization.
Questions we usually get
Is Content Science related to Data Science?
Content Science can indeed be seen as the counterpart to Data Science: Content Science focuses on the systematic organization, classification, and management of unstructured content to enforce higher-quality results in content retrieval scenarios using AI models. Data Science, on the other hand, involves the extraction of insights from structured data through statistical analysis, machine learning, and other advanced analytical techniques. It is typically used for audience segmentation, content personalization and recommendations.
What are the first steps to take?
We are still in the early stages of the widespread AI adoption across industries. Because of that, organizations currently find themselves in an exploratory context, where the feasibility of AI initiatives needs to be further investigated. As a first step, we therefore propose adopting a "lean" approach, focusing on specific AI use cases fitting your business domain.
In that regard, we propose scheduling two initial workshops:
1. General introduction to AI and the most recent developments in the field. This workshop is mainly intended as inspiration and to gain insight into potential use cases as well as the risks of the technology.
2. Exploration of your business domain and definition of a pilot AI use case for your organization. This workshop has 4 distinct phases:
- Isolate a representative target audience segment and define its customer journey
- Decompose this customer journey into the different touch points between the customer and your organization
- For every touch points, investigate potential enhancements enabled by AI
- Using an impact/effort matrix, select which use case is the most potential as a pilot