AI Search for Your Website
Semantic site search understands what a visitor means rather than matching the words they typed. Someone searching "something for a leaking tap" finds the right products even though no listing contains that phrase. It replaces the keyword search that returns nothing for most of the queries real people type.
Who it's for
Large catalogues, documentation sites, knowledge bases.
What changes
People find things, which is most of what conversion is.
- Starting at
- ₹6,00,000
- Timeline
- 10–20 weeks
- Category
- AI Web Apps & SaaS
- Built from
- Vashi, Navi Mumbai
Key takeaways
- A large share of site searches return zero results — those are visitors leaving.
- Semantic search finds products by meaning; keyword search finds them by luck.
- Hybrid search, combining keyword and meaning, outperforms either alone.
- Zero-result and click-through rates are the two numbers that prove it worked.
- Deploys in 4–8 weeks from ₹2,00,000.
What is wrong with the search you have
Most site search matches the visitor's words against product titles and tags. It works when the visitor happens to use the same words you did.
They usually do not. They describe the problem rather than the product, they misspell, they use the regional or colloquial term, they type a whole sentence. Each of those returns nothing, and a visitor who searched and got nothing leaves at a much higher rate than one who never searched.
Search users are also your most valuable traffic — someone who searches has intent. Failing them is expensive in a way that a mediocre category page is not.
What semantic search handles that keyword search does not
The gap is easiest to see in examples from actual site search logs.
| Visitor typed | Keyword result | Semantic result |
|---|---|---|
| something for a leaking tap | Nothing | Tap washers, plumbing tape |
| gift for father who likes cooking | Nothing | Cookware, knife sets |
| chappal | Nothing if listed as sandals | Sandals |
| cheep laptop bag | Nothing (misspelled) | Laptop bags, price sorted |
| wireless earphone under 2000 | Partial | Filtered by price |
| kurti for office | Weak | Formal kurtis |
Why hybrid beats pure semantic
Semantic search has one weakness: exact matches. Someone searching a product code, a specific model number or an exact brand name wants that item, and a meaning-based system may return conceptually similar things instead.
So the right architecture runs both. Keyword search handles exact identifiers and precise terms; semantic search handles descriptive and conversational queries; results are merged with weighting tuned to your catalogue.
We also add a re-ranking pass over the merged results using business signals — stock availability, margin, popularity — because the most semantically relevant item is not always the one you want at the top.
Beyond the search box
Once meaning-based retrieval exists over your catalogue, several other things become straightforward.
- Related products that are genuinely related. Similarity by meaning rather than by shared category tag.
- Better category pages. Products surfaced by relevance to the category concept, not just tag membership.
- Search-as-you-type suggestions that reflect intent rather than prefix matching.
- Support content retrieval. The same index over your help articles answers questions without a chatbot.
- Query understanding. Extracting filters from natural language — "under ₹2,000", "in blue", "for a 10-year-old" — and applying them automatically.
What determines the quality
The search is only as good as the text it indexes. A catalogue where products have a title and nothing else gives the system very little to reason about.
This is why search projects and catalogue enrichment often run together. Adding attributes and descriptions improves search more than any tuning does, and clients with thin product data are usually better served by fixing that first.
The second factor is your own vocabulary. Regional terms, category names your customers use that you do not, and product synonyms all need to be in the index. We extract these from your search logs rather than guessing them.
Implementation and cost
Four to eight weeks from ₹2,00,000: search log analysis, index build over your catalogue, hybrid retrieval and re-ranking, front-end integration and tuning against real queries.
Running cost is modest — a vector index over a typical catalogue plus query embedding runs a few thousand rupees a month at ordinary traffic levels.
You get the search service on your infrastructure, the indexing pipeline that keeps it current as products change, an analytics view of what people search for, and full source.
FAQ
AI Search for Your Website — your questions
How do we know it is better than what we have?
Two numbers, measured before and after: the proportion of searches returning zero results, and the click-through rate on search results. Both are available in your analytics today. Run the new search on half your traffic for a month rather than switching everyone, so seasonality and other changes do not confuse the comparison. In most deployments the zero-result rate falls sharply and that alone justifies the work.
Does it work in Hindi or mixed-language queries?
Yes, and this is a genuine advantage over keyword search. A customer typing a Hindi word in Roman script, or mixing Hindi and English in one query, gets nothing from keyword matching and sensible results from semantic search. Your catalogue can remain in English; the matching happens at the meaning level. For sites with substantial non-metro traffic this is often where the largest improvement shows up.
How fast is it?
Under 100 milliseconds for the retrieval on a typical catalogue, which is imperceptible. Query embedding adds a small amount, mitigated by caching common queries. Total response time is normally comparable to your existing search and often better, since keyword search over a large catalogue with many filters is not always fast either. We measure this during the build against your actual traffic patterns.
Can it work with our existing platform?
Usually. Shopify, WooCommerce, Magento and most custom platforms can have their search replaced at the front end while the platform continues handling everything else. The integration is a product feed into the index and a change to the search results page. Where a platform's search is deeply embedded in its theme, the work is a little more involved but still measured in days rather than weeks.
What happens when we add new products?
They are indexed automatically. The pipeline watches your product feed and updates the index on a schedule or on change, typically within minutes. Stock status is handled the same way, so out-of-stock items can be demoted or hidden without a reindex. This is configured during the build so nobody has to remember to run anything.
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Next step
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