Let’s say you’re a project manager trying to track down last year’s strategies, performance insights, or sales data. You pull a sea of company information, only to realize the traditional search system isn’t helping—fetching irrelevant results and leaving you frustrated.
The root of the problem? Unstructured data sources, overwhelming volumes of information, disconnected systems, and poor integration. These issues create chaos—wasting time, causing missed deadlines, duplicating efforts, and leading to unnecessary costs.
This is where cognitive search changes the game. It transforms how you access and consume organizational knowledge, making it faster and more efficient.
In this blog post, we’ll explore how cognitive search can help you uncover accurate insights, optimize knowledge discovery, and enhance decision-making like never before.
Let’s go!
What is Cognitive Search and How Does it Work?
⏰60-Second Summary
- Cognitive search uses AI technologies like machine learning and NLP to process and retrieve information, offering context-aware results
- It differs from traditional search by understanding query meanings, learning from user interactions, and accessing multiple data sources to provide accurate and personalized results
- Cognitive search uses techniques like data ingestion, indexing, enrichment, query understanding, and continuous learning to improve results
- From eCommerce and customer support to healthcare, enterprise knowledge management, and finance, cognitive search finds a host of useful applications across industry verticals
- Challenges in implementing cognitive search include lack of expertise, high costs, inconsistent data quality, and privacy concerns
- provides scalable and sustainable solutions through Connected Search, AI, Docs, and Whiteboards
- Future trends involve multimodal searches and deeper AI integrations for more intuitive and tailored search experiences
What is Cognitive Search?
Cognitive search is an advanced information retrieval method that uses sophisticated artificial intelligence (AI) technologies such as machine learning (ML), deep learning, and natural language processing (NLP) algorithms to process, organize, and retrieve information.
These AI-based search engines self-learn and refine themselves while processing new data to produce intuitive and context-aware results for users.
Here’s how it’s different from the usual enterprise search systems:
🔍How it searches: Traditional information retrieval matches words in your search query to words in documents, often missing context. Cognitive search, however, understands the meaning behind your query, delivering more accurate and relevant results.
📈How it improves: Regular search systems don’t adapt based on user behavior. Cognitive search, on the other hand, learns from the query responses you engage with, refining its results over time to become more helpful.
👀Where it looks: Traditional enterprise search typically pulls data from a single, structured source like a database. Cognitive search, in contrast, taps into multiple sources, including structured and unstructured data, to give you a broader search capability.
🗃️How it handles data: As data grows, traditional enterprise search systems can slow down or struggle. Cognitive search is designed to handle vast amounts of complex data while maintaining fast and precise results.
Now, let’s look at how traditional search and cognitive search stack up against each other in more granularity 👇
| Criteria | Traditional search | Cognitive search |
| Retrieval mechanism | Relies heavily on keyword matching | Uses ML, NLP, and deep learning algorithms to understand the query and extract context-aware insights |
| Data sources | Single source, structured data | Multiple sources, unstructured data |
| User input | Demands a full query with specific keywords | Supports natural language queries |
| Improvement | Static, provides only basic information | Dynamic, learning relationships and user history to generate meaningful, valuable insights |
| Scalability | Struggles with large data sources | Efficiently handles vast amounts of data |
Benefits of Cognitive Search
If you’re thinking about adding cognitive search to your knowledge base, here’s why it could be a big win for your enterprise:
- Better accuracy: Cognitive search digs into a query through NLP algorithms, understanding the context, sentiment, and intent behind it. This leads to accurate search results and rich insights that solve an exact problem
- Personalized results: Cognitive search uses ML to learn from your past searches and adapt over time, making each search feel like receiving personalized recommendations from someone who understands you
- Handling unstructured data: A lot of business data is unstructured information, like emails, images, audio, and video, which can be tough to make sense of. But cognitive search dives into various types of raw content to pull out useful insights for your team
- Supports multiple languages and formats: Whether your query is in English, Spanish, or any other language, cognitive search can handle it. And it doesn’t matter if you’re asking for text, images, or videos—it retrieves what you need across different languages and formats
- Scalable and flexible: As your data volume increases or your business processes change, cognitive search adapts seamlessly, maintaining accuracy and performance while scaling effortlessly
📌Fun Fact: Cognitive search mimics the way our brains process information. Using neural networks, it clusters and links data the same way neurons connect and fire, creating a digital ‘thought process.’
