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 Search, 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.
Continue exploring
How do you measure your AI search pilot and scale with confidence?
Read From Pilot to Performance: Measuring and Executing Your AI Search Rollout to see how product, marketing, and tech teams structure their AI search metrics—and the five steps to move from a validated pilot to production.
