AI
How to Bring the AI Search Experience to Your Knowledge Ecosystem
Modern search engines must understand your content and users equally well. AI search is taking over to achieve this. AI search includes both semantic search and natural language responses. Discover how to bring the experience to your company's knowledge ecosystem.
Key Takeaways
- AI search adds two key features compared to keyword search: a semantic search engine for query intent understanding and natural-language answers to respond directly to the user’s question.
- AI search needs an AI search engine, an adapted UI, and AI-ready content. Teams can introduce AI search using a data lake with AI enterprise search, federated AI search, or an AI knowledge platform.
- To provide AI search within a particular knowledge domain, teams must anchor it to an authoritative knowledge source that can oversee, manage, and govern content specificities and requirements.
Knowledge teams have spent years refining how content is organized. They’ve invested in content strategies and keyword search engines so users can find, navigate, and understand knowledge faster. Yet, now, AI answer engines are disrupting search patterns as users are increasingly asking tools, like ChatGPT and Claude, questions about products and company content. Instead of scrolling through links, they ask in plain language and get immediate, tailored responses.
These new experiences are transforming public search trends. Users have fully adopted longer, conversational queries and have even started using this approach for keyword searches. However, most documentation search engines can’t provide a conversational experience and aren’t built to handle long queries with many words. Without an updated search solution, users will receive fewer relevant results, leading to extra effort and frustration.
Knowledge teams are now asking themselves: how do we bring the AI search experience into our company so users can find enterprise content just as seamlessly?
How Does AI Search Differentiate from Keyword Search?
Traditional search engines match the keywords in a query to the same terms in their index. They retrieve relevant documents, rank them, and present the results as clickable links. Users then open those links to read and continue researching. But as more people ask longer, natural-language questions, keyword-based systems have a harder time identifying what matters most. What companies need now is AI search engines.
Compared to keyword search, AI search adds two essentials: a semantic search engine that understands query meaning and natural-language answers that respond directly to the user’s question.
1. Semantic Search
Semantic search engines allow users to interact naturally in everyday language, understanding intent beyond the exact words used, in any language.
Semantic search uses Natural Language Processing techniques to analyze not just the literal words in a query, but also the underlying context and intent behind them. When indexing content, the engine converts text fragments into dense vectors or embeddings. These embeddings encode the meaning of the text, allowing the system to understand and compare both stored information and incoming queries.
The search engine compares query vector coordinates to those of your knowledge base content. Once it finds embeddings with similar coordinates to the search query, it retrieves the associated content behind these close embeddings as search results.
Example:
- Query: “How do I fix a leaky faucet?”
- Retrieves: Plumbing guides, tap repair tutorials, water fixture maintenance
2. A Dedicated Response
The next step is the response format which includes a UI adapted to search and new user experience possibilities. After a user enters a query, the AI search engine returns a clear, well-structured answer in natural language rather than a list of search results. The best AI search user interfaces also include direct links to source material, so users can explore topics in more detail and review the original content when needed. These citations build trust by showing that the response is grounded in reliable, verified knowledge.
AI search also uses interactive interfaces that create more engaging experiences, for example, through AI chatbots. After each answer, users can ask follow-up questions to clarify details or explore related topics. This back-and-forth conversation continues until they get the information they need.
Examples of conversational queries:
- Following up to ask for the prerequisite steps before an action.
- Requesting the complete list of tools needed for an intervention.
- Asking what user permissions are needed for the procedure and how to obtain them.
Comparing AI Search Tools and Methods
AI search requires an AI search engine, an adapted UI, and AI-ready content. You own the content and can prepare it for AI understanding with our eight best practices.
On the search architecture side, there are three options for how knowledge teams can introduce AI search into their knowledge base: use a data lake with AI enterprise search, federated AI search, or an AI knowledge platform. Before we compare options, let’s agree on some definitions of each:
- Data lake with AI enterprise search: An internally built repository where teams dump raw content and data from different sources. They then build their own search pipeline on top of the data lake.
- Federated AI search: A system that pulls content from various silos in real time. It runs a single query either in real time across multiple separate systems or across a central index.
- AI knowledge platform: A platform that unifies all information in a certain domain, no matter the format or source, into a single, intelligent, searchable knowledge hub with a native AI search engine.
