Customers search in everyday words, with typos and half-descriptions. We study what they type, fix the searches that fail, and teach your search to understand them.
What’s included.
Three steps.
Study
We read what customers type and what comes back.
Teach
We connect everyday words to your products.
Tune
We improve the order of the results.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Product-search relevance implementation
What you may be seeing
- Customers search for products we sell but see no results.
- The most useful products appear below irrelevant ones.
- A spelling mistake makes the search fail.
- Filters and categories do not match how buyers choose.
- Customers still call staff because the site search is unhelpful.
We improve how product searches handle real customer language, missing details and irrelevant results.
Implementation & verification
We build a judged set of real customer searches and compare the current results with the products that should appear. We improve indexed catalogue attributes, synonyms, typo handling, filters and ranking rules without promoting unsuitable products. We code the search interface and catalogue synchronization where the existing integration cannot expose the needed results or controls. We verify offline relevance and live search-to-purchase outcomes, testing ranking alternatives when traffic supports an experiment.
How we verify the work
- Sample zero-result, high-volume and high-revenue queries; label acceptable results for each.
- Fix missing catalogue fields and index freshness before tuning ranking.
- Separate exact identifiers from broad terms; constrain stock, region and variant eligibility.
- Measure top-result relevance, zero-result rate and successful outcomes among search users.
Tools considered for this work
Delivery: search configuration + frontend/backend development. Hosted option: Algolia search, rules, synonyms and analytics. Alternatives: Elasticsearch/OpenSearch or the client's existing search engine. Commerce option: Shopify Search & Discovery with compatible themes/APIs (controlled connections between applications). Validation: judged-query fixtures, search event tracking and Playwright.
02Semantic product-finder installation
What you may be seeing
- Shoppers describe a need, but our search expects exact product names.
- Long, natural-language searches return poor matches.
- Keyword search misses products that solve the same need.
- AI search suggests items outside the buyer's budget or requirements.
- We want smarter search without losing exact SKU matching.
We build a finder that understands the intent behind a question while respecting product facts and practical constraints.
Implementation & verification
We combine exact keyword retrieval with semantic retrieval so natural-language needs and precise identifiers can both succeed. We enrich product records and code query handling that applies explicit budget, category and availability constraints. We merge or rerank eligible candidates and provide a useful keyword fallback when the semantic service is unavailable. We evaluate relevance against real queries and compare completed buying outcomes, including latency and misleading-result rates.
How we verify the work
- Create separate evaluation sets for exact IDs, descriptive needs and hard constraints.
- Improve catalogue attributes and combine semantic candidates with lexical matches.
- Apply hard requirements before final recommendations; do not treat similarity as compatibility.
- Accept on relevance, constraint violations, query latency and conversion among eligible search users.
Tools considered for this work
Delivery: search/AI development + indexing and relevance configuration. Hybrid option: Elasticsearch lexical/vector retrieval and ranking fusion. Alternative: Algolia supported AI search capabilities; confirm plan and catalogue fit. Custom option: an embedding model with Qdrant plus a lexical search engine. Code: catalogue ingestion, query parsing, eligibility filters and frontend search.
03Basic AI product search bar
What you may be seeing
- Our search only works when customers know the exact product name.
- A request such as black office chair under 5,000 returns irrelevant items.
- Suitable products are missed because customers describe them differently.
- Customers leave after empty or unhelpful search results.
- Search results show products, prices or stock that do not match our catalogue.
We add a focused question-led search experience that helps customers narrow their options without inventing product details.
Implementation & verification
We add a basic search bar where customers describe the product they need and receive matching catalogue results. We code the search interface and backend connection, index approved product information and synchronize current price and stock data. We configure semantic or hybrid keyword-and-AI retrieval, with explicit filters for supported requirements such as category, price and availability. We return familiar product cards and product-page links, then verify relevant matches, loading speed and useful no-result behavior using representative customer searches.
How we verify the work
- Implement one query-to-results search interaction; use niche 109 for deeper semantic-retrieval tuning when needed.
- Normalize catalogue fields and apply supported price/category filters explicitly; preserve exact product-code matching.
- Test natural descriptions, unfamiliar terminology, empty queries, unavailable products and service failures.
- Accept on relevant catalogue matches, accurate displayed product data, response time and result-click completion.
Tools considered for this work
Delivery: frontend/backend development + AI-search configuration. Hosted option: Algolia NeuralSearch for keyword and semantic catalogue search; confirm catalogue and plan fit. Alternative: Elasticsearch hybrid/semantic search with a custom search endpoint. Integration: existing Shopify, WooCommerce or custom catalogue APIs (controlled connections between applications); normal search-results UI. Verification: expected query-to-product matches, browser/mobile checks and basic search/result-click events.
Understand the question behind the search.
We combine search engineering with AI that interprets customer intent. Product facts, filters and evaluation queries keep suggestions grounded in the options you can actually provide.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Relevance
Useful results for representative real customer queries.
Confidence
Recommendations that satisfy stated requirements.
Progress
Successful searches, trials and qualified buying actions.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Do we need a new search system?
Often not. Better matching on your current one gives most of the gain.
Does it work in other languages?
Yes, we plan for the languages your customers use.