Exact Match Search v/s AI Search - Key Fundamentals when Integrating AI in Your Business Apps with Softools DataLens
Most of us have gotten used to opening up GPT or Gemini to get work done - “search” for anything based on the prompts and keywords. A decade ago, this could be said for Google Search. The difference? Keyword search produces results containing exactly matched keywords of the user’s query. AI search gives you results based on a deeper understanding of the intention and context of the user’s search query. Let’s take a practical look at different search types and how they can be integrated into your apps for more efficiency and smoother user experience.
Right now, as more AI services are becoming more available and complex, effective context-based search and its emerging use cases are crucial for business apps regardless the size of databases they work on. When app builders use Softools, they have the capability to use filters and quick search for structured databases - where exact match search can produce the best results - since every datapoint is defined. For example Project Name, Supplier Number, et al.
But What happens in scenarios where a process produces unstructured but elaborate data in an app, and useful insights and reports need to be extracted? for example, in use cases like a Safety Audit app which holds data of hazards in a manufacturing facility, and the quality manager wants description of a hazard fast and with insights on prevention measures. In such cases, AI-search can produce contextualised results, with narratives and more in-depth information and analysis of hazards - and this is now possible for your apps with Softools DataLens.
Before we get to an actionable checklist you need when you decide to activate DataLens for your apps, here are a few fundamentals on types of search and how they work.
Understanding Keyword Search
Traditionally, search functionality relies on keyword matching. When a user enters a phrase, the search engine scans through the database to find exact or closely related keyword matches. This method is fast and efficient when searching structured data where exact matches are crucial, such as locating a specific file name or a known customer ID.
However, keyword searches may fall short when dealing with vast datasets where context matters. A keyword-based search engine cannot infer meaning beyond direct matches, limiting the accuracy of results when searching for nuanced or context-dependent information.
AI Search: Beyond Keywords to Contextual Understanding
AI-powered search, or semantic search, integrates Natural Language Processing (NLP) and machine learning to understand the intent behind a search query. Instead of merely finding text matches, AI search interprets the meaning of words in relation to their context, making it highly effective when searching in large, unstructured datasets.
For example, if a user searches for "compliance audit performance", a keyword search may return any document containing those exact terms. AI search, however, understands that the user is likely looking for reports on audit results and performance metrics, even if those exact words aren’t used. AI search also considers synonyms and related terms, so a query for "risk assessment procedures" may also surface records related to "safety evaluations" or "compliance checks."
With AI powering search, the search engine is able to process your query in a way that is similar to natural language and you'll get faster, better results tailored to what really matters to your business process and the end user.
Given the nature of NLP and models that AI-powered search operates on, it is not so effective when you’re looking for things like exact ticket numbers in your app’s database.
These days, many modern search engines are using a hybrid of semantic and keyword searches.
Hybrid Search - A New Level of Effective Search
Hybrid search is a technique that combines different search methods to improve the accuracy and relevance of results. It blends keyword-based search, which relies on exact word matches, with AI-driven search that understands context and meaning. This approach ensures users get both precise and context-aware results.
Hybrid search works by using two types of data structures: sparse vectors and dense vectors. Sparse vectors are commonly used in traditional keyword searches, like those powered by ranking algorithms such as BM25, and are effective at finding exact matches. Dense vectors, on the other hand, are used in AI-based search to understand the relationships between words, capturing deeper meanings and context.
By combining these methods, hybrid search delivers a more refined and efficient search experience, making it particularly useful for handling both structured and unstructured data.
How Softools DataLens Makes Your App’s Search Experience Seamless
Softools AI Data Lens allows businesses to integrate AI-powered search into their applications, optimizing search functionality across various industries. Whether searching for compliance documentation, customer records, or project reports, DataLens enables information retrieval and search to give relevant results from your databases accurately.
Moreover, Softools’ DataLens integrates seamlessly into complex workflows, allowing businesses to:
Retrieve context-aware search results from large datasets
Improve search accuracy when dealing with industry-specific terminologies
Automate search-based workflows, reducing manual effort in data retrieval
Now when you run a search using DataLens in your app, you can seamlessly toggle between the exact matched keyword results and the results from the AI-powered search, without having to re-run the search again.
You can also choose to keep AI-search as the default, or switch back to Keyword search when you need. Going forward, as the Softools NoCode platform evolves, app builders will be able to choose which search to prioritise in their app’s UX, while keeping other types as secondary search to ensure efficiency and smarter workflows.
Actionable approaches to decide which type of Search best suits your business scenarios
If you’re building your apps on Softools to solve for scenarios illustrated earlier and want to integrate Search capabilities, you need:
Clarity on the type of database - is it structured or unstructured?
Detailed understanding of what the app’s end user will search. Does their work involve daily searching for information like project numbers or ticket numbers? Or will they be looking for context-based information like “Assembly line hazards and how to resolve them”
What does your user journey look like when you’ve deployed your app. Are there integrations or further already-set workflows that depend on their search results, like Supplier Details or periodic compliance renewals.
Look out for more content on AI-based workflows and Softools DataLens for more updates, while we continue to improve and evolve the platform to make your app building faster and better.