Every decision
between a query
and a good result.

Know where customers lose their way.

A search can return products and still fail the customer. We look beyond zero results: query reformulations, abandonment, clicks and the journey after a search.

Grouping queries by intent, product category and market helps reveal patterns that an average hides. We also review whether your tracking can answer the questions your team is asking.

• Query classification and recurring failure patterns
• Analytics definitions, instrumentation gaps and useful segments
• Baselines and measures for evaluating a change

A match should make sense.

“Linen shirt” should favour shirts, not every product made from linen. “Winter wedding” adds context that a literal match can miss. Those distinctions depend on both your product data and the way the engine uses it.

We examine searchable attributes, synonyms, typo handling, ranking and commercial rules. We test different query types and languages so a change that helps one group does not quietly harm another.

• Catalogue attributes, index structure and query interpretation
• Ranking, boosts, availability and merchandising rules
• Language differences and industry-specific vocabulary

Help customers finish what they started.

Suggestions set expectations before the results arrive. Filters, sorting and product information help a customer decide whether the results are useful. A dead end needs a credible next step.

We connect those interface decisions to the capabilities of your engine and the behaviour in your data. Recommendations describe the interaction as well as the search logic behind it.

• Autocomplete, suggestions and discovery
• Facets, sorting and the results page
• Recovery from zero or poor results

Better by evidence, not by intuition.

A ranking change can look convincing in a few examples and behave differently across the catalogue. We help define the hypothesis, evaluation queries, success measures and safeguards before it is released.

Where traffic supports it, A/B testing measures the effect in real use. We interpret the outcome by query class and market when the data allows, then use it to decide what to keep, adjust or investigate.

Start with the problem a feature would solve.

Your existing platform may already have useful features that are underused. Semantic retrieval or a focused AI script may help with particular kinds of intent, classification or evaluation. The value depends on your catalogue and customers.

We assess the opportunity, define a useful test and work with your engineers on what implementation would require. Simulated customer searches can help explore emerging patterns; they complement real query data and customer experiments.

• Assessment of unused platform capabilities
• Semantic and hybrid search opportunities
• AI-assisted analysis and hypothetical search scenarios