A practical comparison of enterprise search engine types

AI search is changing the way people interact with websites. The search bar is no longer just a utility; it’s a strategic interaction and gateway to your digital experience.

But with multiple search platforms now claiming AI credentials, choosing the right one is increasingly complex. With so many solutions promising relevance, speed, and automation, understanding what each type of search engine does best, and where it may fall short, can help you find the right fit.

This guide compares four major types of enterprise search solutions, outlining how each works and where they perform best. It also explores how Squiz Conversational AI Search, part of the Squiz Funnelback platform, fits into the mix. You’ll find practical examples and guidance to help you determine which approach aligns best with your organization’s needs.

Skip ahead:

• Commerce-focused engines vs. Squiz Conversational AI Search
  • Key takeaway
• Open-source based platforms vs. Squiz Conversational AI Search
  • Key takeaway
• Hands-off AI engines vs. Squiz Conversational AI Search
  • Key takeaway
• Why consider Squiz Conversational AI Search

Commerce-focused platforms are optimized for product search. They’re fast and lightweight, but not built for content-rich sites or complex use cases across government, higher education, or professional services.

Here’s how commerce-focused engines compare to Squiz Conversational AI Search across key capabilities:

Commerce-focused engines are best for:

Ecommerce platforms or product-heavy websites that need fast, structured data retrieval with minimal complexity.

Example: A furniture retailer with thousands of SKUs uses a commerce engine to let users filter by size, material, and delivery time. The product database is well structured, and customers don’t need nuanced explanations, just fast, faceted search and sorting.

Squiz Conversational AI Search is best for:

Content-rich service sites that need advanced ranking, marketer control, and unstructured data support.

Example: A university offers hundreds of degrees, scholarships, and student services across multiple faculties. With Squiz Conversational AI Search, teams can index both structured and unstructured content, ensuring students get accurate, context-aware answers, whether they’re asking about postgraduate scholarships or how to defer an offer.

While commerce engines work well for transactional sites, organizations with complex services and long-form content may benefit from a more content-focused approach.

These platforms offer high flexibility and deep customization. But that power often comes with complexity, cost, and dependency on specialist development resources.

Here’s how open-source based platforms compare to Squiz Conversational AI Search across key capabilities:

Open-source based platforms are best for:

Organizations with deep in-house technical expertise that want full control over every aspect of the search stack.

Example: A scientific research group with a skilled internal dev team builds a federated search experience across dozens of niche datasets and journals. Their engineers fine-tune the algorithm and indexing logic regularly, adapting it to new formats and taxonomies as needed.

Squiz Conversational AI Search is best for:

Teams that want flexibility without complexity, combining enterprise-grade performance with low-code configuration and managed support.

Example: A professional services firm needs to adapt search results quickly as new regulations and insights are published. With Squiz Conversational AI Search, content teams can use Curator to update collections, tune rankings, and add curated answers, all without relying on developers.

Open-source tools offer deep customization, but may require significant dev investment. Squiz provides similar control with less overhead.

Machine learning-focused platforms promise high levels of automation. But in practice, that can mean limited transparency, little custom control, and unpredictable behavior, especially in regulated or high-stakes environments.

Here’s how hands-off AI engines compare to Squiz Conversational AI Search across key capabilities:

Hands-off AI engines are best for:

Teams prioritizing automation over transparency, often in content environments where precision and oversight are less critical.

Example: A small business integrates an AI search plugin into its blog archive. The tool pulls answers directly from recent articles, providing fast, conversational summaries for common queries like “how do I reset my password?” with minimal setup or ongoing tuning required.

Squiz Conversational AI Search is best for:

Regulated or high-stakes environments where AI outputs need to be governed, explainable, and trustworthy.

Example: A government agency wants to simplify FAQ discovery for citizens. With Squiz Funnelback, they can scope responses to only include vetted service guides and policy documents, ensuring answers stay accurate, up-to-date, and source-backed, even as content evolves.

Hands-off tools reduce setup effort, but may lack explainability. Squiz is designed for teams that need AI outputs to be grounded, transparent, and configurable.

Squiz Funnelback Search is designed to support organizations with complex, content-rich environments.

While some AI-powered tools lean heavily toward automation or require extensive technical input, Squiz Conversational AI Search, part of the Funnelback engine, offers a more balanced path. It combines enterprise-grade search capabilities with a conversational interface, while keeping responses grounded in content you’ve vetted and approved.

For teams managing large government portals, higher education sites, or professional services platforms, it’s a way to support AI-powered discovery without losing oversight, accuracy, or ease of use.