How Cognitive Search Works?
Cognitive search incorporates search queries, relevant documents, and indexing engines in its flow.
Here’s how ⬇️
1. Data ingestion
- Gathers information from diverse sources such as databases, documents, emails, multimedia, and web content
- Converts raw data from various formats (e.g., PDFs, audio, or videos) into a unified structure, preparing it for full-text search
2. Data indexing
- Uses indexing engines to organize ingested data into a searchable structure
- Attaches metadata (e.g., file type, date, author) to enrich the dataset, ensuring relevant documents can be retrieved accurately when users enter search queries
3. Data enrichment
Applies AI technologies to enhance raw data:
- NLP understands the context, intent, and sentiment of the data
- Semantic analysis matches the meaning behind terms, ensuring the search engine retrieves results based on the query’s intent, not just literal keyword matches
📌 For example, if a user searches for “how to fix a broken phone screen,” a cognitive search engine using semantic analysis might return results like:
“Guide to repairing cracked smartphone screens.”
“DIY phone screen replacement tips.”
“Best repair kits for mobile phone screens.”
Instead of focusing solely on literal keywords like “broken” or “fix,” the system understands the intent behind the query—seeking solutions for phone screen repair. This ensures more relevant and helpful results, even if the exact words don’t match.
- Speech-to-text conversion converts audio into text for inclusion in full-text search capabilities
- Entity recognition identifies and tags critical entities like people, places, or organizations
📌 Example: Google’s Knowledge Panel, which returns key information about Central Park (or other famous places and people) when you search about it
- Visual data processing analyzes images or videos to make them accessible for search queries
4. Query understanding and relevance ranking
- Processes search queries by understanding user intent through NLP, accounting for synonyms, typos, and ambiguities
- Uses previous interactions and user-specific contexts to personalize the most relevant documents
- Assigns relevance scores to results based on metadata, query intent, and context
5. Result presentation
- Displays results with filters, categories, or highlighted excerpts, helping users navigate large datasets
- Provides a summary of information or links to relevant documents for immediate use
6. Continuous learning
- Tracks user behavior (e.g., clicks, time spent on results) to refine the effectiveness of the search engine over time
- Continuously update algorithms to adapt to user search habits
Example workflow 👇
- A user enters a search query, ‘latest marketing trends this year’
- The system interprets the query, applying NLP and semantic analysis to understand the intent
- Indexing engines scan the dataset to locate relevant documents enriched with contextual metadata
- Results are ranked for relevance and displayed with clear highlights and insights
Key Features of a Cognitive Search Platform
When it comes to cognitive search platforms, there are a few standout features that make them powerful:
- Conversational search interface: Ask questions like you would in a conversation with your friend. With a cognitive search engine, you can query for specifics like ‘Show me all data related to client orders in Q3 2024,’ and watch it use advanced search capabilities to deliver relevant results
- Federated search: Search across multiple systems and databases, even if they’re disconnected. Whether your files are scattered across cloud storage or local servers, federated search supports finding and extracting text from all sources
- Advanced search options: When you need detailed results, cognitive search solutions offer faceted navigation options like boolean search (AND, OR, NOT), keyword variations, date ranges, and more. You can also filter by document type, author, or location to find exactly what you’re looking for
- Personalization: With ML and computer vision, the platform adapts to your habits and preferences. By understanding your workflows, the search engine surfaces the most relevant information automatically
- Security and privacy: Cognitive search supports robust security measures like encryption, access controls, and secure connections (e.g., HTTPS), ensuring your information stays protected from threats
👀Did You Know?
Back in the mid-20th century, cognitive science took center stage, thanks to Noam Chomsky’s Syntactic Structures in 1957. This era, often called the ‘cognitive revolution,’ shifted the spotlight to understanding how our minds work, paving the way for technologies that think and process information like humans do.
Applications of Cognitive Search
Cognitive search is showing up in all kinds of industries, making life a lot easier in ways you might not even notice!
