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Squiz Conversational Search vs hands-off AI engines: finding the right fit for high-stakes content

A practical guide for digital leaders comparing open-source stacks with Squiz Conversational Search, powered by Funnelback.

Julie Brettle 11 Aug 2025

The image is a dark teal rectangular banner with rounded corners, titled “KEY TAKEAWAYS:” in bold white capital letters. It lists three white bullet points: the first says that hands-off AI engines are fast to deploy and work for simple, low-risk queries but often lack transparency, governance, and content control in complex or regulated environments; the second states that conversational AI search combines AI-driven answers with enterprise-grade governance, two-step verification, and configurable ranking to ensure responses are accurate, traceable, and based on approved content; the third notes that the best choice depends on whether the priority is speed and simplicity or control, accuracy, and collaboration across technical and non-technical teams.

Some AI search engines promise effortless implementation and full automation: plug in your content, and let the model do the rest. While hands-off tools sound appealing on the surface, they may limit transparency, governance, and content control, particularly in high-stakes or regulated environments.

If your organization operates in a regulated sector, serves diverse audiences, or depends on complex, high-stakes content, a more governed and configurable solution may be essential.

Squiz Conversational Search, built on the enterprise-grade Funnelback engine, adds value by striking a pragmatic balance, empowering teams with intuitive tools for governance, content control, and ongoing improvement. It doesn’t aim to replace your expertise with automation, but to enhance it through precision and flexibility.

This blog explores the key differences between hands-off AI search platforms and Squiz Conversational Search.

Skip ahead:

Dissecting hands-off AI search tools

Hands-off AI search platforms emphasize automation. These tools are often marketed as quick to implement, requiring minimal configuration to start delivering results. They typically use external large language models (LLMs) trained on open datasets, combined with lightweight machine learning layers to interpret content and user queries.

These engines shine when:

  • The content is clean, simple, and easy to index
  • You want fast setup and out-of-the-box performance
  • The risk of inaccuracies is low or acceptable
  • You’re comfortable with a black-box model that can infer answers without human tuning

These engines may be less suited when:

  • Content spans complex documents or highly specific formats
  • Accuracy, traceability, or compliance are important
  • You need to configure what the AI uses and how it responds
  • Teams require control over tuning, governance, or performance

Dissecting Squiz Conversational Search

Squiz Conversational Search offers the benefits of AI-powered answers with full transparency and governance. It layers a conversational interface on top of the Funnelback engine, giving technical teams deep configuration while enabling non-technical teams to contribute meaningfully to the search experience.

This solution shines when:

  • Content must be vetted, traceable, and approved
  • Responses need to stay grounded in known sources and accuracy is non-negotiable
  • Your users ask nuanced questions across formats and topics

This solution may be less suited when:

  • Simple use cases accept generic responses
  • Organizations prefer to hand over all control to the AI

How it works

Here’s how Squiz Conversational Search delivers accurate, governed, and user-friendly results:

  1. Flexible content ingestion: Indexes structured and unstructured content from web pages, PDFs, portals, databases, and file repositories.
  2. Smart ranking engine: Applies 75+ relevance signals, including user intent, content quality, and document structure, to rank results.
  3. Audience-aware scoping: Lets teams define which collections serve which users or topics (e.g. students, citizens, clients).
  4. Natural language understanding: Interprets conversational queries and retrieves the most relevant, context-aware responses.
  5. Two-step verification process: The system first generates an answer based on your content, then automatically runs that answer through a built-in validation step. During this validation step, the system performs a “faithfulness check”, verifying that the generated response draws only from your approved content sources and doesn’t include any external information. This two-step process ensures that all responses remain accurate and grounded in your organization’s content.
  6. Retrieval-Augmented Generation (RAG): Combines Funnelback’s precision search with AI generation, ensuring all answers are grounded in approved, indexed content only.
  7. Low-code admin tools: Enables marketers and content teams to manage ranking logic, scope, and curated answers without relying on developers.
  8. Content readiness & governance: Includes pre-launch audits with the aid of a Squiz consulting team, and monitoring tools to flag outdated, incomplete, or misaligned content.
  9. Enterprise-grade infrastructure: Fully hosted in Squiz DXP with strict privacy controls, no data is sent to open systems.

For more details on our unique, 5-step framework to prepare your website for AI search, check out this blog.

Squiz Conversational Search vs hands-off AI engines

Unlike tools that treat AI as the search engine, Squiz Conversational Search uses the enterprise-grade Funnelback engine to control what content gets used and how the AI works with it.

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

With Squiz, teams will soon be able to monitor search and AI performance from day one, using built-in dashboards that track top queries, user behavior, and answer effectiveness. This visibility helps optimize faster, without waiting for user complaints or guesswork.

Real-world use cases

To understand how hands-off AI engines compare to Squiz in real-world contexts, let’s break down some common scenarios across sectors:

Government use case:

 

Hands-off AI engines

Squiz Conversational Search within Squiz Funnelback

When it comes to government websites…

…can offer fast responses to routine queries where content is consistent and standardized. But they may struggle with nuanced policy or compliance content if they lack content boundaries.

…enables teams to scope and manage content inputs, making it easier to ensure responses are grounded in up-to-date, approved policy and service guides.

Example:

A department launches an AI-driven FAQ tool…

…and over time, answers start referencing outdated PDFs or irrelevant sources, with no way to see what’s been used.

…and scopes the system to current service guides only, giving users answers backed by vetted content and traceable sources.

Higher education use case:

 

Hands-off AI engines

Squiz Conversational Search within Squiz Funnelback

When it comes to higher education websites…

…can help with common queries like “term dates” or “how to apply.” But auditability becomes a challenge when students ask complex, context-specific questions.

…offers full traceability through query logs, two-step verification, and source attribution, helping staff understand and refine the AI’s behavior over time.

Example: A student asks “How do I defer my enrolment?”...

…and gets a vague response pulled from a dated blog post. Staff can’t see what source was used or why it was surfaced.

…and receives a clear, policy-backed answer with links to the deferment page. Staff can see exactly how the result was formed.

Professional services use case:

 

Hands-off AI engines

Squiz Conversational Search within Squiz Funnelback

When it comes to professional services websites…

…can return quick answers for general service questions, but may lack the depth needed for complex legal or regulatory guidance without structured control..

…enables content owners to shape responses and prioritize high-trust content types, like regulatory briefings.

Example:A law firm launches an AI assistant to help clients find compliance information…

…and users get conversational responses that feel helpful, but require follow-up due to vagueness.

…and users asking questions like “What are the data retention requirements in NSW?” get a direct, source-backed response from a vetted regulation.

The image is a rectangular banner with a light orange background and rounded corners. It contains black text that reads, “For more details on the benefits of conversational AI search for these specific industries,” followed by a blue, underlined hyperlink that says “check out the blogs here.”

Key takeaway and next steps

If you're evaluating whether your current AI search setup meets your standards for accuracy, governance, and team collaboration, it might be time to explore more flexible, transparent alternatives.

Hands-off AI platforms offer speed and ease, especially for low-risk use cases. But in content-rich, high-stakes environments, they may fall short in areas like control, explainability, and accuracy - all critical for building user trust.

Squiz Conversational Search, part of the Funnelback engine, gives you the best of both worlds: a powerful AI-driven experience that’s governed, transparent, and aligned with your organization’s standards from day one.

Explore how Squiz can help you create AI search experiences that are smarter, safer, and more useful, without giving up control.

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