Unlock unparalleled search precision and relevance with hybrid search techniques. Discover how Atlas leverages this technology to transform information retrieval in the legal, professional services and other information-intensive sectors.
In information-intensive industries such as law or professional services, the ability to quickly and accurately retrieve relevant information is paramount. Traditional keyword-based search methods can be very effective but are at risk of falling short when it comes to end users executing effective queries. This is mainly due to traditional search tools not understanding the semantics or context behind the user’s query, leading to end-user annoyance, inefficiencies and ultimately to missed opportunities. Hybrid search is the technical ability that can address these challenges by combining the strengths of keyword and semantic search techniques, ensuring comprehensive and precise results.
For legal and professionals services work, accurate information retrieval is critical. Hybrid search enhances the relevance and precision of search results, enabling professionals to find pertinent information swiftly, even when dealing with complex search tasks.
Hybrid search is an advanced information retrieval strategy that combines two or more search techniques into a search algorithm. Typically, it combines traditional keyword search with semantic search, utilizing advanced machine learning techniques. This dual approach leverages the exact matching capabilities of keyword search and the contextual understanding of semantic search to provide more accurate and relevant results.
In the hybrid search process the system retrieves an initial set of candidate documents using keyword matching and semantic similarity, which are then re-ranked based on a combination of relevance scores from the relevant index. This ensures that users receive results that are both precise and contextually meaningful.
| Feature | Keyword Search | Semantic Search | Hybrid Search |
|---|---|---|---|
| Vector type | Sparse vectors | Dense vectors | Sparse and dense vectors |
| Search method | Exact keyword matching | Understanding context and meaning | Combines keyword matching and semantic understanding |
| Techniques used | TF-IDF, inverted index | Word embeddings (e.g., Word2vec), contextual embeddings (e.g., BERT, GPT) | All of the left combined |
| Relevance matching | Matches exact terms | Captures semantic similarity | Balances exact matches with semantic relevance |
| Query handling | Needs precise keywords | Understands natural language | Handles both keyword and natural language queries |
| User experience | Requires training on syntax | Natural, intuitive interaction | Delivers best UX with flexibility and accuracy |
| Scalability | Scales easily | Requires powerful compute (especially at scale) | Optimized for scale by combining fast filters with deep learning |
| System complexity | Low | High | Medium–high, depending on integration strategy |
| ML integration | Minimal | Requires pre-trained models | Often includes ranking/re-ranking models using ML |
Microsoft 365 native search is powerful within its own ecosystem, but in a complex enterprise = where data spans across multiple platforms, clouds, and repositories - it often lacks the flexibility, depth, and contextual intelligence needed for comprehensive enterprise search. Organizations increasingly require unified, cross-platform search experiences that go beyond Microsoft 365’s boundaries to drive productivity, ensure compliance, and enable efficient knowledge discovery.
Microsoft’s Copilot relies heavily on the Microsoft Graph and is directly impacted by the above. Additionally, the Semantic Index for Copilot lacks a lot of detail as it stores document summaries not the entire document content.
Key advantages of hybrid search
Both keyword search and semantic search have unique strengths. Keyword search uses a ranking algorithm and specific terms to determine how relevant a document is to a search query. Semantic search takes the search query and considers the context.
Hybrid search therefore offers several key advantages over traditional search methods. Firstly, it enhances relevance and precision by combining the strengths of keyword and semantic search. This means users receive results that match exact keywords and those that share the same meaning.
Secondly, hybrid search provides better query handling. It can process both simple, precise keyword queries and complex, natural language queries, making it versatile for various user needs. The ability to understand the context and intent behind queries significantly improves the user experience.
Moreover, hybrid search ensures comprehensive results. It minimizes the chances of missing relevant documents, whether they match the exact keywords or are semantically related to the query. This reduces the need for multiple search attempts, saving time and effort.
Several platforms are leading the way in hybrid search foundational technology.
For example, Microsoft’s Azure AI Search combines traditional keyword search with newer, AI-powered techniques to find the most relevant results quickly and accurately, within a fully managed cloud service.
Couchbase makes it easy to search using different methods like text, location, and filters, all in one place, making searches faster and simpler.
Elasticsearch is another strong option that blends keyword and AI-based search. It also includes tools for visualizing data and improving search results using machine learning.
Amazon Kendra uses artificial intelligence to understand what users are really looking for, combining different search methods to deliver better answers.
These search technologies show the increased focus on search systems that can handle both traditional and AI-powered approaches, to cater for increased demand from businesses that need to deal with more complex and varied data.
Atlas delivers a next-generation hybrid search experience that makes searching across multiple systems simple, secure, and contextually rich.
Everything is searchable through a single interface, removing the need to jump between platforms or re-train users. Atlas' search interface surfaces results from Microsoft 365 and third-party platforms’ including for example:
In fact, over hundred unique data sources can be supported, in addition to support for custom API integration.
Unlike traditional enterprise search tools, Atlas uses auto-classification and metadata enrichment to tag content across systems. This ensures results are:
Hybrid search isn’t just for humans, it’s for AI, too. Atlas feeds AI with the right, governed knowledge collections using Retrieval-Augmented Generation (RAG) principles. This means:
Atlas doesn’t just improve search - it transforms search into a knowledge productivity engine.