★★★★★
The automation was set up in a clean and practical way by the Checkout Loom team. It saved me a lot of manual work and made the whole process much easier.
JanoUnited Kingdom
Verified
AI ecommerce search personalization
AI ecommerce search personalization that understands what shoppers mean and puts the right product first
AI ecommerce search personalization starts with the awkward fact that shoppers who use site search are the shoppers closest to buying, and that on many stores a large share of their queries return nothing useful because the search matches words and not meaning. We study your real queries, fix zero-result searches with synonyms and attribute understanding, add semantic search for descriptive queries, rank results by relevance and profit, personalise by behaviour where consent allows and report what shoppers search for, so search becomes a conversion tool and a source of catalogue insight.
- Fixed price from $2,640, paid once
- Zero-result searches found and fixed first
- Personalisation only where consent allows
- Platforms supportedShopify with Search and Discovery and the Storefront API, WooCommerce, BigCommerce and headless storefronts, with Algolia, Klevu or a custom search service.
- Typical storeStores with a few hundred to many thousands of products, a visible search box and shoppers who search by attribute, use or brand.
- Signals usedSearch queries, clicks, add-to-carts, orders from search, catalogue attributes and, with consent, shopper behaviour.
- Storefronts coveredOne storefront per plan, with extra storefronts and languages available as priced options.
- OutputsSynonym and rules sets, attribute mapping, semantic search, ranking, personalisation, analytics and a tested rollout.
- Live inFive to seven weeks for the first improvements and up to ten weeks for semantic search and personalisation with testing.
The costly disconnect
Why default site search loses sales from the shoppers most ready to buy
A visitor who types into the search box has told you what they want, and when the search returns nothing, the wrong product or a list sorted by an accident of the catalogue, the sale goes to a competitor whose search understood them.
Zero-result searches
Common queries return nothing because the shopper used a word your catalogue does not, such as backpack for a store that sells rucksacks, and the shopper leaves. The fix is not more effort but a better rule, and that is exactly what the automation build for ai ecommerce search personalization puts in place.
Keyword matching without meaning
A search that matches words cannot tell that a waterproof twenty litre bag for hiking is the same need as a hiking daypack, so descriptive queries fail. Many owners only notice this problem when a customer complains, so catching it earlier is one of the quiet benefits of automation for ai ecommerce search personalization.
Ranking by chance
Results ordered by title match or date show a poor fit first and a best seller last, so the best product is buried. Left alone, this problem repeats with every order, which is why automation for ai ecommerce search personalization is built to handle it by rule and not by memory.
The same results for everyone
A returning buyer of trail gear and a first-time gift buyer see identical results, though their intents differ. It is easy to overlook until a busy week exposes it, and by then the cost to ai ecommerce search personalization is already counted in refunds, hours or lost sales in the automation work.
Typos and plurals
Misspellings and word forms are common on phones, and search that does not forgive them loses mobile shoppers first. The fix is not more effort but a better rule, and that is exactly what the automation build for ai ecommerce search personalization puts in place.
Nobody reads the search log
The queries shoppers type are a free survey of demand, and without reporting they never reach merchandising, content or buying. Many owners only notice this problem when a customer complains, so catching it earlier is one of the quiet benefits of automation for ai ecommerce search personalization.
- -62%zero-result searches is a typical fall in our builds after synonyms and attribute mapping, which is indicative and varies by catalogue.
- 2search methods work together in our builds, keyword matching for exact terms and semantic matching for descriptive queries.
- 1report of top zero-result and low-conversion queries goes to merchandising every week.
Sources: Shopify Search and Discovery and Predictive Search API documentation; Algolia and Klevu documentation on relevance and synonyms; results come from catalogues we have worked with and are indicative.
What changes in your site search once it understands meaning, ranks well and learns
Shoppers find the right product from the first query, and the search log guides merchandising.
How search works today
- Common queries return nothing.
- Descriptive searches fail.
- Results are ranked by accident.
- Everyone sees the same list.
- The search log is never read.
How search works afterwards
- Zero-result queries are found and fixed with synonyms and attribute mapping.
- Semantic search understands descriptive and intent-based queries.
- Ranking blends relevance, availability and profit under your rules.
- Results adapt to the shopper’s behaviour where consent allows.
- A weekly report lists top, failing and low-converting queries.
What the search report shows
- Fewer lost searches.
- Higher search conversion.
