# How to Let Shoppers Find Products in Their Own Words on Salesforce Commerce Cloud
> Salesforce Commerce Cloud's keyword search misses descriptive queries. Add Nobi to resolve natural-language searches from your connected catalog.
_Source: https://nobi.ai/blog/natural-language-search-salesforce-commerce-cloud_
## Can I add natural-language search to Salesforce Commerce Cloud without replacing the platform?
Shoppers rarely search using the exact words in your product titles. They type "warm hiking jacket under $200," "gift for a trail runner," or "does this run narrow" - plain descriptions of what they're after. Salesforce Commerce Cloud's built-in search matches on the words in your catalog, so a descriptive query like those can come back empty: no error, no suggestion, just a results page with nothing on it. The good news is you can close that gap without leaving SFCC. Nobi adds semantic search directly to your storefront so descriptive queries resolve from your connected catalog. Here's how to connect it, configure it for spec and compatibility questions, and track the improvement in the dashboard.
## Why do descriptive searches come up empty on Salesforce Commerce Cloud?
Salesforce Commerce Cloud's built-in search matches on keywords - the exact words a shopper typed against the words in your product titles, descriptions, and attributes. So when a shopper types "warm hiking jacket under $200" and your catalog says "men's insulated shell" or "mountain parka," the engine finds no overlap and returns a [zero-result page](/blog/fix-zero-result-searches). The shopper usually just moves on, and your analytics record a bounce rather than the vocabulary mismatch behind it.
This shows up more as shoppers describe what they want in plain terms. Queries like "gift for a trail runner" or sizing questions like "does this run narrow" don't map to the keyword patterns the engine recognizes. SFCC does give you synonym groups and boosting rules to close some of those gaps, and they help - but every new phrasing variation needs its own manual entry, so the list grows along with your catalog.
It's also easy to overlook, because there's no signal: shoppers who come up empty don't file a ticket, they just move on. And since many of them arrive from paid campaigns or email - often your highest-intent traffic - those empty results quietly work against the spend that brought them in.
## What is Nobi, and how does it handle natural-language search on Salesforce Commerce Cloud?
Nobi closes those dead ends. It's a conversational website assistant that adds semantic product search and automated shopper Q&A to your SFCC storefront - no platform migration, no native search widget to remove.
For natural-language search, the key is how Nobi reads queries. It uses semantic embeddings to map what a shopper means to products in your connected catalog, not the exact words in a product title. A shopper who types "warm hiking jacket under $200" against a catalog full of "men's insulated shell" and "mountain parka" listings gets results instead of a dead-end page.
The knowledge base builds from your existing SFCC content - product pages, PDPs, policy docs, FAQ routes, help-center articles, PDFs - with no manual re-entry or data migration required. You point Nobi at the content you already have, and it reads it where it lives. Connected sources refresh twice a day, so a pricing change or availability update lands in shopper answers within hours of the catalog update.
Every conversational answer carries an inline citation pill back to the exact source document, date, and excerpt it came from. A shopper can verify any claim against your own published content without leaving the chat. That matters because the failure mode ecommerce operators fear most - a confident AI answer built on stale or fabricated data - is exactly what grounded, cited answers make visible.
[UNTUCKit](/customers/untuckit) saw a 17.1% conversion rate lift over a two-month A/B test compared to their prior search tool.
Nobi starts at $25/month, including 2,500 searches and 250 conversational messages. Beyond that, $0.01 per additional search and $0.10 per additional message.
## How does semantic search map a shopper's words to the right products in my SFCC catalog?
That lift comes from how the matching works. Nobi converts a shopper's query into a vector embedding - a numerical representation of meaning - and compares it against embeddings derived from your catalog. When a shopper types "warm jacket for hiking under $200," products that are semantically close to that phrase surface even if no individual words overlap. A title like "men's insulated shell" or "mountain parka" scores as a match because the meaning aligns, not because the words do.
The semantic layer also reads your catalog's structured attributes - material, activity type, color, price range - as part of the match. A shopper who types "$200 hiking jacket" doesn't need to touch a facet to narrow results by price. The query handles it. Spec and use-case searches work the same way: "best option for wide feet" or "works in temperatures below 20°F" routes through the same pipeline, pulling from connected product and policy content rather than a keyword index.
The practical win for your merchandising team is that synonym groups stop being a maintenance job. Right now, every alternate phrase a shopper uses - "down coat" vs "insulated jacket," "waterproof" vs "weatherproof" - needs its own rule entry to close the gap in a keyword engine. Nobi handles phrasing variation by design. The semantic model understands that "insulated shell" and "warm jacket" describe the same thing, so you don't need to write a rule for every alternate phrasing a shopper might try.
[Kilte](/customers/kilte), a DTC fashion brand, saw a +21.7% conversion rate lift over Shopify's default search in an A/B test after switching to Nobi. The same semantic matching applies regardless of which storefront platform you're running.
## How do I install Nobi and connect my Salesforce Commerce Cloud catalog?
Getting that semantic layer running on your SFCC storefront is a small theme tweak - no cartridge development, no custom builds required. A developer makes two additions to your storefront template: one that loads the Nobi runtime across every page, and one that places the search widget where you want it. Most SFCC stores are live within hours.
Connecting your catalog comes next. Point Nobi at your product feed URL or upload a CSV or JSON export from SFCC. Nobi parses titles, descriptions, attributes, pricing, and availability from that feed and builds the semantic index. Policy docs, FAQ pages, and help-center content add as additional sources via URL or PDF upload - no manual re-entry into a separate system. All source types index through the same pipeline - no separate system to manage for catalog content versus policy docs.
