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A practical comparison of enterprise search engine types

A practical guide to the three enterprise search platform categories offering AI functionality, and how they compare to Squiz Conversational Search, powered by Funnelback.

Julie Brettle 19 Jun 2025

A dark teal banner titled "Key Takeaways" explains that not all AI search platforms serve the same purpose—some are designed for product search, while others prioritize developer control or speed. It highlights that the guide offers side-by-side comparisons and practical examples to help organizations evaluate which approach best fits their needs. It also notes that teams operating in content-rich or high-stakes environments may benefit from platforms offering greater governance, configurability, and traceability of content sources.

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 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:

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1. Commerce-focused engines vs. Squiz Conversational 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 Search across key capabilities:

Feature

Commerce-focused engines

Squiz Conversational Search within Squiz Funnelback

Ranking configuration

Basic out-of-the-box ranking, using simple product rules (e.g. popularity, price)

Uses 75+ customizable ranking signals optimized for relevance and content-rich environments

Content support

Optimized for structured data only, like product catalogs or ecommerce-style data

Handles structured and unstructured rich, varied content, like documents, pages, and PDFs

Audience targeting

Limited personalization with a narrow focus that is specific to product catalogues. It also; often requires dev work

Built-in content scoping for audience-specific results

Marketer control

Minimal low-code options; developer-heavy tuning

Curator tool allows content teams to tune results directly

In summary: Commerce-focused engines offer basic out-of-the-box ranking based on simple rules like popularity or price. They are optimized for structured data, such as product catalogs, but have limited personalization beyond product-specific contexts and often require developer involvement for changes. Marketer control is minimal, with few low-code tools available. In contrast, Squiz Conversational Search uses over 75 customizable ranking signals suited for content-rich environments, handles both structured and unstructured formats like PDFs and policy documents, offers built-in audience targeting, and allows content teams to directly manage results via the Curator tool without code.

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 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 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.

Key takeaway:

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

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2. Open-source based platforms vs. Squiz Conversational Search

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 Search across key capabilities:

Feature

Open-source platforms

Squiz Conversational Search within Squiz Funnelback

Technical setup

Requires upfront architecture, integration, and tuning

Hosted and managed as part of Squiz DXP, ready-to-go search engine with flexible configuration

Ranking configuration

Fully customizable but manual

Uses 75+ customizable ranking signals plus tunable logic and override options

Developer dependency

High, with ongoing engineering work needed for changes and improvements

Low, as business users manage and optimize via no-code tools

Governance tools

Must be built or integrated separately

Built-in content scoping, audit logs, and admin controls

Machine learning (ML)

Basic or requires plug-ins

Integrated ML with manual control and governance

Content auditing tools Typically lacks proactive tools to identify outdated or conflicting content Includes pre-launch content audits and ongoing diagnostics to improve accuracy and flag content issues before they affect users

Marketer tools

Minimal; devs own most of the configuration capabilities

Marketers can tune search via Curator without code

In summary: Open-source search platforms require significant upfront technical setup, including architecture, integration, and tuning, and depend heavily on developers for changes. Ranking is fully customizable but must be configured manually. Governance tools and content auditing typically need to be built or integrated separately, and proactive tools for detecting outdated or conflicting content are often absent. Machine learning capabilities are basic or rely on plug-ins. In contrast, Squiz Conversational Search is hosted and managed as part of the Squiz DXP, offering ready-to-go flexible configuration with more than 75 customizable ranking signals and tunable logic. It reduces developer dependency by enabling business users to manage and optimize via no-code tools, has built-in governance features like content scoping and audit logs, integrates ML with manual control, and includes pre-launch audits plus ongoing diagnostics to maintain accuracy. Marketers can make changes directly via Curator without code.

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 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 Search, content teams can use Curator to update collections, tune rankings, and add curated answers, all without relying on developers.

Key takeaway:

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

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3. Hands-off AI engines vs. Squiz Conversational Search

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 Search across key capabilities:

Feature

Hands-off AI engines

Squiz Conversational Search within Squiz Funnelback

Ranking configuration

Good out-of-the-box ranking, but often non-configurable

Transparent, override-ready, and tunable

Content control

Often pulls from uncontrolled datasets or open web

Strict indexing from approved, trusted content only

Governance & source transparency

Automated ML with limited admin control, making it hard to trace how answers are generated

Transparent logging and answer traceability with source-based, auditable results

Risk of hallucination

High, as AI may generate false or unsourced answers due to lack of control over content crawled

Low, thanks to content crawling controls and a two-step verification process that checks every answer

Optimization

Limited configuration options; relies on passive learning from user behavior over time

Extensive tuning options from day one, including seasonal prioritization, curated answers, query overrides, and relevance weighting across 75+ signals

Content auditing tools Typically lacks proactive tools to identify outdated or conflicting content Includes pre-launch content audits and ongoing diagnostics to improve accuracy and flag content issues before they affect users

Output

Answers that can be generic, wrong, or too vague to be actionable

Contextual, conversational answers with clear source attribution

In summary: Hands-off AI engines deliver good out-of-the-box ranking but often cannot be configured. They may pull from uncontrolled datasets or the open web, rely on automated machine learning with minimal admin oversight, and make it difficult to trace how answers are generated. This can increase the risk of hallucination, as AI may generate unsourced or inaccurate content, and optimization options are limited, often relying on passive learning from user behavior over time. Proactive content auditing tools are typically absent. Squiz Conversational Search, on the other hand, offers transparent, tunable ranking and strict indexing from approved content only. It supports transparent logging and source traceability, uses a two-step verification process to check every answer, and allows extensive tuning from day one—covering seasonal prioritization, curated answers, query overrides, and weighting across 75+ signals. It also includes pre-launch audits and ongoing diagnostics to identify and resolve content issues before they impact users.

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 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.

Key takeaway:

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.


Why consider Squiz Conversational Search

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 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.

Want advice on how to get started with conversational AI search? Book a 30-minute chat with a Squiz strategy consultant here.