- Better first results.
- Relevant personalisation.
- Insight into demand.
What the search now does
- Correcting typos and word forms and applying synonyms.
- Understanding attributes and descriptive queries.
- Ranking by relevance, stock and profit.
- Personalising by behaviour where consent allows.
- Reporting zero-result and low-converting queries.
What the merchandiser controls
- Approving synonym and rule changes.
- Pinning and blocking specific products.
- Merchandising for campaigns and seasons.
- Fixing catalogue gaps the report reveals.
- Calling a search test once the planned traffic has arrived.
Which online stores gain most from improving site search
If search users convert far better than other visitors, or your zero-result rate is high, search is where the cheapest gains are.
Stores with large catalogues
Shoppers rely on search when browsing thousands of products is impractical. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for automation for ai ecommerce search personalization.
Stores with mobile traffic
Typos, short queries and voice input make forgiving search essential on phones. We look at this situation in the audit and give ai ecommerce search personalization an honest view of whether the build is worth doing now or later in the automation work.
Stores with high search exit rates
Searches that end the visit show exactly where relevance fails. If this describes your business, the automation work for ai ecommerce search personalization is likely to pay back quickly, and we will tell you plainly if it will not.
Stores with varied customers
Personalisation helps beginners, experts and gift buyers find different things. Owners in this position often say the biggest change is the time returned each week, and automation is built to deliver that first for ai ecommerce search personalization.
Stores with changing ranges
Synonyms and rules keep search aligned with new products and seasons. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for automation for ai ecommerce search personalization.
Stores that sell technical products
Attribute understanding, such as volume or size, makes precise queries work. We look at this situation in the audit and give ai ecommerce search personalization an honest view of whether the build is worth doing now or later in the automation work.
The events that train, test and refresh the search
Search improves from what shoppers type and do, so each query and click is an event the system learns from.
search_queryA shopper searches
The query is corrected, expanded with synonyms and matched by keyword and by meaning. The timing of this step is written down in the rule table, which means you can change when it happens without asking us to rebuild anything in the automation build for ai ecommerce search personalization.
zero_resultsA query returns nothing
The query is logged, and the weekly job proposes a synonym, an attribute fix or a content gap. If the same event arrives twice, the second copy is recognised and ignored, so automation for ai ecommerce search personalization never repeats an action by accident.
result_clickedA result is clicked
The click and any add-to-cart or order from the query feed back into ranking. A short summary of how often this event occurs appears in the weekly report, giving ai ecommerce search personalization a clear view of how busy this part of the automation work is.
product_updatedA product changes
Changes to titles, attributes or stock update the index within minutes. Where the event depends on outside data, the data is checked for freshness first, because a stale figure is worse than no figure for automation for ai ecommerce search personalization.
consent_changedConsent changes
Personalisation turns on or off for that shopper immediately. Each moment like this has its own rule, so automation for ai ecommerce search personalization behaves the same way every time and never has to guess what to do next.
test_endedA test ends
The lift in search conversion and revenue is compared with the control and reported. The event is logged with the rule that handled it, which lets you check later exactly what automation did for ai ecommerce search personalization and why it did it.
One search followed from a misspelt query to a personalised, tested result
This shows how a single query is understood, ranked and measured.
- 01
The shopper types a query
A visitor on a phone types navy rucksak 20 litre waterproof, with a typo and a descriptive phrase. If the step cannot finish, the record says why and waits for you, so automation for ai ecommerce search personalization never moves on with a half-completed task.
- 02
The query is understood
The engine corrects rucksak, maps rucksack to your daypack category, reads twenty litre as a volume attribute and waterproof as a feature. You can see the outcome of this step before the next one starts, which keeps the automation build for ai ecommerce search personalization under your control throughout.
- 03
Results are retrieved
Keyword matching finds daypacks with those attributes, and semantic matching adds a hiking pack described in different words. The step uses only the facts you supplied, so nothing in the automation result for ai ecommerce search personalization depends on guesses or on outside sources you did not approve.
- 04
Results are ranked
In-stock products rank first, relevance and profit weights order them and a product you have pinned for the season is placed at the top. Timing for this step is paced to the limits of your tools, so automation for ai ecommerce search personalization keeps running calmly even when volume is high.
