Our 5-step framework to preparing your website for AI search

As conversational tools like ChatGPT reshape how people find information, users now expect websites to deliver answers, not links. Whether they’re using generative AI platforms or interacting with conversational interfaces on your site, the expectation is the same: ask a natural question, get a clear, human-like response.

We explore this changing behavior in our blog on what the rise of AI search means for your digital strategy. But understanding the shift is just the beginning. Successfully delivering the conversational experience users now expect, on your own site, is something else entirely.

The truth is, AI search implementations risk falling short not because the technology isn’t advanced, but because the content supporting it isn't ready. The effectiveness of conversational search depends on the quality, clarity, and structure of the content it pulls from. If that content is outdated, inconsistent, or buried in confusing site architecture that makes it difficult to find and synthetize, then the responses will reflect those issues. And users will be frustrated.

At Squiz, we help organizations avoid this scenario with a structured, consulting-led approach to preparing for the implementation of our Conversational Search feature, powered by Squiz Funnelback Search. It all starts well before deployment - by preparing your teams, your content, and your site for the realities of AI search. This prep work ensures that what the AI draws from is accurate, relevant, and usable.

“AI search isn’t a future trend, it’s already transforming how people find information. Our mission is to help organizations meet these new expectations by getting their content ready, guiding them step by step, and providing the tools to keep improving over time.” - Julie Brettle, Chief Product Officer, Squiz

Let’s break down the framework we use to get it right.

Skip ahead:

• Inside our 5-phase framework
  • Phase 1: Engage
  • Phase 2: Discover
  • Phase 3: Define & Refine
  • Phase 4: Deliver
  • Phase 5: Optimize
• Is your site ready for AI search?

Success with conversational search isn’t just about surfacing great content. It’s also about aligning your teams and creating experiences framed around real user needs.

To make this happen, we use a consulting-led framework that provides structure, clarity, and accountability at every stage of implementation: Engage, Discover, Define & Refine, Deliver, and Optimize.

No matter your level of readiness or resources, every project follows this 5-phase framework. What changes is the size and scope of the content audit, as well as the depth, speed, and level of support based on your current situation, goals and resources. We offer three implementation approaches to match different needs:

• Starter sprint: A focused and fast paced sprint designed to quickly test conversational search on a small content area, typically using a small number of queries and responses, ideal for teams looking to validate and go live quickly before scaling​.
• Pro rollout: A strategic implementation of conversational search on a live, customer-facing section of your site, supported by stakeholder alignment, success metrics, and a wider content assessment and best-practice guidance to support internal content optimization for an initial use case​.
• Custom engagement: A customized conversational search experience combining functional and technical implementation with in-depth content audits, UX/ UI design, strategic guidance, user research, capability building, and a roadmap for continuous improvement across multiple phases​.

Whether you’re running a quick sprint or a phased, multi-stage strategy, the framework remains flexible and scalable to meet your needs.

Let’s now dive into each of the five phases:

This phase sets the foundation for a successful engagement. Once an organization expresses interest in conversational search, we align on goals, define the first use case, and ensure readiness. We establish team roles, review feasibility, and confirm shared expectations at this stage, working together with customers to define measurable success criteria, such as response accuracy, user engagement, or time efficiencies.

This ensures everyone is aligned on goals, scope, and what success looks like before the work begins.

This is when we would also finalize the selection of the first slice for initial implementation. The right slice is:

• Low-risk and high confidence to update (one that teams are confident around the content quality, accuracy, recency, and need for)
• High value in terms of user needs and business impact
• Accessible to our search crawler

This could be something like a FAQ section, help center, or product overview.

With the project approved, we move into discovery. This phase focuses on further alignment and insight gathering through a review of current search configuration, performance and analytics. We analyze search logs and current search performance to identify the most frequently used keywords, understand where users are struggling, and what content is being accessed or missed. These insights help surface user behavior and content patterns, pain points, and opportunities to consider in our initial use case.

We run common queries through a conversational readiness tool (Semantic Auditor) to test how well your existing content supports clear, consistent answers and pinpoint where refinements are needed. If you still want to explore the process of making your content AI-ready in more depth, we cover this in detail in this blog.

This phase turns discovery insights into a focused implementation plan. We assess the semantic content audit findings and identify what needs to be updated, removed, or optimized. We determine key success metrics and the best implementation pattern to support user needs and business outcomes. From a content perspective, we use the Squiz DXP AI search tools to identify any problem areas for any content anomalies or differences, checking for duplication, outdated material, and mis-alignment with best-practice, accessibility and usability principles.

To support content refinement and remediation, we walk through our assessment review and recommendations, providing a best-practice guide to help teams make their content AI-ready: clear, consistent, and aligned to real user intent.

With content reviewed and approved, we move into implementation. During this phase, the conversational search tool is configured, tested, and prepared for launch.

We start by  the conversational search experience into your website and then move into user acceptance testing (UAT), which is a structured testing phase where your team checks that everything works as expected and the search experience is delivering accurate, useful answers. Our consulting team continues to support you closely during this phase, as this is also when we troubleshoot any outstanding issues identified during UAT, advise and make final refinements to the content if needed, and confirm readiness to go live.

For example, during testing we might find that a query like “How do I update my account settings?” returns a generic help page instead of a specific guide. In that case, we’d troubleshoot whether the issue lies with the content structure, search configuration, or missing metadata, and refine accordingly before launch.

Once all testing is complete and the implementation is signed off, we launch the first use case on your live site.

Once the first use case is live, we move into performance monitoring and continuous improvement. This phase is focused on ensuring the conversational search experience stays accurate, helpful, and aligned with user needs over time.

We begin with performance monitoring and a post-launch review, supported by our technical team. From there, we schedule regular check-ins to evaluate results, troubleshoot any issues, and identify opportunities to refine the experience further.

For example, we might track how users interact with the search tool, identify queries that aren't returning helpful answers, and flag areas where content needs to be adjusted or expanded. If we see patterns of confusion or drop-off, we revisit the content structure or update the way queries are interpreted.

This phase makes sure conversational search isn’t a one-time implementation. It’s a capability that evolves, growing smarter and more useful as you learn from real user behavior.

We review against our established and measurable success criteria, such as query accuracy, content engagement, or user satisfaction, to track performance and guide ongoing optimization.

Rolling out conversational search isn’t just about the tool, it’s about change management and long-term support. That’s why our consulting model includes post-launch performance tracking, guided templates for content owners, and regular check-ins to ensure continued success. Whether you start small or scale fast, we provide the strategic support to grow with you.

“Implementing conversational search is just the beginning. The real value comes from continuous refinement, tracking how users engage, learning from the data, and evolving the experience over time. That’s why structure and post-launch support are built into every project we deliver.” - Julie Brettle, Chief Product Officer, Squiz

With the right framework, you can create a search experience that’s not just functional, but genuinely helpful. One that meets modern expectations and earns user trust.

At Squiz, we’re not just giving you a tool. We partner with you through a consulting-led approach that ensures your teams, your content, and your infrastructure are fully prepared for success. Because no matter how advanced the technology, a conversational search experience is only as good as the content it draws from. That’s why we focus so heavily on the pre-implementation phase, helping you audit, optimize, and align your content before anything goes live.