Search Relevancy
Relevance is the single most important factor in whether users trust your search. A technically perfect cluster that returns poor results will drive users away just as surely as downtime. Our relevancy engineering practice combines deep Elasticsearch expertise with data-driven experimentation to build search experiences where the right result appears at the top — consistently, across every query and every user context.
Custom Scoring & Ranking
Default BM25 scoring treats every field and every signal equally, which is rarely what your business needs. We design scoring strategies that reflect actual user intent and business value — boosting freshness for news sites, conversion signals for e-commerce, or authority signals for knowledge bases. Our scoring models are explainable, maintainable, and tunable without a full reindex.
- ✓Field-level and query-level boost configuration
- ✓Function score and script score query design
- ✓Business signal integration — popularity, recency, and margin weighting
- ✓Pinned queries and curated result overrides
Query Analysis & A/B Testing
Relevance cannot be improved by intuition alone. We instrument your search to capture the signals that matter, turn them into a continuous feedback loop, and design controlled experiments that measure actual relevance impact — giving you the evidence to make confident configuration changes.
Click-through rates, reformulation patterns, and session abandonment captured as first-class relevance signals.
Systematic review of zero-result and low-click queries to find where your search silently fails users.
Controlled experiment design with statistical evaluation, so ranking changes ship on evidence rather than opinion.
Human judgment lists and offline grading pipelines that benchmark quality before changes ever reach production.
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