Leveraging AI Summaries to Fix ‘Broken’ SharePoint Search Results
When using the SharePoint search query tool to find files, incomplete or irrelevant results can be frustrating. Traditional SharePoint search uses keyword matching and that can overlook relevant documents that use synonymous terminology or different phrasing, often requiring users to perform multiple manual searches to find a single set of results. Standard LLM-based retrieval, such as Copilot, often provides results without making the reasoning behind them explicit. PointFire provides transparency by surfacing the specific reasoning behind each match. PointFire’s approach bridges the gap: it rewrites your query with synonyms and corrections, then adds concise summaries to each result. Each result shows exactly why it matches your query, letting you spot the right documents faster.
Keyword Matching vs Semantic Search
Keyword-based search primarily matches query terms to text and metadata, and may miss documents using different wording. SharePoint’s default search finds only documents containing the exact query terms (and specified metadata). This precision means it can miss relevant content when phrasing changes. In contrast, semantic search aims to understand meaning. For example, Microsoft’s Copilot builds a semantic index of concepts in your content, so related documents surface even if they use different words. However, this method can sometimes be opaque. It can miss exact matches and include irrelevant information, all without explanation and not necessarily reproducible. PointFire’s solution brings some of this power into SharePoint by enhancing queries with AI, without losing the accuracy of exact keyword filters.
Why Traditional Search Can Be Broken
Standard SharePoint search can sometimes return few results or results that are less relevant when queries contain typos, synonyms, or alternative phrasing. A query with a typo, an alternate word, or a missing stopword might return nothing. Even when results do appear, the default snippet may not highlight relevant lines, forcing you to open multiple files. For instance, a search for a budget report might pull up a different part of a file as its snippet. The experience feels fragmented and incomplete.
AI Summaries to the Rescue
PointFire adds AI into the process to fix these gaps. Key improvements include:
- Configurable relevance summaries: Each result has a short AI-generated summary showing why it’s relevant.
- Top sentences in context: The best matching sentences from each document are highlighted so you see answers without opening files.
- AI-enhanced queries: The tool suggests synonyms, alternate spellings, and applies Boolean logic (AND/OR) for you.
By “presenting a summary of in what way each document is relevant (or not), and which sentences are particularly pertinent,” the PointFire Search Summarizer makes results transparent and actionable.
PointFire Search Summarizer in Action
PointFire Search Summarizer is an add-on for SharePoint Online (it plugs into PnP Modern Search with no extra Copilot license). It processes data within your tenant, preserving permissions. As a smarter SharePoint search query tool, it sees the rewritten query and every matching document. In practice, the tool replaces vague snippets with useful, AI-generated ones. For example, as the product page explains, this AI-powered solution “optimizes search queries and generates query-based summaries for SharePoint search results,” helping you with high-precision results.
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While keyword-based indexing is precise, it creates a discovery gap when user terminology deviates from the indexed metadata. PointFire bridges this gap by aligning search intent with document content. By leveraging AI summaries and smarter queries, PointFire’s tool effectively transforms your SharePoint search results. It highlights relevant passages and corrects your query behind the scenes, so you spend less time filtering hits. This means faster answers and minimizing discovery gaps — exactly the fix for broken search.
