Search audit
A look at where your current search actually fails before recommending a rebuild.
Overview
Keyword search breaks down exactly when your catalog gets interesting
Traditional keyword search matches exact words, which works fine for a small catalog and starts failing as content or product count grows: searches for "warm jacket" miss a product literally called "insulated coat," and a content library returns nothing for a question phrased slightly differently than the article title.
Semantic search using vector embeddings matches on meaning rather than exact wording, which handles both of those cases naturally. We build this on top of your existing catalog or content, along with recommendation logic that surfaces genuinely related items rather than just "other things in the same category."
Tools & platforms we use
- vector databases
- embeddings APIs
- Algolia
What's included
Search and recommendations tuned to your actual content
Semantic search implementation
Vector-based search that matches on meaning, built against your real catalog or content.
Recommendation engine setup
Suggestions based on genuine relevance, not just shared category tags.
Relevance tuning
Refined against real user queries after launch, since relevance is rarely perfect on the first pass.
Questions
Common questions about AI search projects
4 questions
How is this different from the search we already have?
Most existing search is keyword matching, which requires the search term to closely match the product or content's actual text. Semantic search matches based on meaning, so it handles synonyms, typos and rephrased questions that keyword search misses entirely.
Does this work with our existing catalog or CMS?
Yes, we build the embedding pipeline against your actual data source, whether that's a CMS, a product catalog, or a document library, rather than requiring you to migrate content somewhere new.
Is this worth it for a smaller catalog?
Sometimes not. For a catalog under a few hundred items, well-built keyword search often performs fine, and the added complexity of semantic search may not pay off. We'll tell you honestly if that's the case for your specific catalog.
How much does this cost to run ongoing?
There's a usage-based cost for generating and querying embeddings, which scales with your catalog size and search volume. We'll estimate this against your actual numbers before you commit.
Quick question?
Ask us about Intelligent Search & Recommendations
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Ready to get started?
Tell us where your current search falls short.
Send a few examples of searches that return bad results today, and we'll come back with a fixed estimate for fixing it.
If the first milestone doesn't match the brief, we'll revise it at no extra cost.
NDA signed before we see anything. Delivered under your brand.