Here’s a sneak peek:
eCommerce
In eCommerce, cognitive search looks at your age, gender, location, past purchases, and even your budget to suggest products you’ll love. Plus, if you make a typo or use a different word for something, it still gets what you mean and finds exactly what you’re looking for.
📋Example: Ever wondered how Amazon seems to know exactly what products to recommend based on your shopping habits? That’s cognitive search at work.
Customer support
Cognitive search makes customer support all the more efficient. It powers chatbots to help users find solutions in self-service portals and automatically routes support tickets to the right teams. This means quicker resolutions and happier customers.
📋Example: A software company uses cognitive search in its help desk so customers can type questions like, ‘How do I fix this error?’ and get instant answers.
Healthcare
Cognitive search can quickly retrieve patient records and analyze complex text documents. It can also analyze massive medical datasets, such as genetic information or clinical studies, to predict disease patterns and epidemics and even create personalized treatments. In all honesty, this is a game-changer for drug development.
📋Example: Doctors can input ‘Patient [X] blood test and CT scan results’ to get a line-by-line summary of the records they need.
Knowledge management in enterprises
For enterprises, cognitive search helps employees quickly find the right information, no matter where it’s stored. Whether you’re looking for company policies, project details, or industry insights, cognitive search pulls everything together from different departments in one place.
📋Example: Especially useful for employees, they can use AI in the workplace and find answers to common questions like leave policies or training resources within seconds.
Finance
Cognitive search helps finance professionals spot fraud faster, analyze customer behavior, and organize data for smarter investment decisions. It’s like having a financial assistant who never misses a detail.
📋Example: A bank can use cognitive search to spot unusual transaction patterns for fraud detection and quickly analyze customer histories to offer personalized financial products.
Implementing Cognitive Search with
Let’s face it—scattered information is a nightmare. Jumping between apps, hunting for files, and trying to connect the dots between fragmented data just drains productivity. It’s frustrating, slows down projects, and makes collaboration harder than it needs to be.
📮 Insight: Knowledge workers send an average of 25 messages daily searching for information.
According to research by , while an average of 25 messages a day may not seem significant, nearly 1 in 5 employees send over 50 messages to their colleagues. This back-and-forth isn’t just distracting. It can also be severely unproductive—for both the sender and the recipient.
To prevent unnecessary platform hopping while searching for information, you can centralize context in , the everything app for work. It brings your tasks, files, and conversations together for maximum focus and productivity.
Enter , the all-in-one platform that’s already helping teams streamline workflows, boost productivity, and keep communication smooth.
But wait, there’s more!
Brain and ’s AI-powered Connected Search are advanced features that extend their cognitive search principles to solve the biggest challenge in workspaces—siloed information. These sophisticated search capabilities allow teams to find documents, data, and tasks while managing projects efficiently.
Here’s how Brain’s connected AI revolutionizes the way you work:
Create an effective knowledge base
Creating a centralized knowledge base has never been easier than with Docs. Whether you’re documenting processes or compiling resources, Docs organize everything in one place.
With Docs, you can:
- Draft fully customizable documents for team knowledge, project guides, FAQs, onboarding materials, and more
- Link Docs directly to tasks, projects, or dashboards so everyone has context at a glance
- Work together in real-time with inline comments, editing, and sharing options—no need for external tools
- Use Brain to instantly find the right Doc or section, even within massive piles of enterprise information
Once your knowledge base is up and running with Docs, the next step is to use Whiteboards to implement that knowledge.
Here’s what we mean 👇
- Map out concepts, workflows, or processes with customizable shapes, connectors, and free-form drawing tools
- Whether brainstorming or planning, your team can add thoughts, make edits, and see updates instantly—no matter where they are
- Unlike traditional whiteboards, everything is saved, searchable, and linked, so nothing gets lost
- Transform whiteboard elements into Tasks or link them directly to your projects and Docs for seamless execution
And the best part? You can bring your whole team to the whiteboard and create a collaborative workspace to go from idle concepts to ultimate execution.
💡Pro Tip: Building a knowledge base from scratch sounds overwhelming, right?