For headless SFCC deployments, Nobi's API accepts catalog data via direct feed. The semantic matching works the same regardless of your frontend stack.
For the search widget itself, you have two options:
**Option 1 - Replace your existing search form with Nobi's search bar:**
```html
```
**Option 2 - Add an AI mode toggle to your existing form:**
```html
```
The `input-selector` attribute identifies your existing search field, so Option 2 intercepts queries without replacing what you already have. Both options include a toggle that lets shoppers switch between standard and AI-powered search.
Once the catalog is indexed, contextual suggestion pills appear automatically on each page - prompts scoped to wherever the visitor is on your site, displayed as tappable buttons. [UNTUCKit](/customers/untuckit) asked for developer-controlled customization over how Nobi displayed products; that request became the Hooks API, now available to every Nobi customer.
## How does Nobi handle spec, compatibility, and use-case queries without fabricating answers?
That install puts a live semantic layer on your storefront - but the harder test isn't "warm hiking jacket." It's "does this jacket perform below 20°F?", "is the zipper YKK?", and "what's the return window on final-sale items?"
Those are the queries where a wrong answer does real damage. A shopper who buys based on a fabricated spec doesn't just return the item - they file a chargeback and leave. Spec and compatibility questions are the highest-risk queries for any AI tool because a confident wrong answer is worse than no answer at all.
Nobi handles these by pulling answers only from the sources you've connected - product pages, spec sheets, PDFs, policy docs. If the answer isn't in what you've connected, Nobi says so rather than guessing.
Every answer includes inline citation pills: the source document name, date, and the exact excerpt the answer came from. A sources sidebar lists every reference with direct links, so shoppers can verify any claim against your official content without leaving the chat.
For questions where variation itself creates risk - return policy, warranty terms, size run disclaimers - you can pin an exact merchant-approved answer. That question fires the same response every time, word for word, with no AI paraphrasing.
There's also an optional second AI review. Before an answer sends, a separate model checks the draft against the raw cited content and flags inaccuracies. It's on by default for high-consideration industries and toggleable per merchant.
For catalogs where technical details close the sale, grounded answers with verifiable citations change what's possible. [Lucchese](/customers/lucchese), the luxury Western boot brand, attributed $1M+ in incremental revenue in year one and a 39x ROI running Nobi across search and PDP - a catalog where material details and craftsmanship specs are what close the sale.
## How do I track zero-result rate reduction in the Nobi dashboard after go-live?
Nobi's dashboard surfaces zero-result queries, search-to-cart rates, and revenue per searcher - the metrics that connect search performance directly to CVR and revenue. For SFCC merchants, the leading indicator to watch after go-live is zero-result rate: the share of search sessions that return no products. As descriptive and long-tail queries that previously hit dead ends start resolving, that rate drops and revenue per searcher rises.
Track zero-result rate week over week from your launch date. A well-indexed catalog typically shows a measurable drop within the first two weeks as long-tail queries begin resolving. The semantic layer handles phrasing variation by design, so queries that used to land on dead-end pages - "warm waterproof shell," "slim tapered chino in olive" - start returning results instead.
Revenue per searcher is the metric that ties search quality directly to top-line impact. [UNTUCKit](/customers/untuckit) saw +21.3% revenue per searcher ($39.17 vs. $32.30) after switching to Nobi's semantic search in a two-month A/B test. That number came from shoppers finding what they were actually looking for, not from merchandising overrides or promotional boosting.
Watch search-to-add-to-cart rate alongside it. A falling zero-result rate means fewer dead ends; a rising add-to-cart rate confirms shoppers are acting on what they find. If zero-result rate drops but add-to-cart stays flat, the results page is returning products but not the right ones - a signal to revisit catalog content or attribute completeness.
Zero-result queries that persist after indexing usually point to a catalog content gap, not a search configuration problem. The dashboard surfaces exactly which queries are still coming up empty, so you can route them to the merchandising or copy team rather than guessing. UNTUCKit runs that review in their standing weekly meeting, using zero-result data and search trends to inform catalog decisions on a rolling basis.
## When does a different search tool make more sense than Nobi for a Salesforce Commerce Cloud store?
Those metrics - zero-result rate, revenue per searcher, add-to-cart lift - are exactly what Nobi is built to move. But there are two cases where a different tool is the more honest recommendation.
If your team has a dedicated search engineer who wants full control over ranking logic, [Algolia](https://www.algolia.com) is the better fit. Every relevance parameter - custom ranking strategies, query rules, synonyms, merchandising configs - lives in code. One caveat worth knowing before you compare costs: Algolia's semantic layer (NeuralSearch) is only available on the Elevate plan. Build, Grow, and Grow Plus are keyword-matching only, so the semantic capability that would compete with Nobi sits at the top of their pricing stack.
If you're consolidating search, CMS, and customer data into one enterprise contract - and prepared for a rollout measured in quarters - [Bloomreach](https://www.bloomreach.com)'s Discovery module integrates deeply with SFCC at that scale. The Conversational Shopping module connects to the SFCC customer data layer and runs across search, email, and CMS in one contract. The tradeoff is six-figure annual pricing and implementation timelines to match.
Two limits worth flagging on Nobi's side before you decide: behavioral reranking based on individual shopper click and purchase history is not yet available, and Nobi curates the search results page, not category or collection pages across the full site. Teams whose headline requirement is session-level personalization or full-site merchandising will need to factor those gaps in.
Descriptive search queries are where keyword engines break down. Connect your Salesforce Commerce Cloud catalog to Nobi and start resolving the ones your built-in search misses. [Try Nobi free](https://dashboard.nobi.ai).