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Squiz Conversational Search vs open-source search platforms: how much control is too much?

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 rectangular banner with a dark teal background and rounded corners, featuring the heading “KEY TAKEAWAYS:” in bold white capital letters. Below the heading are three white bullet points. The first states that open-source search engines offer maximum flexibility and control but require significant developer resources, governance planning, and ongoing maintenance. The second explains that conversational AI search provides enterprise-grade configuration with low-code tools, allowing technical and non-technical teams to collaborate and optimize results. The third notes that the best choice depends on whether the priority is building a fully bespoke search stack or achieving speed, scalability, and shared ownership without heavy developer reliance.

Some organizations want total control over every layer of their search stack. For technically advanced teams, open-source platforms offer a tempting promise: total freedom to design and control every aspect of the search experience. That level of freedom is powerful, but it also means teams need the time, technical expertise, and resources to manage it effectively.

In contrast, Squiz Conversational Search, built on the enterprise-grade Funnelback engine, offers a balanced approach: giving technical teams the power they want, while enabling content and marketing teams to contribute directly.

This blog breaks down how Squiz compares to open-source platforms, helping you find the right balance of configurability, speed, and shared ownership.

Skip ahead:

Dissecting open-source platforms

Open-source search engines are highly customizable and often favored by engineering-led teams. These platforms make their source code freely available, enabling developers to modify, extend, and integrate search functionality with nearly any system or infrastructure.

But the same flexibility that makes open-source appealing can also introduce trade-offs, especially when fast setup or broader team involvement is needed.

These engines shine when:

  • You want full control over infrastructure and search logic
  • You have in-house developers familiar with the toolset
  • You need to build a bespoke search experience for a very specific use case

These engines may be less suited when:

  • Content or marketing teams need direct access to tune or configure results
  • Implementation needs to be fast or low-code
  • Governance, explainability, and ongoing maintenance need to be shared across teams
  • Budget or timeframes limit extensive custom builds

Dissecting Squiz Conversational Search

Squiz Conversational Search combines the control of an enterprise search engine with the accessibility of a conversational interface. Built on Funnelback, it offers enterprise-grade configuration while enabling non-technical teams to tune and manage results via low-code tools.

This solution shines when:

  • Multiple teams (marketing, IT, content) need to collaborate on the search experience
  • Results must be accurate, traceable, and adaptable over time
  • Time-to-value is critical and technical resources are limited
  • Your organization needs a secure, scalable, and governed AI search experience

This solution may be less suited when:

  • Organizations make a point of building from scratch or deeply embeding search logic into custom architecture
  • Teams want to own every layer of the stack and are resourced to support it

How it works

What makes Squiz different is how it balances enterprise-grade capability with ease of use. Instead of requiring custom development for every feature, it offers flexible configuration out of the box, while still giving teams the tools to optimize continuously, through:

  1. Managed platform: Squiz is hosted and maintained within the Squiz DXP, reducing the infrastructure burden on your team.
  2. Configurable ranking engine: Uses 75+ signals and customizable logic to determine relevance, with no need to build from scratch.
  3. Flexible ingestion: Indexes content from structured and unstructured sources, including databases, files, and websites.
  4. Low-code optimization: Content teams use Curator to manage collections, fine-tune results, and add curated answers.
  5. Conversational layer: Adds natural language understanding to Funnelback’s powerful search stack, making results more intuitive and user-friendly.

The image contains a rectangular banner with a light orange background and rounded corners. It features black text that reads, “For more details on what conversational AI search is and how it works,” followed by a blue, underlined hyperlink that says “check out this blog.”

Squiz Conversational Search vs open-source search platforms

Open-source platforms are known for their flexibility, but often require significant effort to maintain and scale. Squiz delivers that same depth without the heavy lift.

Here are the key differences between this enterprise-grade solution and open-source search platforms:

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

Real-world use cases

To understand how open-source tools compare to Squiz in real-world contexts, let’s break down some common scenarios across sectors:

Government use case:

 

Open-source platforms

Squiz Conversational Search within Squiz Funnelback

When it comes to government websites…

…can be customized to meet rigorous public sector needs, especially where data infrastructure is already in place. However, governance and explainability may require add-ons or bespoke development.

…has built-in query logs, source attribution, and content scoping provide traceability and transparency out of the box.

Example:

A department launches a public search tool …

…tailored to internal systems, but that struggles to trace what the AI is surfacing or explain results.

…and gets traceable, auditable responses with source links out of the box.

Higher education use case:

 

Open-source platforms

Squiz Conversational Search within Squiz Funnelback

When it comes to higher education websites…

…universities with strong dev teams may prefer open-source tools to fully tailor search across faculties or departments, but tuning search logic typically requires ongoing developer input.

…marketers and IT can co-manage search experiences via Curator, allowing for faster iteration without engineering handoffs.

Example: A university’s content team…

…implements a customized search setup across academic departments, but needs developer time for every change, even when only content needs adjusting.

…implements a customized search setup across academic departments, using Curator to update search results and add audience-specific answers directly, in minutes.

Professional services use case:

 

Open-source platforms

Squiz Conversational Search within Squiz Funnelback

When it comes to professional services websites…

…consulting firms may choose open-source platforms to build custom logic tailored to niche domains or regulatory content,  but adjusting that logic over time may require dev backlog and QA testing.

…has a built-in ranking engine that uses 75+ signals and can be tuned by business users to reflect content priorities with minimal lift.

Example:A consulting firm…

…configures a tailored search algorithm to prioritize compliance and insights, but changing ranking logic takes weeks of dev time.

…fine-tunes ranking weights using built-in admin tools, with no engineering backlog required.

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

Choosing the right search engine means asking the right questions:

  • Can your teams configure and optimize search without handoffs or blockers?
  • Will the system scale with your needs without adding overhead?
  • Does it help users find accurate, trusted answers every time?

If your current stack is becoming too complex to scale or too technical to democratize, it might be time for a change.

Open-source platforms offer maximum flexibility, but that can come at the cost of speed, collaboration, and maintainability.

For teams who want flexibility without building everything from scratch, Squiz Conversational Search, part of the Funnelback engine, offers a practical alternative, combining enterprise-grade tools with a managed platform that scales across teams. It empowers your business and technical teams to collaborate, adapt, and improve search experiences quickly, without sacrificing control, security, or transparency.

The result? Faster answers for users, less reliance on developers, and more value from your content.

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