- 05
The results are personalised
Because the visitor has agreed to personalisation and has viewed trail gear before, a lighter model they viewed earlier moves up. A check at the end of the step compares the result with the input, which catches the small mistakes that otherwise surface days later in the automation build for ai ecommerce search personalization.
- 06
The outcome is learned
The click, add-to-cart and order are attributed to the query, and the weekly report shows the search conversion for that query group. Because the steps are separate, a change to one of them, such as a new rule for ai ecommerce search personalization, does not disturb the others in the automation flow.
Pricing
Price your AI ecommerce search personalization
Choose a plan, tell us your platform and catalogue size, and add any extras, and the total and live date update as you go.
What AI ecommerce search personalization costs and what raises the price
Our fee is fixed and paid once, while the search service, hosting and model usage are billed by their own providers.
Catalogue size
Larger catalogues need larger indexes and more tests. We explain this factor in the quote in plain words, because ai ecommerce search personalization should understand what drives the price before agreeing to anything in the automation work.
Languages
Each language adds synonyms, attributes and tests. Changes to this factor after the quote are handled as a written change, with its own price, and never as a hidden charge on automation for ai ecommerce search personalization.
Search service choice
A move to a dedicated search service adds integration and migration work. A smaller first build can leave this factor for later, and ai ecommerce search personalization can add it at a priced step once the first results are in in the automation work.
Test and reporting depth
Several tests and dashboards add build time. The tools around automation bill separately, and we estimate their monthly cost for this factor so your running cost is known as well as the build for ai ecommerce search personalization.
Consent and personalisation
Consent-aware personalisation needs event collection and checks. Owners who run automation for ai ecommerce search personalization often find this factor is the one that most changes the quote, which is why we ask about it first.
Rule volume
Many pins, blocks and seasonal rules add configuration. We check this factor against your real records during the audit, so the number in the quote comes from your data and not from a guess in the automation build for ai ecommerce search personalization.
Search services, hosting and model usage are billed by those providers.
Next step
Ready to price AI Ecommerce Search Personalization?
AI ecommerce search personalization makes your site search understand what shoppers mean, correcting typos, mapping synonyms and attributes, ranking by relevance and profit, personalizing results by behaviour where consent allows and reporting zero-result searches so the catalogue and the search improve together.
- One fixed price from $2,640, paid once
- Zero-result searches found and fixed first
- Personalisation only where consent allows
Three more uses of the improved search
Better search feeds other parts of the store.
Merchandising from the search log
Top queries without good results become new categories, filters, product pages or buying decisions. The same records and rules that power automation for ai ecommerce search personalization are reused here, which keeps the cost low and the results consistent with the rest of the build.
Multilingual search
Queries in other languages are understood, with synonyms and attributes per language. You can switch this job on later without rebuilding anything, so ai ecommerce search personalization can start with the core automation work and grow when the time is right.
Search-driven emails
Shoppers who searched and did not buy can receive a relevant follow-up where consent exists. Results from this job appear in the same weekly report as the main automation work, giving ai ecommerce search personalization one place to judge whether it earns its keep.
Which data the search uses and where each fact is the source of truth
Search works from a copy of your catalogue and from queries, so we keep the index and its sources in step.
| Store object | Fields we use | Direction | Source of truth |
|---|---|---|---|
| Catalogue index | Title, attributes, category, stock, margin class | Synced within minutes | Search index from your catalogue |
| Queries | Text, results shown, clicks, orders | Logged | Search log |
| Synonyms and rules | Terms, pins, blocks, boosts | Read before every query | Rule sheet |
| Embeddings | Vector for each product and query | Calculated by the model | Vector index |
| Behaviour | Views and clicks by anonymous id | Collected with consent | Event stream |
| Test results | Group, search conversion, revenue | Written daily | Experiment log |
Behaviour data is collected only where consent allows, and without it the search still understands queries and ranks by relevance and your rules.
Edge cases in ecommerce search that we test before launch
Search misbehaves on awkward queries, so we test a long list of them against your own catalogue before launch.
Brand and model searches
Exact model numbers and brands rank exact matches first and are never replaced by semantic guesses. Handling the case early protects the margin and the reputation of ai ecommerce search personalization, which is worth more than the hours it takes to write the rule in the automation work.
Synonyms that conflict
A synonym that is right for one category and wrong for another is scoped to the category. We test this case before launch on your own records, so automation for ai ecommerce search personalization has met it before it meets a real customer on a busy day.
