The new front line of experience: AI-powered discovery
Search and recommendation engines have evolved from being mere back ends behind the scenes to becoming the center of digital business success. Whether it's a customer finding the perfect product in moments or a reader diving deeper into your content, AI has become the engine behind discovery, engagement, and — most critically — conversions.
But experimenting with AI search requires solid preparation beforehand. First, you need to wrap your head around three critical areas: your understanding of what AI-powered search actually involves, your data, and your architecture.
This guide explores what AI search really is (and what it's often confused with), what technical aspects to consider, what a solid AI search stack looks like, and how Brimit helps companies design and implement intelligent discovery experiences.
Smart search and AI: What's what among modern search tools
Modern search systems vary in terms of how much intelligence they bring to the user experience. Many combine keyword logic with machine learning features to boost relevance and usability. Others go further, introducing semantic understanding and generative capabilities.
Let's break down the difference.
Smart search with rule logic and usability features
Tools like Sitecore Discover, Algolia Recommend, and Coveo primarily rely on keyword-based search, but they enhance it with machine learning features to improve relevance and automate certain decisions. These systems don't offer full semantic understanding or generative capabilities, but they do include valuable features such as:
- Typo tolerance and synonym support
- Faceted filtering and ranking rules
- Merchandising tools (e.g., product boosting and pinning)
- Predefined product or content recommendations ("you might also like")
- Manual boost of key products during promotions like Black Friday
- A/B tests on layouts, messaging, or product cards
- Personalized listings based on session behavior, even without logging in
- Rule-based recommendations (upsell, cross-sell, affinity-based)
AI-driven search: Understanding context and generation
At the other end of the spectrum are systems that go beyond keyword logic. They apply semantic techniques and large language models (LLMs) to understand user intent and context. Tools like Azure AI Foundry, Weaviate, and Amazon Q use vector embeddings to match queries based on meaning, not just terms. When paired with LLMs, they can also retrieve relevant internal content and generate direct, contextual responses in natural language.
Main features include:- Semantic search with vector matching: matching queries to content based on meaning, even when keywords don't overlap
- Real-time behavioral personalization: dynamically re-ranking results based on live session data
- LLM-powered responses (RAG): retrieving internal knowledge and generating natural-language answers on the fly
- Embedded chat-style search in product detail pages, account portals, or mobile apps
- Contextual promotions based on user intent (e.g., show a sample pack if a user browses multiple products)
- Triggered guided experiences (e.g., "Help me choose" wizards that evolve based on user feedback)
- Automated answers to complex queries, which are especially helpful in B2B, technical, and support contexts
4 important aspects in AI search implementation
There is a risk of jumping into AI by starting with interface enhancements or integrating LLMs. But effective AI discovery depends on well-prepared data, scalable architecture, and the right components working together.
Data readiness and structure
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Ensure content is accessible through APIs
Your core systems, such as CMS, e-commerce engines, or media libraries, should allow external systems (e.g., a search engine) to fetch content programmatically.
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Unify structured and unstructured content sources
AI search thrives when it has access to both types of data: product catalogs, articles, guides, images, videos, and even support tickets.
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Prepare content for semantic search
Tagging, categorization, and metadata should be consistent across systems. Content needs to be deduplicated and enriched so that AI can identify and prioritize relevant results.
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Standardize content formats for real-time use
Are documents hidden in PDFs or slides? Video transcripts? Make sure they're converted into searchable text and structured so that AI tools can index and update them in real time.
Semantic search (vector-based retrieval)
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Does your platform support vector search?
Vector-based search indexes meaning, not just keywords. It's important to check if your system (or vendor) supports vector embedding and retrieval, either natively or via extension.
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Is it compatible with your AI model?
Embeddings (vector representations of meaning) must be generated by the same model family as the one used to interpret queries or LLMs — mismatched embeddings reduce answer accuracy.
Intelligent response generation (LLMs)
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Does your use case actually require LLMs?
Not every scenario requires generation. For many product or FAQ queries, semantic search alone is sufficient. Start with retrieval, and only add generation (RAG) where it enhances user value.
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Will you use a vendor-hosted model or a custom setup?
While fast to deploy, vendor-hosted options such as Azure OpenAI and Amazon Q may offer limited control over fine-tuning and data storage policies. At the same time, custom/self-managed options (e.g., an open-source LLM coupled with your infrastructure) may incur higher setup costs and require ongoing infrastructure and MLOps.
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How is access to sensitive data controlled?
Whether you're using RAG or document retrieval, sensitive content may be exposed. Evaluate whether your system enforces role-based access (RBAC) to restrict content shown in results or whether it is passed to LLMs.
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If you opt for a vendor or a platform, how do they secure your data?
For LLMs hosted in the cloud or provided by a vendor, ensure your data is not used to train the base model. Also, look for providers that support bring-your-own-data (BYOD) securely.
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How do you plan to manage and optimize costs?
LLM-related costs can grow significantly as query volume, content size, and model complexity increase. Key aspects to consider include the type of model used (larger models like GPT-4 cost more than task-specific alternatives), as well as the length of input and output, and how frequently generation is triggered during user interactions.
Orchestration and UX integration
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Can you integrate with existing DXP, commerce, or support systems?
Make sure your current tech stack is flexible enough to bring all components into a unified experience rather than keeping them siloed across systems.
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Is the user experience optimized for AI-driven interaction?
Evaluate whether your front end supports modern interaction models, such as autocomplete, conversational input, dynamic filtering, and whether the orchestration layer can connect user inputs to back-end logic (e.g., search, recommendations, and LLMs) with low latency.
