We improve search, filtering, recommendations and search measurement for e-commerce catalogs, content-heavy websites, knowledge bases and enterprise platforms.
The work starts with a practical question. When someone searches, do they find the product, document, answer or next step they need, or do they leave, refine the search repeatedly, contact support, or buy something else?
Search is often the shortest path between a question and a decision, and what it decides depends
on who is asking.
In a store, whether a shopper finds the right product, discovers a useful alternative, adds another item to the cart, or leaves.
In a large organization, whether a member finds a resource, an employee finds a policy, a customer solves a problem alone, or someone gives up and asks for help.
Search and filtering deserve the same care as navigation, product pages and checkout. Baymard
research summarized by Shopify in 2026 found that 58% of desktop e-commerce sites and 78% of mobile
sites have mediocre or worse product-list and filtering performance.
Search Inside Your Site, Not Google Rankings
This service covers the search box inside your website, store, portal or application. If you
need search engine optimization, that is a different discipline.
Explore SEO services.
What We Do
What We Help Improve
Strategy
Search Strategy and Platform Decisions
The first question is not which search product to buy. It is what people need to find, what data exists, what systems the search has to connect to, and what happens when a result is useful. We help teams evaluate the search they have, choose a platform when a change is needed, and build an implementation plan that accounts for catalog or content data, permissions, indexing, user experience, measurement, operations and cost.
Search audit and opportunity assessment
Platform evaluation and implementation planning
Content, product and metadata review
Indexing and data-pipeline strategy
Search UX and result-page requirements
Search analytics and relevance-measurement plan
Internal team training and documentation
Quality
Relevance and Search Quality
A search can return hundreds of results and still fail if the useful one is buried. We evaluate the queries people use, inspect what ranks first, and identify why the results do not match the need. That may involve synonyms, spelling and language handling, field weighting, ranking rules, product availability, content freshness, category logic, or the way a query is interpreted. We test changes against real query sets and defined relevance criteria before they reach the public experience.
Top-query and zero-result analysis
Query intent and synonym mapping
Relevance assessment and result grading
Ranking, boosting and business-rule review
Search-result and landing-page testing
Regression testing as content and catalog data change
Ongoing relevance review
E-Commerce
E-Commerce Search, Filtering and Recommendations
Shoppers do not always know the exact product name. They search by problem, brand, compatibility, material, size, use case, color, price range, or a word your catalog does not use. We improve the path from product discovery to purchase through search, filters, sorting, category navigation and recommendations.
Product-search relevance and typo handling
Zero-result and poor-result query recovery
Faceted navigation and mobile filter design
Product titles, attributes, categories and metadata
Sort order and merchandising rules
Related, substitute and complementary product strategies
Search-to-cart, search-to-purchase, revenue and average order value tracking
Enterprise Content
Content, Knowledge Base and Portal Search
Large sites often have a different search problem. Useful material exists, and it is spread across articles, PDFs, journal content, help centers, records, member areas and separate systems. We help organizations make that material easier to find and easier to trust, through content-model and metadata work, index strategy, relevance tuning, filters, permission-aware results, and measurement that shows whether people found a useful answer.
Search across multiple content sources
Article, document, journal and resource discovery
Facets for topic, date, format, audience, specialty or content type
Permission-aware and role-aware result handling
Knowledge-base and intranet search improvement
Content freshness and duplicate-result review
Search behavior, exit, refinement and support-deflection measurement
Measurement
Search Analytics and Measurement
A search box creates its own research data. Search logs show what people are trying to find, the words they use, what returns nothing, what they refine, and which results they choose. We set up reporting that turns that behavior into a practical improvement list, looking for the queries that indicate a missed product, a content gap, a poor filter, confusing language, or a problem with how results are ranked.
Search usage and search adoption
Top, rising and seasonal queries
Zero-result and low-result queries
Query reformulation and high-refinement behavior
Result clicks and click position
Search exit rate
Search-to-cart and search-to-purchase rate
Revenue and average order value from search sessions
Content engagement and support-deflection signals after search
Automation
AI Search and Product Recommendations
AI can help with query understanding, semantic retrieval, product discovery and
recommendations. It does not replace product data, clear business rules, or relevance
testing.
Before recommending an AI search or recommendation feature, we look at the catalog, the
query behavior, the data quality, the permissions, the search volume and the decision the
feature needs to support. Then we identify whether a simpler improvement, such as better
attributes, synonyms, filters or ranking, would solve the problem first.
Where recommendations are appropriate, they help shoppers discover complementary products
and support cross-sell. Platforms such as Algolia use interaction data to surface related
items for that purpose.
We begin with the people using search and the decisions they are trying to make. A shopper
choosing a compatible product, a member looking for a resource and an employee searching for
a policy need different results, filters and measures of success.
02
Review the Queries, Results and Data
We inspect real search terms, zero-result queries, refinements, result clicks, product or
content data, filters and existing analytics. That shows whether the issue is the index, the
ranking, the content, the interface, the permissions, or something outside search.
03
Prioritize the Changes
We separate fast improvements from larger platform or data work. A useful synonym, a better
product attribute or a corrected filter may solve an immediate problem. Other needs require
new indexing, a platform change, new content structures or a recommendation strategy.
04
Test, Release and Keep Checking
Search changes should be tested against real queries before release. Afterward we monitor
the result quality and the behavior that matters: whether people find a product, complete a
task, purchase, read, register, or stop needing help.
Platforms
Platforms and Search Systems
We work with the search platform you have when it fits the problem, and help evaluate alternatives
when it does not. The right choice depends on the catalog or content, the data sources, the user
roles, the integrations, the editorial needs, the budget, and the search experience you need to
create.
AlgoliaElasticsearch and OpenSearchApache SolrCoveoAdobe Search & PromoteCustom and homegrown search systems
We do not recommend a platform because it is new or because it has an AI feature. We recommend it
when it fits the work people need the search to do.
Experience
Relevant Experience
Enterprise Search and Relevance Work
More than 15 years of enterprise-search experience, including work at the American Academy
of Family Physicians across AAFP Journals and a network of websites and applications. That
covered the parts of search that decide whether it is useful: indexing, relevance, content
and metadata structures, search-interface design, filtering, analytics, testing and
stakeholder communication.
Search Across Different Kinds of Information
We have worked with medical journals, product catalogs, member platforms and B2B content
libraries. The information differs, and the central question is the same. Can the right
person find the right thing quickly enough to act on it?
Clear Advice Before a Platform Decision
Search projects can become expensive quickly. We explain what is broken, what can improve
without replacing the platform, what requires a larger investment, and what should be tested
before a team commits.
Related website and search work
Adjacent projects covering catalogs, multi-source content, membership and measurement. None is a
dedicated enterprise-search engagement.