Restricted or hidden products
Products you hide, such as age-restricted goods, never appear to the wrong audience or in suggestions. When the case appears, the workflow follows a written rule and records the choice it made, so you can review it afterwards if you wish in the automation build for ai ecommerce search personalization.
Languages and spelling variants
British and American spellings and other languages are handled with their own synonyms. If a rule cannot settle the case safely, it goes to you with the record attached, and automation for ai ecommerce search personalization waits instead of guessing.
Out-of-stock products
Sold-out products rank below available ones and offer an alert, instead of leading the results. Cases like this are the reason the automation build for ai ecommerce search personalization includes a trial run, because a tidy example never shows them but real records do.
Long and conversational queries
Long descriptive queries are broken into attributes and intent, and fall back to keyword matching if unclear. Your answer to this case is added to the rule table, so the same situation is handled automatically the next time it appears for ai ecommerce search personalization in the automation work.
Default platform search, a search app and a built AI search compared
Default platform search is free and basic, a search app such as Klevu, Searchanise or Algolia is a strong step up, and a built AI search fits relevance, merchandising rules and personalisation to your catalogue and measures them.
| Platform search | Search app | Checkout Loom build | |
|---|---|---|---|
| Typo tolerance and synonyms | Partly | ✓ | ✓ |
| Semantic understanding of descriptive queries | – | Partly | ✓ |
| Ranking by relevance and profit | – | Partly | ✓ |
| Personalisation with consent controls | – | Partly | ✓ |
| Weekly zero-result and gap reports | Partly | Partly | ✓ |
| Cost pattern | Included | Monthly plan by products or traffic | A one-time build fee, then hosting and model costs |
Shopify’s free Search and Discovery app already offers synonyms, filters and boosting, and we configure it fully before recommending anything more.
How the AI search is built and what each part costs
We choose the search stack for your catalogue and traffic and show the monthly cost of each part beside our fixed fee.
Shopify Search and Discovery
The free app for synonyms, filters, product boosts and the Predictive Search API on Shopify, configured to its limits first. If the layer fails or changes its terms, the handover guide explains how to swap it, so ai ecommerce search personalization is never stranded by one supplier in the automation work.
A search service
Algolia, Klevu, Typesense or a managed vector search for stores that need more than the platform provides, chosen by catalogue size and traffic. Its role is written in plain language in the handover guide, which means a new team member at ai ecommerce search personalization can understand it in a few minutes in the automation work.
Embeddings and a ranking layer
A current embedding model for semantic matching and a ranking layer that blends relevance, stock and profit. The monthly price of this layer is written next to our fixed fee before you commit, so the true cost of running automation for ai ecommerce search personalization is clear from day one.
A rule sheet
Synonyms, pins, blocks and boosts that merchandisers can edit without code. You can replace this layer later with another tool, because the rules live in a table you own and are not locked inside any one product in the automation build for ai ecommerce search personalization.
A search analytics report
Top queries, zero results, exits and revenue by query group. We choose the lightest option that does the job for ai ecommerce search personalization, since a simpler layer means fewer things to maintain and fewer ways to fail in the automation work.
Make, Zapier or n8n
The workflow layer that syncs the catalogue, runs reports and writes the log. If you already pay for something that covers this need, we use it, and we only add a new service when automation for ai ecommerce search personalization genuinely requires one.
How we keep search fast and dependable
Search is on the critical path of every visit, so speed and fallback matter as much as relevance.
Latency budget
Results must arrive within a short limit, and slower responses fall back to the platform search. The safeguard is tested during the trial batch by feeding it deliberately awkward records, so ai ecommerce search personalization sees how it behaves before real customers are involved in the automation work.
Index freshness
Catalogue changes reach the index within minutes, and stock changes sooner. Everything the safeguard decides is written to the log with the reason, giving ai ecommerce search personalization a trail that is useful when someone asks what happened and why in the automation work.
A query test set
Hundreds of real queries are run before and after each change, so a fix for one never breaks another. During the month of corrections after launch, any gap in this safeguard is fixed in writing and the fix is added to the handover guide in the automation build for ai ecommerce search personalization.
Alerts that name the query group
Each alert gives the query group, the change and the effect. Reports on how often the safeguard fires show ai ecommerce search personalization whether the automation work is getting steadier or whether a rule needs attention.
