From Keywords to Cognition: A Practical Guide to Search in Squirro

Have you ever stared at a search bar, knowing the information you need is in the system, but found yourself completely unable to find it? You search for "reducing power bills" and miss the key report titled "Energy Savings Initiatives." This disconnect between what you mean and what you type is the central challenge of modern enterprise search.

In today's data-rich environment, a simple search box doesn't cut it. You need intelligent, insightful, and context-aware retrieval. And with the rise of AI, this is no longer just about helping humans find files; it's the critical foundation for powering trustworthy, high-performance enterprise AI. This is where Squirro shines.

This article will demystify the four core concepts you'll encounter in our product documentation: Keyword, Semantic, Hybrid, and Cognitive Search. We'll break down what each one does, when to use it, and how they work together to deliver unparalleled insights.

The Search Landscape: An At-a-Glance View

Before we dive deep, let's get a high-level comparison. Each search type serves a different purpose, building upon the last in capability and intelligence.

The Four Pillars of Squirro Search

Let's break down each concept.

The Foundation: Keyword Search

This is the search we've grown used to over the past decades. Keyword search is the foundational method that matches the literal terms you type against the indexed content. It's fast, precise, and predictable.

When you type solar energy into the search bar, the system retrieves documents containing those exact terms or their simple variants (like stemming "energy" to "energies"). The results are then ranked based on factors like how often those keywords appear. This method is exceptionally good for finding specific documents, part numbers, or exact technical jargon where precision is paramount.

• Simple, fast, and easy to understand.
• Excellent for finding specific documents or exact technical terms.

• It's brittle. It misses relevant results if different wording is used (e.g., it won't find "photovoltaic power").
• It doesn't understand your intent or the context of the document.

Because of this, it's best to rely on keyword-only search for well-defined, compliance-heavy, or technical queries where you must find an exact match.

The Leap to Meaning: Semantic Search

This is where true intelligence begins. Semantic search uses Natural Language Processing (NLP) and machine learning to understand the intent and context of your query, not just the literal words.

It works by transforming both your query and your documents into numerical representations called "vector embeddings." Instead of matching words, it finds documents whose meaning is closest to the meaning of your query.

For example, if you ask "How can we reduce electricity costs?" the system understands the concept of saving money on power. It will return documents discussing "energy savings," "lowering power bills," or "cost reduction strategies," even if they never use the words "reduce" or "electricity."

• Finds contextually relevant results, dramatically increasing recall.
• Supports natural language questions and exploratory search.
• Can be multilingual, finding a concept regardless of the language it's written in.

• More computationally intensive (slower and requires more storage for vectors).
• May sometimes retrieve results that are semantically related but not the exact match you needed for a highly specific query.

This search type is ideal for exploratory queries, broad questions, or situations where your users may not know the exact corporate terminology to search for.

The Best of Both Worlds: Hybrid Search

Keyword search is precise but misses context. Semantic search understands context but can sometimes miss specific keywords. Why not use both?

That's Hybrid Search. It combines the strengths of keyword and semantic search into a single, unified results list. This is the default search method in most Squirro projects (since version 3.8.6 LTS) because it provides the best balance of precision and recall.

When you run a semantic search query for "solar energy," Squirro executes both a keyword search for "solar energy" and a semantic search for the concept of solar energy. It then intelligently merges the two result sets, re-ranking them to surface the most relevant items, whether they're a perfect keyword match or a strong contextual match.

• Maximizes both recall (finding everything relevant) and relevance (putting the best results on top).
• Provides a safety net: you get the exact matches and the related concepts.

• Can be slightly more complex to tune the scoring, balancing how much weight to give to keyword vs. semantic results.

This is the clear choice for your general-purpose, primary search bar. It serves the widest range of user needs, from precise lookups to open-ended exploration.

The Umbrella: Cognitive Search

Finally, we have Cognitive Search. It's crucial to understand that this isn't just another search type. Cognitive Search is the umbrella term for Squirro's entire, AI-powered search capability.

It includes hybrid, semantic, and keyword search, but adds a wealth of other AI-driven features on top. Cognitive Search leverages AI to interpret queries, deeply analyze unstructured data, and learn from user interactions.

For instance, it enables powerful features like Question Answering, which directly extracts an answer ("Foreign Supplier Verification Programs") instead of just giving you the document that contains the acronym "FSVP." It also handles Synonym Lists (so "ankle biter" is understood as "small cap investment") and Federated Search to query all your connected data sources at once.

• Delivers the most relevant, personalized, and actionable results.
• Continuously improves over time by learning from user feedback.
• Handles the most complex enterprise use cases.

• Requires the most setup and tuning to enable and configure all its features.
• Its effectiveness depends on the quality and breadth of the underlying data.

You adopt the Cognitive Search framework when your goal is an enterprise-wide deployment where users need to find deep insights across multiple, complex data sources and types.

From Cognitive Search to Generative AI

This powerful retrieval capability is the bedrock for modern enterprise AI, particularly for retrieval augmented generation (RAG) systems. RAG works by first retrieving relevant documents from a knowledge base and then feeding that information to a large language model (LLM) to generate a precise, context-aware answer. The quality of this entire system hinges on the "R" (Retrieval).

If the search step fails, failing to find the most accurate, relevant, or up-to-date document, the LLM will still confidently generate a answer. That answer will, however, not necessarily be based on the optimal subset of information available to the system. A world-class cognitive search, therefore, isn't just about finding documents; it's the critical first step to ensuring trustworthy generative AI.

Balancing Precision, Context, and the "Right" Answer

It's tempting to see these search types as a simple progression, but it's more a question of balancing trade-offs. The primary tension in all of search is between precision (finding the exact thing) and recall (finding all the relevant things).

The pitfall of relying only on keyword search is that you sacrifice recall. You miss the "Energy Savings Initiatives" report because you searched for "power bills." The pitfall of relying only on semantic search is that for a specific compliance query, you might get a dozen articles about the policy instead of the one exact policy document you needed.

This is precisely why Hybrid Search exists. It's the engine designed to manage this balance, ensuring the precision of keywords and the contextual power of semantics work together. Your goal isn't just to find a document; it's to find the right one, and hybrid search is the most effective tool for the job.

The Takeaway: From Finding to Understanding

Ultimately, Squirro's layered search capabilities signal a fundamental shift. The goal is no longer just to find documents. The goal is to understand the information within them.

Choosing the right search strategy isn't about picking one method over another. It's about building a system that can fluidly move between them, using keywords for precision, semantics for context, and cognitive features for direct answers. By moving beyond a simple search box, you transform your data from a passive archive to be "found" into an active, intelligent partner ready to be "understood."

Download our Technical Guide: 'Driving Business Growth with Secure AI-Driven Enterprise Search' for a roadmap outlining exactly what it takes to implement secure AI search that delivers scalable, real-world impact.