AI search solutions for relevant answers
Our AI search solutions help customers and staff find the right result, even when their words differ from yours. We pair semantic retrieval with exact matching and test both on your real queries, so exact lookups keep working.
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What is AI or semantic search?
AI search, or semantic search, matches queries to content by meaning rather than exact keyword overlap. It uses embeddings to represent both the query and the content numerically. As a result, a search for “waterproof jacket” matches a product described as “rainproof shell”. Our AI search solutions use hybrid search, which combines this with traditional keyword matching so exact identifiers still work.
Zero results for things you definitely sell
Search analytics in most organizations tell the same story: a meaningful share of queries return nothing useful. Moreover, a large share of those are for content that exists under different wording.
On an e-commerce site, that costs revenue you can measure. Internally, it wastes time you cannot measure, which is why internal search stays broken far longer.
Hybrid search, tuned against real queries
Semantic search matches meaning, so a query and a document that share no words can still match. On its own it has a weakness: it is poor at exact identifiers like part numbers and SKUs.
So we build hybrid: semantic and keyword search run together with the results fused and reranked. And we tune it against your real query logs with measured relevance, rather than against intuition.
What our AI search solutions include
The first thing we do is measure what you have. Most teams have never quantified their search relevance, which means they cannot tell whether a change helped.
We build a relevance evaluation set from your actual query logs, with human-judged correct results. That gives a baseline number. We then score every subsequent change against it, so tuning becomes engineering rather than guesswork.
Then come the improvements AI search solutions need: hybrid retrieval, reranking, synonym and vocabulary handling, and faceting that stays fast. Query understanding also handles typos, plurals and intent classification.
Our AI-powered search solutions combine semantic search solutions with keyword matching. These hybrid search systems use vector search integration to find meaning, while still matching exact part numbers and codes.
- Relevance baseline measured from real query logs with human judgments
- Hybrid retrieval: semantic embeddings fused with keyword matching
- Reranking to reorder candidates by genuine relevance
- Query understanding: typo tolerance, intent detection, entity extraction
- Fast filtering and faceting that does not degrade with catalog size
- Continuous relevance monitoring that catches quality drift
Is AI search right for your business?
For e-commerce, the case for AI search solutions is direct: search users convert at a much higher rate than browsers. Search relevance is therefore a revenue lever rather than a usability nicety.
For internal and documentation search, the case is time. Suppose people routinely give up on search and ask a colleague. Then the search box is costing two people’s attention per query rather than none.
Enterprise AI search helps staff find answers across internal content. AI document search and natural language enterprise search let people ask questions in their own words instead of guessing the right terms.
- E-commerce sites with measurable zero-result or no-click search rates
- Product catalogs where customers and copy use different vocabulary
- Documentation sites where users cannot find existing answers
- Internal search that staff have learned to bypass entirely
- Marketplaces where relevance directly drives conversion
- Any search where nobody can say whether relevance is good or bad
What AI search can do for your business
Fewer dead-end searches
Semantic matching finds content described differently from how the user asked, which is the largest single cause of zero results.
Exact lookups still work
Hybrid search preserves precise SKU, part number and identifier matching that pure semantic search degrades.
Relevance you can measure
A scored evaluation set means you measure tuning rather than argue about it in meetings.
Conversion impact
For commerce, search users convert substantially better than browsers. As a result, relevance gains from AI search solutions show up in revenue.
Search that stays fast
Filtering and faceting designed to hold latency as the catalog or corpus grows.
Drift detected early
Continuous relevance monitoring catches degradation from content changes before users complain.
Problems AI search solves
High zero-result rate
Queries returning nothing for content that exists. Semantic matching resolves most vocabulary mismatches.
Customers use different words
Product copy written in industry terms, customers searching in plain language. Embeddings bridge the gap.
Part numbers stopped matching
A semantic-only migration breaking exact lookups. Hybrid retrieval restores precision without losing recall.
Filtered search too slow
Facets causing timeouts at scale. Index and pre-filter design fixes what more hardware will not.
No relevance measurement
Changes shipped on intuition. An evaluation set makes improvement provable.
Staff bypassing internal search
People asking colleagues instead of searching. Better relevance changes the habit.
What every AI search engagement includes
Everything below is in scope on a standard engagement. None of it is an upsell we reveal halfway through the build.
