Elasticsearch: implementation and integration

+35%increase in customer satisfaction after tuning relevance scoring in Elasticsearch (Paul Smith case)

−76%search response time after moving to Elasticsearch

15k+companies use Elasticsearch in their stack, including Amazon, Walmart, Apple

ms-latencysearch latency on the Lucene engine, scaling to petabytes with no downtime

Industry solutions

What you can build with Elasticsearch

E-commerce / retail Relevant catalog search with facets, synonyms and typo correction Product search and discovery on the storefront — higher conversion and average order valueLearn more →Marketplaces Hybrid search across millions of SKUs with personalized ranking Results and offer sorting — higher share of successful search sessionsLearn more →Media and content Full-text and semantic search across articles and archives Content navigation and recommendations — deeper views, longer sessionsLearn more →Banking and fintech Real-time search and analytics across transactions, logs, and events Incident monitoring and investigation — faster problem resolutionLearn more →Logistics Geo-search and time-series analytics on movements and warehouses Nearest-location search and tracking — more accurate delivery planningLearn more →SaaS and B2B platforms Built-in search across product data and application observability In-product search and monitoring — retention and service stabilityLearn more →Industry / IoT Real-time indexing and search across sensor logs and metrics Equipment operation — early anomaly detectionLearn more →Enterprise sector Enterprise search and RAG search over an internal knowledge base for LLMs Knowledge search and AI assistants — faster answers for employeesLearn more →

Capabilities

Elasticsearch Capabilities

User: search box + typoAnalyzer: tokens, synonyms, typo correctionCatalog index (Lucene): lexical matchVector match: semantics and embeddingsHybrid ranking + rerankingResults: facets, filters, autocompleteAnalytics: zero-results and behavior — catalog improvement

Sequence diagram of relevant search. The user query is processed (analysis, typos, synonyms); lexical and vector matching run in parallel against the catalog index; results are merged by a hybrid ranker and reranked; facets and suggestions are added to the output; the search event is written to analytics to close zero-result queries. It shows how each step works toward relevance and conversion.

Full-text search with relevance tuning

The shopper finds the product on the first query — conversion and average order value rise, while the share of zero-result queries drops.

Facets and filtering (aggregations)

A complex catalog narrows to what's needed in a few clicks — a shorter path to purchase and fewer drop-offs.

Vector and hybrid (semantic) search

The system understands the meaning of a query, not just exact words — relevant results even with typos and synonyms.

Autocomplete, typos, synonyms

Search-bar suggestions speed up typing and rescue misspelled queries — fewer lost sessions.

Search analytics and zero-result queries

You see what users search for but don't find — the content team closes catalog gaps and turns demand into revenue.

Logs, metrics, observability on one engine

One platform for the storefront and infrastructure monitoring — fewer tools, faster root-cause search for incidents.

Scaling to petabytes with no downtime

Catalog and load grow without rewriting and without downtime — ms-latency holds at peaks.

Geo-search and time-series data

"Near me" search and time-based analytics out of the box — precise scenarios for retail and logistics.

Approach

How we implement Elasticsearch

Minimal core modification

We don't fork or patch the Elasticsearch core. Elasticsearch stays on the standard, upgradable version — we move business logic into separate microservices alongside it, so platform updates don't break your customizations.

International Standards, Not Homegrown Hacks

Where a mature international solution exists, we use it instead of inventing our own protocol or platform. Before writing code, we study how the problem is already solved in the industry.

Transferability

The solution is loosely coupled and documented: it can be handed over between teams and contractors without rewriting. You are not tied to us.

AI compatibility

Elasticsearch in the AI stack

Vector database for RAG

Stores and searches embeddings (text, image, multi-modal) through a single API — a foundation for retrieval-augmented generation on enterprise data.

Hybrid ranking + reranking for LLM

Combines lexical and vector search with reranking to feed the LLM precise context and reduce hallucinations.

LLM observability

Monitor the quality and cost of AI applications in the same stack where logs and metrics live.

Semantic search by meaning

Results returned by query meaning, not word matching — higher recall without losing precision.

Transferability through a standard

A mature international engine and open API: search business logic is built alongside, the core isn't forked — handover between teams without rewriting.

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