A control group
A share of shoppers keeps the old search so the lift is measured against something real. Because this protection is written as a rule and not as a habit, it keeps working when staff change or the business gets busier in the automation build for ai ecommerce search personalization.
Plain explanations after setbacks
If a change harms search, the owner explains in writing what happened and restores the previous state. When something goes wrong in automation for ai ecommerce search personalization, the failure is named, retried within safe limits and listed for you, instead of vanishing into a log nobody reads.
Privacy, consent and permissions in AI search personalization
Search logs and behaviour are personal data in many places, so we collect little and ask for consent.
Least-privilege access
We request read access to the catalogue and write access to search settings only. We explain in plain words what each permission allows, so the person approving it for ai ecommerce search personalization knows exactly what they are agreeing to in the automation work.
Consent-aware personalisation
Behaviour-based ranking runs only where the shopper has agreed, and others see relevance-based results. At the end of the engagement you receive a list of every connection that was made, so nothing stays open by accident in the automation build for ai ecommerce search personalization.
Keys in your accounts
Search service and store keys sit in your accounts and can be revoked at any time. For automation on ai ecommerce search personalization, access is limited to what the job needs and can be withdrawn in a minute, so ending the work leaves nothing behind.
Anonymous identifiers
Sessions use anonymous identifiers, and query logs do not store names or emails. Nothing in this arrangement requires you to share a password, because access is granted from your own account in a form you can revoke in the automation build for ai ecommerce search personalization.
Retention you choose
Search logs are kept for a period you set and deleted on request. We keep no copy of your keys, records or customer details in systems that belong to us, and the handover guide shows you how to confirm that in the automation build for ai ecommerce search personalization.
Handover and removal
You receive a list of every service and setting we added, and our own access is removed on the last day. Only the fields that automation needs are read, and only for as long as the task takes, which keeps the exposure for ai ecommerce search personalization as small as it can be.
What you receive with your AI ecommerce search personalization
Each plan ends with better search, rules you control and the evidence of the effect.
- A query audit listing zero-result, low-converting and high-volume searches.
- A synonym, attribute and rules set tuned to your catalogue.
- Typo tolerance, word forms and attribute understanding.
- Semantic search for descriptive queries, combined with keyword matching.
- Ranking by relevance, stock and profit within your rules.
- Personalised results where consent allows.
- A search test with a fair split of visitors and a rule for when to stop.
- A weekly search report and a merchandiser guide.
- A month of corrections after the search goes live.
Verified client reviews and the measurement behind our search builds
The unedited words of verified clients of the Checkout Loom team appear below, followed by the measurement rules every search build follows.
- -62%zero-result searches is a typical fall in builds that fix synonyms and attributes first.
- 1control group keeps the old search so the lift is measured fairly.
- 30 daysof corrections follow launch so the search settles before we step back.
Results come from catalogues we have worked with and are indicative, and platform behaviour follows vendor documentation.
★★★★★
I highly recommend ordering from the Checkout Loom team. I had slow responses and poor communication with other providers, but this team was great and I am very happy with their service.
HarmetUnited States
Verified
★★★★★
I am a returning customer because my first AI automation workflow worked so well. Once again everything was built exactly as requested, and the whole process was reliable from start to finish.
WilliamUnited States
Verified
★★★★★
The determination and commitment to quality from the Checkout Loom team were clear from day one. They understood my requirements perfectly and completed everything with professionalism and care, and it is rare to find a team this dependable.
LorcanColombia
Verified
★★★★★
It was an excellent experience from start to finish, and the automation works reliably with everything configured properly. It is clear that the team has strong knowledge of automation, and I will definitely be returning for future work.
EllieUnited States
Verified
★★★★★
The team carefully investigated the problem, tested different scenarios and delivered a dependable solution. A duplicate contact issue had been causing problems for quite some time, and it is finally resolved.
RichardUnited States
Verified
★★★★★
The Checkout Loom team were easy to work with and brilliant at the work itself. I highly recommend them to anyone who needs dependable automation.
IveraAutomation client
Verified
★★★★★
Everything was configured exactly as discussed, and the team made sure the automation was stable before delivering it. It has been a huge time-saver for our whole team, and it runs without any manual work.
BellamyUnited States
Verified
★★★★★
This was excellent automation work, and it is clear that the team has strong experience with AI and automation. I highly recommend the Checkout Loom team to other businesses.
FinnUnited States
Verified
How the search build runs from query audit to a measured rollout
We read your real queries first, because they show exactly where search fails.