Data Lake + AI Enterprise Search
Federated AI Search
AI Knowledge Platform
Advantages for AI search
This is the approach that IT teams traditionally take. They know and own the infrastructure.
Enables one single query across multiple existing repositories as they are. This is useful when systems can’t be centralized.
Content is unified, enriched, and structured to make it AI-ready while preserving context. Real-time indexing updates appear immediately for better results.
Disadvantages for AI search
High complexity and maintenance costs. No native governance or real-time data synchronization, which degrades search quality over time. Loss of context provided by the initial content source.
Querying multiple systems in real time may delay performance. Also, excess data sources and too many results make it hard for AI search engines to prioritize information.
Requires upfront content setup (connecting to sources, adding metadata, etc.). Note: Tools like Fluid Topics automate much of this via connectors.
Content governance & access rules
Governance must be custom-built and manually updated at regular intervals to avoid risk of data leakage. Access rights management is not native and must be engineered.
Content governance is possible, but access rules are hard to implement because they are managed for each independent content source. Content maintenance is also challenging.
Governance is native, and role-based access controls are centrally managed for all content. Embeddings and vector databases are processed internally to prevent content leakage.
User experience
Users may be frustrated with incomplete, outdated, or misleading results due to inconsistent or stale data and fragmented contexts.
Searches are redirected to original content sources, which creates an inconsistent user experience. Users also need access to each content source (e.g., CMS, repositories, etc.) to access the original content.
Users get reliable answers from a centralized, authoritative knowledge repository no matter where they search from. AI responses are personalized for user profiles (e.g., rights, role, preferences, and location).
Why is Domain Knowledge Important for AI Search?
Every organization has multiple arenas of knowledge (e.g., product, legal, finance, etc.), and each has content with domain-specific requirements and specificities. To deliver reliable AI search for a specific domain, you need to ground it in an authoritative knowledge source that can manage and govern those requirements. Domain-specific knowledge platforms are built for this purpose: they preserve context and apply the right rules and understanding to the information they manage.
Introducing AI Search with Fluid Topics for Product Knowledge
For product domain knowledge, Fluid Topics is the referential Product Knowledge Platform. When gathering content into its unified knowledge hub, it renders structured content, PDFs, Word documents, and multimedia content instantly searchable.
Fluid Topics’ AI search engine lets users communicate in the way that suits them best, thanks to its engine that combines keyword and semantic search capabilities. Powered by a state-of-the-art embeddings model and vector similarity matching, it consistently delivers precise, relevant, and dependable results every time.
Fluid Topics has enhanced the search experience. The portal improves the way users can navigate our content, allowing them to find solutions in a snap and improve service levels.
Aurélien Unfer
Project Manager, New Information and Communication Technologies at Liebherr Mining
Fluid Topics offers several native features that contribute to its advanced search experience:
- A centrally indexed product knowledge hub
- Hybrid search capabilities
- Integrations with content sources and applications
- Relevance tuning for business rules and user preferences
- A native AI Chatbot
- Dedicated documentation analytics
AI search also uses high context preservation to understand not only a user’s query but their intent and user profile. Combined, this information allows AI search engines to truly understand who each user is and what they need, leading to contextualized and personalized responses.
Conclusion
Modern search engines are only useful when they truly understand both the content and the user. AI-powered discovery — including both semantic search and generated responses in natural language — closes that gap. To maximize results, companies need to choose the best approach and solution for each knowledge domain.
For product knowledge searches, Fluid Topics remains the best choice. Its platform delivers personalized, relevant responses no matter how complex your product knowledge may be. With native AI search capabilities, it allows companies to seamlessly upgrade their engines in response to shifting user behavior.
See how Fluid Topics turns your content into discoverable knowledge
AI Search FAQs
Federated search offers three approaches: search-time merging, index-time merging, or hybrid merging.
- Search-time merging: An approach where the federated search engine queries each source live. The sources return ranked lists of search results and the federated system combines and ranks the lists into a single set of search results.
- Index-time merging: First, the company creates a central index built with data from all sources. Then, the federated search engine parses that constructed index when performing searches.
- Hybrid federated search: This approach combines methods from both index-time and search–time merging. Here, teams still create a central index, but they can also connect the engine to external sources whose data is not in the central index. The final results are aggregated and resorted as they are in search-time merging.
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