Every solution maps to a real storefront problem. Here is how ParticleSearch improves storefront search, catalogue freshness, and merchant visibility.
Keep the search interaction responsive while a shopper is deciding.
Carry catalogue, price, and stock changes into the search experience.
See failed queries, apply a focused change, and verify the result.
Choose the task that matches your store. Each path explains what to inspect and points to the product page that covers that decision in more detail.
Some searches return nothing. Others show products shoppers skip.
How it helps
Review searches with no results and see which products shoppers click. When order tracking is available, you can also see orders linked to search.
Some searches keep missing products or put the wrong ones first.
How it helps
Try a synonym, redirect, or ranking change, then preview the results before publishing.
Shoppers may know the SKU or option, but not the product title.
How it helps
Search those details when they are included in your catalogue, including model numbers and product options.
How to read the solutions
A fast search engine cannot repair a missing variant, and a perfect catalogue cannot help if the result is ranked or presented poorly. ParticleSearch connects the layers so your team can find the responsible boundary before changing anything.
Is the product current, published, available, and represented at the level a shopper is searching for?
Can the query reach the right product, and can your team correct a known gap without changing unrelated results?
Can you see what failed, make a bounded change, and confirm that the shopper experience improved?
Use the cards as a map of possible interventions, not a checklist. Start with a search issue you can reproduce and choose the smallest change that addresses it.
Responsive
storefront search
ParticleSearch measures product retrieval separately from network and rendering time. Check the full search experience on your own theme and device.
Traceable
catalogue freshness
Review product edits, price changes, and stock updates through their sync state and storefront check when a result looks out of date.
Recovered
messy queries
Offer a corrected search path for common misspellings when the catalogue has a relevant match, so a small typing mistake is less likely to end the search.
Beyond titles
deep indexing
Use eligible variant options, SKUs, tags, and product details alongside titles, so shoppers can search with the information they already have.
Policy-led
eligibility
Draft and storefront-invisible products are excluded before results reach shoppers. Sold-out behaviour follows the merchant's configured policy, so the team can choose whether those products show, move down, or disappear.
Visible
search gaps
Review searches with no results, low engagement, or related catalogue sync gaps in the dashboard, then decide whether to investigate product data, query handling, or result order.
Exact
part number match
Professional buyers search by SKU or part number. When an eligible identifier matches, search can surface the relevant product or variant without forcing a manual catalogue browse.
Structured
browse refinement
Full-page search gives shoppers facets, price ranges, and sort controls. Results go from a broad set to the right product without starting a new search.
Shorter
path to cart
When a shopper already knows what they want, they can add to cart directly from the search result and keep moving. One less page load per purchase.
Guided
setup path
Use Shopify's app flow, let the catalogue prepare, enable the theme app embed, and verify a real storefront query. No manual Liquid integration is required for the supported delivery path.
Smarter
intent matching
Hybrid vector and keyword search matches shopper intent, not just exact words. “water resistant jacket” still finds the right product even when the title says “rain shell.”
Manual
ranking control
Boost, pin, or hide products per query. Priority items, seasonal promotions, and clearance sales get the visibility they deserve, without a code deploy.
Adaptive
filter profiles
Different collections get different filter profiles. Apparel gets size and colour. Electronics gets specs and brand. Filters match the shopper’s context, not a one-size-fits-all list.
Ranked
fix queue
Search surfaces a ranked list of issues: zero-result queries with traffic, ignored results, filter dead-ends. Each one links directly to the fix surface so nothing gets lost.
Observed
orders linked to search
On Scale, with tracking set up, review orders linked to search activity. Use the report to investigate shopper journeys, not to claim that search caused extra sales.
Measured
discovery evidence
Seven recommendation strategies can support comparison, basket completion, and open-ended discovery across supported placements. Eligible plans can report views, clicks, errors, experiment activity, and orders linked to recommendation activity. These are observed signals, not proof recommendations caused an order.
Tested
before rolling out
Run controlled comparisons on search ranking and recommendations. Assign traffic, compare variants, and review clicks, add-to-carts, and available order matches with sample and evidence limits in view.
Unified
product + content results
Blog articles, size guides, and Shopify pages appear in search results alongside products. Shoppers find your guides and policies without leaving the search bar.
Scheduled
campaign rules
Schedule seasonal promotions, campaign banners, and curated product placements with audience targeting. Rules activate and expire automatically, with no theme edits or code deploys.
6 profiles
one-click presets
Six pre-built search profiles, including Balanced, Precision, Discovery, Variant-rich, Technical, and Content-rich, each tuned as a complete ranking, typo tolerance, and recovery profile. Switch in one click.
Protected
publish safety
Use protected golden queries to check the changes your team chooses to guard, review before-and-after results, publish deliberately, and restore a previous state when needed.
Ready
idle discovery
Before shoppers type a query, search shows recent products, trending items, category shortcuts, and suggestion chips. Discovery starts from the first tap, not after the first query.
Worked decisions
Start with what the shopper tried to do, identify the first broken handoff, and choose the smallest intervention that can repair it. These examples show why a feature checklist is not a diagnosis.
Observed symptom
What it means
The buyer has supplied identity, not a broad product idea. The useful path is exact field coverage and variant preservation. Fuzzy or meaning-led recovery can turn a near identifier into a dangerous false answer.
Responsible intervention
Confirm the SKU on the intended variant, searchable catalogue delivery, and the active storefront request. Repair field or variant coverage before changing ranking.
Evidence of success
The exact SKU returns the expected product and variant, a near-collision does not displace it, and the card, product page, and cart preserve the same sellable item.
Observed symptom
What it means
The product family may be relevant while its visible variant contradicts the query. This is a handoff problem between matched evidence and presentation, not necessarily a failure to retrieve the parent product.
Responsible intervention
Keep the matched variant identity in the response and render its image, option, price, availability, and destination. Use ranking only if the wrong product family is also winning.
Evidence of success
The navy evidence is visible before the click, the destination opens the navy option, and unavailable colour combinations are not implied by the parent product.
Observed symptom
What it means
A live Shopify page proves storefront publication, but not that the search catalogue, active index, and installed search runtime all received the same revision.
Responsible intervention
Trace one launch product from Shopify change through sync, searchable document, runtime acknowledgement, and a live smoke query. Do not add synonyms to hide a freshness failure.
Evidence of success
The expected revision reaches the live search path within the store-owned freshness boundary, while an unchanged control product still behaves normally.
Observed symptom
What it means
Retrieval has cleared only the first hurdle. The first visible products may be poorly ordered, missing decision evidence, unavailable, difficult to filter, or inappropriate for the query job.
Responsible intervention
Inspect the live result set as a shopper, then change the smallest responsible layer: catalogue data, card information, filters, a focused ranking rule, or a controlled layout or strategy experiment.
Evidence of success
The intended query family improves on its primary shopper outcome while protected exact queries, availability, latency, errors, and downstream handoff remain acceptable.
A useful solution leaves a narrower explanation. After the change, the team should know which layer was responsible, why that intervention fit, which buyer job improved, and which nearby behaviour was protected. If the evidence cannot say that, the change may have moved a metric without teaching the store what worked.
Search and store performance
Review connected orders, investigate searches that reach a dead end, and model the economics with your own margin.
See every detail
See the complete breakdown of what ParticleSearch does on your store today.
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