Search Implementation

Search quality is an engineering discipline, not a knob. Good results come from the whole chain — how documents are analyzed and mapped, how queries are constructed, how business signals are folded in, and how changes are measured before they ship.

What we build

• Schema and mappings — field types, analyzers, and normalizers chosen for your language, your data, and your query patterns; multi-field strategies that keep exact matching, full-text scoring, and aggregations from fighting each other.
• Query design — the query DSL constructed deliberately: multi_match field weighting, minimum_should_match, phrase and proximity handling, filters vs queries, and function scoring for business signals like freshness, popularity, or margin.
• Relevance tuning with measurement — a judgment list built from your real queries, scored with the ranking evaluation API, so every tuning change is a measured improvement rather than a hunch. We tune BM25 and analyzers first; they fix more than most teams expect.
• Hybrid and vector search — kNN and dense-vector retrieval, combined with lexical scoring, where the measurements say it earns its operational cost. We are equally comfortable telling you it doesn't — vector search is a tool, not an upgrade.
• Operational search concerns — index-per-locale strategies, reindex-safe aliasing, autocomplete and suggesters, pagination that survives deep result sets, and latency budgets under concurrent load.

Elasticsearch or OpenSearch

Both. The lexical machinery is largely shared; the vector, ES|QL/SQL, and plugin stories diverge. We'll be specific about which features exist on your platform and version before anything is designed around them.

How engagements run

Search work is iterative by nature: establish the measurement harness, fix the foundations (analysis and mappings), then tune in measured passes. We work in your codebase and your cluster, with your team in the loop — the goal is that your engineers can keep tuning after we leave.