Which AI search tools should an apparel brand compare?
Nobi, Algolia, HawkSearch, and Coveo are options for apparel teams evaluating search. Start with the searches your own customers struggle with: a color name that differs from your catalog, a specific fit, or a style that must be available in a particular size. A convincing demo should solve those problems using products you actually sell.
| Platform | When to evaluate it | Pricing approach | Apparel-specific buying question |
|---|---|---|---|
| Nobi | Semantic product search and shopper Q&A in one platform | Monthly message bundles from $25 for 500 messages; going over bills the month at the bundle that fits | Can it connect shopper language to your catalog's fit, fabric, color, and availability data? |
| Algolia | A team that wants control over the search implementation | Published usage plans and custom enterprise pricing | Which plan and implementation handle your required relevance and variant behavior? |
| HawkSearch | Search and merchandising control for complex catalogs | Core from $500/month; Premium from $850/month | Which AI features, attributes, integrations, and services are included? |
| Coveo | Search, product listings, and recommendations in one commerce project | Request pricing for the required scope | Who will implement catalog relationships, event tracking, and storefront interfaces? |
Disclosure: We build Nobi. This comparison uses vendor documentation and Nobi customer case studies, reviewed October 15, 2026. We have not tested all four tools against a common apparel catalog. Recommendations reflect the buying situations below, not an independent performance ranking.
What should an AI search tool actually do for an apparel catalog?
An apparel search engine needs to distinguish relevance from a loose association. "Wide-leg cropped trousers" should not be satisfied by any pair of trousers. "Blue dress in size 12" is not a good match when the dress exists but that size is sold out. And a question about washing instructions should use the garment's care information rather than guess from its appearance.
Those are useful evaluation requirements for any vendor. The quality of your product descriptions, variants, metafields, inventory, and size guides also matters. A search provider cannot reliably recover measurements or material details that your store has never supplied.
If shopper Q&A is the primary need, also compare the shopping-assistant experience for fashion and apparel. A buyer choosing a search replacement should first test the search box, results, filters, and purchase path.
1. Nobi
Evaluate Nobi when: Your shoppers describe clothes differently from your product titles and you want semantic site search plus an assistant grounded in your catalog and approved content.
Nobi supports search, conversational refinement, filters, and shopper Q&A. Its merchandising controls can boost, bury, slot, or filter products. Confirm the placements and rules you need if the project also changes existing collection pages; a catalog search deployment and a redesign of all collection merchandising are different scopes.
Apparel evidence: UNTUCKit reported a 17.1% conversion-rate lift and 21.3% more revenue per searcher in a two-month A/B test. Kilte reported a 21.7% conversion-rate lift over Shopify default search. Its catalog uses colors such as Cappuccino and Ivory, and its implementation includes search, collection filters, and product discovery. These customer results give you specific examples to inspect; they do not predict your store's lift.
Pricing: Nobi is sold in monthly message bundles, from $25 for 500 messages up to $2,999 for 100,000 messages. A search or a chat question each count as one message. At 10,000 searches and chat questions a month, the 10,000-message bundle costs $379. Above 100,000 messages a month, pricing is custom.
Before committing: Preview your actual products and difficult queries. Check size availability, ambiguous fit requests, missing measurements, and changes to seasonal inventory. The free dashboard preview requires no credit card; the live-site trial is a separate step after configuration, with 100,000 free messages during the 30-day trial.
2. Algolia
Algolia's plans offer different search and AI capabilities, with NeuralSearch listed under Elevate. It also provides merchandising tools.
Evaluate it when: Your team wants control over the implementation. Confirm the required plan, record and request counts, variant behavior, and who will maintain relevance. Do not assume the cheapest keyword-search plan includes every AI feature used in a demonstration.
3. HawkSearch
HawkSearch offers search and merchandising controls, including support for complex B2B catalogs. It also publishes a Shopify integration.
Evaluate it when: Your apparel catalog needs detailed product attributes, merchandising rules, or account-specific buying experiences. Ask for a demo of your actual size and fit filters.
Pricing: Its current pricing page lists Core from $500/month and Premium from $850/month, with custom Enterprise pricing. Some AI features are add-ons. Confirm those, usage allowances, and implementation costs before comparing the total with another vendor. Launch timing depends on your integration requirements.
4. Coveo
Coveo for Commerce includes search, product listings, recommendations, and merchandising tools. Its product data model supports products, variants, and availability channels.
Evaluate it when: You need those experiences together and have an implementation team. For Shopify, Coveo documents an app for setup and data integration, while stating that you still need to build the storefront commerce interface. Request pricing and an implementation scope for your needs rather than relying on a generic enterprise cost estimate.
Test these apparel searches before choosing a vendor
Take real examples from your search logs and support conversations. Run the same queries against each candidate with your catalog, then have someone who knows the products assess the results.
| Test query or question | What to check |
|---|---|
| "Cream knit sweater" | Does it find the relevant Ivory or Ecru products without including unrelated colors? |
| "Wide-leg cropped trousers under $100" | Are silhouette, length, category, and price respected together? |
| "Blue dress in size 12" | Is that size available in that color, rather than merely present somewhere in the product's variants? |
| "Machine-washable wool sweater" | Is the material and care claim supported by the product data? |
| "Does this run small?" | Does the answer cite your fit guidance or acknowledge that the information is missing? |
| A discontinued style or unavailable combination | Does it clearly explain the mismatch and label any alternatives? |
Include a popular exact product name as well. Improving conversational searches is not useful if ordinary product lookup gets worse.
Check the buying experience on your own storefront
Test the mobile results and filters, then open a product and add the intended variant to the cart. Change a product price or inventory record in your test environment and verify that search updates. Ask what your theme or headless frontend requires and who will own the integration after launch.
For a fashion team, seasonal work matters too. Demonstrate a new collection, a product exclusion, and a promotion rule. Check that the merchandising team can make its routine changes without depending on an unplanned engineering project.
Measure sales impact after relevance passes
Use a randomized comparison of eligible shopper traffic against your current experience. Measure conversion and revenue per eligible shopper during the same period, with search behavior and latency as diagnostics. Keep promotions and traffic sources comparable. A small number of purchases or a seasonal change can make an apparent lift unreliable, so agree on a sample and measurement plan before switching all traffic.
Frequently asked questions
What should an AI search tool understand in an apparel catalog?
Evaluate product type, fit, fabric, color, price, and size availability together. The search must use the data your store actually supplies, distinguish an available product from an available variant, and avoid making unsupported sizing or material claims.
Can AI search recommend the right clothing size?
It can help shoppers find stated size and fit information when that information exists in the catalog or size guide. Finding an available size is different from guaranteeing that it will fit a particular person. Test how the tool handles missing measurements and conflicting size information.
What does Nobi cost for an apparel store?
Nobi is sold in monthly message bundles, from $25 for 500 messages up to $2,999 for 100,000 messages. A search or a chat question each count as one message. At 10,000 searches and chat questions a month, the 10,000-message bundle costs $379. Above 100,000 messages a month, pricing is custom.
How should we test AI search on Shopify?
Preview the same real shopper queries against your catalog, verify variant availability and mobile filters, and test product updates and add-to-cart behavior. Confirm theme or headless integration work before launch. Use a randomized live comparison to measure conversion and revenue rather than assuming better-looking results will increase sales.
See how Nobi handles your apparel catalog
Bring your storefront URL and a few searches your current tool misses. Start by checking those products and variants before deciding whether to change your live site.
You can preview Nobi free in the dashboard, estimate your monthly cost, or request a demo for your apparel store.