- 01
Audit the search log
We export your queries and group them by volume, results, clicks and conversion, and list the failing groups. Nothing in this step touches your live customers, so ai ecommerce search personalization can review it calmly and ask for changes before anything goes public in the automation work.
- 02
Fix the basics
We add synonyms, typo handling and attribute mapping and re-run a test set of real queries. The step has a clear finish line, which means you always know whether the automation work for ai ecommerce search personalization is on time or running behind.
- 03
Add semantic search
We add semantic matching for descriptive queries and combine it with keyword matching. Questions that arise here are answered in writing, so there is a record of every decision made about automation for ai ecommerce search personalization.
- 04
Set ranking and personalisation
We agree the ranking weights, the pins and the consent-aware personalisation with you. We keep each step small enough to check, because large steps hide mistakes and make the automation build harder to correct for ai ecommerce search personalization.
- 05
Run the test
We run a controlled test, read the result with you and keep what works. At the end of this step you approve or ask for changes, and the next stage of automation starts only after your reply for ai ecommerce search personalization.
- 06
Hand over
You receive the rule sheet, the report and a month of corrections, and we remove our access. The time this step takes depends on how quickly your records and answers arrive, and we tell ai ecommerce search personalization early what we need to keep moving in the automation work.
Estimate what better site search could add to your revenue
Enter your search traffic and conversion and the estimate shows the extra orders from fewer failed searches and a better first result.
Failed searches each month today–
Extra orders if half are recovered–
Extra revenue each year–
The estimate assumes that half of failed searches become successful ones and then convert at your search rate, which is cautious.
AI ecommerce search personalization questions, answered in full
These are the questions store owners ask about semantic search, software, relevance, merchandising and cost before they order search improvements.
What is AI ecommerce search personalization?
It is the improvement of your site search so that it understands typos, synonyms, attributes and descriptive queries, ranks results by relevance and profit and adapts to each shopper’s behaviour where consent allows, with reports and tests that show the effect.
What is AI ecommerce search software and do I need it?
AI ecommerce search software is a service such as Algolia or Klevu that provides typo tolerance, semantic matching and ranking, and you may not need it if the platform’s own search, fully configured, solves your failing queries, which is why we audit first.
What is personalized site search?
Personalized site search reorders results using what the shopper has viewed or bought, so a returning trail runner and a first-time gift buyer can see different first results for the same query, and it runs only where consent allows.
What do ecommerce search and discovery tools include?
Ecommerce search and discovery includes the search box and predictive suggestions, the filters and sorting on category pages and the recommendations that help shoppers find products they could not name, and we improve each of them from the same query data.
How does product search personalization differ from recommendations?
Product search personalization changes the order of results for a query the shopper typed, while recommendations suggest products without a query, and the two share the same behaviour signals and rules.
What is an AI product search engine?
An AI product search engine combines keyword matching with machine-learned understanding of meaning and attributes, so that a descriptive query such as a navy waterproof twenty litre pack finds the right daypacks even when those words are not in the title.
What is semantic search for ecommerce?
Semantic search for ecommerce represents queries and products as vectors that capture meaning, so similar needs match even with different words, and we combine it with keyword matching so exact model numbers and brands still win.
What is search merchandising automation?
Search merchandising automation applies your pins, blocks and boosts by rule and proposes new ones from the search log, so merchandisers spend time on decisions and not on editing lists by hand.
How do you improve ecommerce search relevance?
We improve ecommerce search relevance by fixing zero-result queries, mapping synonyms and attributes, testing a set of real queries before and after each change and ranking by relevance, availability and profit.
Can AI storefront search and personalized search results be tested?
Yes, an AI storefront search can be tested against the old search with a fair split of shoppers, and personalized search results are compared on search conversion and revenue per search before we roll them out.
When will search be improved and what do you need from me?
The first improvements are usually live in five to seven weeks. We need your search log, access to your store and any search service, your merchandising rules and consent settings, and we send a written checklist after you order.
Make your site search understand what shoppers mean and show them what they came for
Choose a plan in the pricing calculator and continue to checkout, or request a quote with your store address, your current search tool and the number of products. We reply in writing within one working day with a confirmed start date and a short checklist, so the query audit begins right away.
- One fixed price from $2,640, paid once
- Zero-result searches found and fixed first
- Personalisation only where consent allows