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Stop manually indexing knowledge: Automate the process and ensure your knowledge base is always current.
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Resolve issues faster: Empower agents and employees with instant, accurate answers from across your company’s knowledge.
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Boost self-service success: Improve ticket deflection rates by providing relevant answers directly in the search bar.
Sound good? Let’s explore how ServiceNow AI Search indexing works and how you can use a tool like eesel AI to make it even more powerful.
How ServiceNow AI Search indexing works
ServiceNow’s AI Search is a significant upgrade from its predecessor, Zing. It uses machine learning to understand the intent and context behind a user's query, delivering more relevant results. The magic behind this is its indexing process.
Here’s a simplified breakdown:
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Content sources: AI Search first identifies the sources of information it needs to index. This primarily includes tables within the ServiceNow platform, like Knowledge Base articles (kb_knowledge), Catalog Items (sc_cat_item), and user records.
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Indexer: An indexer processes these sources, extracting text and metadata. It cleans and normalizes the data, preparing it for the search engine.
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Index: The processed data is stored in a highly organized structure called an index. Think of it like the index at the back of a book, but far more sophisticated. It maps words and concepts to the documents where they appear.
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Querying: When a user types a query, AI Search doesn't just look for keyword matches. It uses Natural Language Understanding (NLU) to grasp the user's intent and searches the index for conceptually related information, not just identical words.
This process ensures that when someone searches for "laptop issue," they find articles titled "Fixing computer problems," even if the word "laptop" isn't explicitly mentioned.
