Search quality is experienced by people, not by a configuration screen. Strawberry can work from the Algolia views and live search journeys open in your browser to investigate why a result feels wrong and turn the evidence into an actionable review.
Use it to compare queries with landing pages and catalog context, document relevance gaps, and prepare the next experiment before changing search behaviour.
01
Test the query the way a customer experiences it.
A search rule can appear sensible in isolation and still produce a poor result for a real phrase, device, or product need. The issue often becomes visible only when you compare the search view with the destination page and the language customers use.
Strawberry can run that browser-context investigation and produce a clear record of the gap. It helps the team discuss the specific broken journey rather than a vague complaint that “search feels off.”
02
Turn zero results into a bounded research task.
A zero-result report may include spelling mistakes, new demand, discontinued products, and queries that should never match. Treating every row as the same kind of problem creates bad fixes.
Strawberry can use the relevant catalog, support, and website context in your browser to group the cases by likely cause. The output becomes a decision list: add content, improve synonyms, redirect intent, or leave the result alone.
03
Give relevance work a shared definition of better.
Search teams can change many variables, but an experiment is only useful when everyone agrees what outcome would count as an improvement. Without a written baseline, a preference can masquerade as a result.
Strawberry can prepare an experiment brief that ties a proposed change to the affected queries, observed behaviour, expected outcome, and check after launch. That gives product, content, and engineering a shared review object.
04
Let recurring query reviews stay evidence-led.
The most useful search-quality process is not a one-off audit.
It is a regular way to notice new demand, regressions, and unhelpful results before they become a larger customer problem.
Save a well-tested Algolia review as a Strawberry skill, and use a routine only when its scope, data source, and recipient are agreed. The recurring output should surface candidates for human action, not quietly alter relevance logic.
It can use the Algolia and live-site browser context you provide to document query behaviour, relevance gaps, and the evidence behind a proposed next step.
Use Strawberry to prepare and review the proposed work. Keep a responsible search owner involved before any customer-facing relevance change is made.
Yes. It can compare them with the catalog, site, and support context visible in your browser, then group them into practical next actions.
Yes. Turn an approved, read-only review method into a skill, then run it as a routine with a defined scope.
Give access only to the needed search context, verify query evidence, and retain approval around changes that affect the customer search experience.
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