How to measure your AI search pilot with precision and gain relevant insights
So far, we've covered the benefits and key aspects of AI search implementation to help you understand what's really involved and clear up some common misconceptions. Now, let's go one step further in your strategy journey: how to measure your implementation effectively. The goal is to ensure it doesn't drain your budget but instead delivers valuable lessons and clear ROI signals to help you plan your next steps.
When implementing AI-powered search and recommendations, you first need to define your target metrics before launch and then set up the right tools. You can't validate impact if you don't know what success looks like.
Based on projects we've delivered, here's how different teams typically structure their AI search metrics, both for pilot validation and long-term optimization.
Product and e-commerce teams
| Metric | What to measure | Target |
|---|---|---|
| Search conversion rate | Are search users converting more than non-search users? | A measurable lift in CR for search-using sessions compared to your site average |
| Revenue per visitor | Is search driving more revenue per session? | Higher RPV for users engaging with AI-powered recommendations vs. static listings |
| Average order value | Are users buying more via search and recommendations? | Growth in AOV specifically for sessions with AI recommendations enabled |
| Click-through rate | Are users clicking what they see first? This indicates relevance. | Focus on the top 3 result CTRs if they earn more clicks, especially on high‑intent queries |
| Zero-result rate | How often does search fail to return results? | A significant reduction in "No Results Found" pages thanks to semantic understanding |
| Time to first click | How quickly do users find relevant items? | The faster they do, the smoother the discovery experience |
Marketing and merchandising teams
| Metric | What to measure | Target |
|---|---|---|
| Sales effectiveness | Is AI helping prioritize and sell more high-margin products to the right customers? | A shift in sales mix and contribution margin, where AI-driven ranking or recommendations increase revenue share from high-margin products |
| Personalization | How many users are receiving "cold start" (session-based) personalization without logging in? | Growth in engagement metrics for anonymous users seeing dynamic, intent-based results |
| Campaign deployment speed | How much manual effort is required to launch a seasonal promotion? | A shift from weeks to days to set up search rules, synonyms, and landing pages |
| Search-driven campaigns | Are marketing emails, landing pages, or on-site campaigns outperforming static ones? | Improved conversion and lower bounce rates on AI-driven dynamic pages vs. manual versions |
Tech and customer experience teams
| Metric | What to measure | Target |
|---|---|---|
| Search latency | Does the experience feel instant to end users? | 95th-percentile response time (under 250 ms) |
| Indexing freshness | How quickly does new content or inventory become searchable? | Achieving near-real-time updates for critical inventory or pricing changes |
| Cost-per-query (CPQ) | What is the operational cost of the AI layer (API calls, LLM tokens)? | Maintaining a sustainable CPQ ratio relative to the revenue or lead value generated |
| System stability and uptime | Is the search layer consistently available and performant? | 99.9% uptime or better |
| Usability for non-technical teams | Can merchandising, content, or marketing teams use the system independently? | Adoption without developer dependency |
Start building AI search that works
By now, you understand what goes into implementing AI-driven discovery and what to measure to ensure it delivers. The final step is execution. Success requires a clear roadmap and the right support to move from pilot to scalable impact.
Scope of work:
Clarifying the goal of AI search. Is it meant to drive revenue (e-commerce), reduce support load (help center), or improve access to internal knowledge (B2B portal)? Deciding whether you need classic search, recommendations, natural-language answers, or all three.
Based on your goal, we prepare a short use case brief with KPIs (e.g., increase CVR by X%, reduce time to answer by Y seconds) and define the test scope (e.g., a specific product category, help section, or region).
Scope of work:
Inventorying your content sources, such as CMSs, knowledge bases, DAMs, CDPs, and product catalogs. Evaluating how data is exposed (e.g., API, export, or databases), whether it's structured or unstructured, and what needs to be cleaned, tagged, or transformed.
We prepare a data readiness report, including:
- Source systems and data types
- Data access methods (API/export availability)
- Gaps in tagging, duplication, or format inconsistency
- Quick wins (e.g., enrich metadata and normalize naming)
Scope of work:
Deciding between:
- Vendor platform (e.g., Azure AI Foundry or Amazon Q)
- Custom search setup (e.g., Qdrant or OpenAI)
- Hybrid approach (a vendor platform combined with a custom LLM or orchestration layer)
Considering cost, time to launch, flexibility, and how it fits with your current stack.
A final architecture diagram and tooling recommendation that shows both reused and new components, the chosen tools for each layer, and a phased roll-out plan.
Scope of work:
Assessing whether LLMs are needed at all. For many workflows, semantic search is enough. Alternatively, figuring out cost-optimized RAG systems that balance user experience with total cost of ownership.
We provide a RAG scope document that includes:
- Source data
- LLM grounding techniques (vector search, keyword retrieval, and knowledge graph integration)
- LLM model selection criteria
- A security and access control plan
- Cost strategy (prompt/token budget, model tiering, chunking logic)
Scope of work:
Embedding the new search and discovery experience into your existing environment (e.g., a DXP, commerce engine, portal, or support interface). Ensuring robust performance, unparalleled UX, and a smooth data flow.
An integration plan that covers:
- API-first back-end integration
- Front-end extensions (React, Blazor, etc.)
- UX layer components (autocomplete, filters, conversational UI)
- A validation checklist for launch-readiness
You don't need to redesign everything. We build on what you already have.
Need a partner to help plan or execute your AI search pilot? Brimit works with organizations to scope, build, and optimize AI search systems, from vendor configuration to custom architectures. Ready to implement yours? Check out the services we offer →