Refer to a knowledge management system or, even better, use the Knowledge Base Template to create and organize a digital library effortlessly. With sections for FAQs, knowledge articles, and resources, you can streamline the process and get started in no time!
Get information and insights at your fingertips
We’ve all been there—frustrated and stuck, searching endlessly for that one piece of information you know is somewhere in your workstation. Switching between apps, digging through mountains of data, and finally finding what you need can eat up precious time you could’ve spent being productive.
👀Did you know?
According to Gartner’s recent survey, 47% of digital workers struggle to find information or data needed to effectively perform their jobs.
Brain’s AI-powered Knowledge Manager makes this hassle a thing of the past. Just ask Brain a question, and it will sift through your Docs, comments, chats, emails, and wikis to deliver precise answers instantly. No more wasted time—just the information you need, exactly when you need it.
More than us, it’s the decision-makers who trust to their core. ⬇️
Find any file, instantly
Ever had to recreate a file from scratch because you couldn’t find it in a sea of documents right when your team needed it most? Not anymore!
With ’s Connected Search, you can easily search PDFs, documents, and other file types. The best part? You don’t need separate document management software. extends its search to your entire connected workspace, letting you find documents across apps—all from one interface.
In fact, takes productivity and search to the next level by aligning perfectly with cognitive search principles. Here’s how:
- lets you access all your enterprise information in one place, saving time and sanity
- Powered by ML and NLP, delivers context-aware search results, helping you find the most relevant answers
- centralizes resources, ensuring your entire team has quick access to the information they need
- integrates seamlessly with tools like Google Drive, SharePoint, Figma, OneDrive, DropBox, and Slack
- has enterprise-grade security features to keep your information safe and compliant
The result?
- Time saved in searching for information
- Improved employee satisfaction
- Faster and data-driven decisions
Challenges and Considerations
Even though cognitive search can do wonders, you still need to overcome some common AI challenges to really make the most of it:
- Expertise and costs: Building, setting up, and keeping a cognitive search system running takes a lot of know-how, time, and money. And let’s face it, finding the right talent for the job can be tough
✅Solution: Instead of building it all from scratch, why not get the right tool? lets you skip the high upfront costs, save on ongoing maintenance, and avoid the need for specialized expertise. That way, you can focus on what you do best and still unlock the full potential of your data!
- Datasets: For machine learning (whether supervised or unsupervised), the system needs a lot of data to train on. But getting access to top-quality datasets and giving the system enough practice can be tricky
✅Solution: Standardizing the data can enhance the quality of datasets, resulting in better training. This includes techniques like text cleaning (removing special characters, stop words, and normalizing text), tokenization and lemmatization, and feature engineering (e.g., creating meaningful features from raw text or metadata).
- Privacy and security: Cognitive search systems analyze tons of user data, which brings up important questions about security and privacy. Users want to know tif heir data is only being used for search and not for anything else
✅Solution: Make sure you have solid security measures in place with encryption, access controls, and strong passwords. Plus, be upfront with your users about how their data is being handled. This transparency builds trust.
Future Trends in Cognitive Search
The future of cognitive search is moving beyond typing with multimodal searches. Imagine finding what you need by using images, voice, or even videos instead of text. Tools like Alexa, Google Assistant, and Google Lens are already showing how intuitive searches can become, letting you ask, show, or play something to get results.
Cognitive search is also becoming more imaginative with deeper AI integrations, primarily through generative AI. This technology enhances search by breaking down complex content, merging information from multiple sources, and eliminating irrelevant details. Google’s AI Overviews are a case in point.
The result? Faster, more tailored answers that make finding information (and answers) effortless.
Access Organizational Knowledge From a Unified Source with
Cognitive search creates a unified search experience. It’s an invaluable tool for teams to find relevant information and prevent time wastage. When searching for information becomes easy, your team can focus on executing the task, moving on to the next one, and improving their productivity.
With a project management tool that has integrated cognitive search functionalities like , locating information and streamlining knowledge discovery is easier than ever!
unifies workflows, connects with your favorite apps, and embeds search where work happens to break you free from the cycle of searching for insights and losing productivity.
Get for free today and say goodbye to the massive costs of disconnected knowledge. 👋
Everything you need to stay organized and get work done.