Query log analysis
Zero-result, no-click and refinement rates analyzed to identify where search is actually failing and how often.
Relevance evaluation set
Real queries with human-judged correct results, giving a baseline score to measure every change against.
Hybrid retrieval
Dense semantic and sparse keyword search fused with reciprocal rank fusion, tuned for your query mix.
Reranking
Cross-encoder reranking of top candidates, typically the largest single relevance improvement available.
Query understanding
Typo tolerance, plural and stemming handling, entity extraction, and intent classification to route queries appropriately.
Faceting and filtering
Fast pre-filtered search across attributes, designed to hold latency as the corpus grows.
Personalization hooks
Optional ranking signals from user history, segment or context, applied transparently and switchable.
Relevance monitoring
Ongoing scoring against the evaluation set plus live click and conversion signals, with alerting on regression.
Which AI models and tools do we use for AI search?
We are not tied to one vendor. Instead, accuracy, cost per task, latency and where your data may live decide which models and infrastructure we use.
How we deliver AI search, step by step
Our AI search solutions follow the same five stages on every engagement. Nothing changes until we have a measured baseline.
Discovery
First, we analyze zero-result, no-click and refinement rates in your query logs to see where search fails.
AI Strategy
Next, we build a relevance evaluation set from real queries and rank fixes by expected relevance gain.
Pilot Build
Then we pilot hybrid retrieval and reranking as a fixed-scope build, scored against the baseline.
Implementation
After that, we A/B test the new search against the old on live traffic instead of a hard cutover.
Optimization
Finally, relevance monitoring tracks the evaluation set and live click signals, alerting you to any regression.
How is an AI search project priced?
AI search solutions are priced on the size of your catalog or content, the systems behind it and the relevance you need to reach. You get a fixed written price before we start, measured against how your search performs today.
Book a free 30-minute call and share a few searches that fail today. We will show you why they miss and how much better results could be.
- A written savings estimate before any paid work
- Fixed scope and fixed price, agreed up front
- Full ownership of everything we build for you
- An honest recommendation when the numbers do not work
Figures are internal measurements across recent engagements, reported to every client monthly in writing.
AI search FAQs
What is the difference between semantic and keyword search?
Keyword search matches literal words, so a query only finds documents containing those exact terms. In contrast, semantic search converts both query and content into numerical representations of meaning. That way, “waterproof jacket” can match “rainproof shell” despite sharing no words. Semantic search is better at vocabulary mismatch and worse at exact identifiers. That is why hybrid search combining both is usually the right answer.
Will semantic search break our part-number lookups?
On its own, frequently yes, and this is the most common reason teams revert semantic search migrations. Embeddings represent meaning, and a part number has no meaning to embed, so exact matching degrades. Hybrid retrieval avoids the problem by running keyword matching alongside semantic search and fusing the results. That keeps precision on identifiers while gaining recall on descriptive queries.
How do you know whether search actually improved?
We build a relevance evaluation set from your real query logs with human-judged correct results. It establishes a baseline score before any change. We then score every subsequent change against it. Without this, search tuning is people arguing about anecdotes. It is remarkable how many organizations ship search changes with no way to tell whether they helped.
How much can we expect search relevance to improve?
It depends heavily on your starting point, and we will not quote a number before measuring. Sites with high zero-result rates and vocabulary mismatch between customers and content typically see large gains. By contrast, sites with already-good keyword search and tightly controlled vocabulary see modest ones. The two-week audit gives you a baseline and a realistic projection before committing to a rebuild.
Do we need to replace our existing search platform?
Usually not. Elasticsearch and OpenSearch both support vector search natively, so we can often add hybrid retrieval to what you already run. Where you genuinely need a replacement, we will say so. However, adding a semantic layer to an existing search cluster is the more common and much cheaper path.
How long does a search rebuild take?
A two-week audit produces the baseline and plan. A full hybrid rebuild with reranking, query understanding and monitoring typically takes six to eight weeks. We usually recommend A/B testing the new search against the old on live traffic rather than a hard cutover. That way, your actual users prove the improvement.
Ready to scope your AI search solutions project?
Book a free 30-minute consultation. Bring one workflow. You will leave with a realistic estimate of what it would cost to automate and what it would save.