Hire Elasticsearch Engineering
for powerful search & analytics

From full-text search and log analytics to application observability, our Elasticsearch engineers build fast, scalable search solutions that deliver relevant results in milliseconds.

Discuss your Elasticsearch project

Full-text search with analyzers, tokenizers, relevance scoring & boosting

Elasticsearch clusters, index lifecycle management & rolling upgrades

Kibana dashboards, Canvas, Lens & alerting for visual analytics

Logstash pipelines, Beats agents & Elastic Agent for data ingestion

Vector search, semantic search & ELSER for AI-powered retrieval

Core Capabilities

What we build
with Elasticsearch

Full-Text Search

Relevant and fast

Search applications with custom analyzers, relevance tuning with BM25, multi-field search, autocomplete with suggesters, and faceted navigation for e-commerce and content platforms.

Log Analytics & Observability

Centralized and actionable

ELK Stack for centralized logging, APM for application performance monitoring, infrastructure monitoring, and unified observability with Elastic Agent and Fleet management.

Vector & AI Search

Semantic and intelligent

Dense and sparse vector search with HNSW, ELSER for semantic search, hybrid search combining lexical and semantic relevance, and RAG pipelines with Elasticsearch as the vector store.

How It Works

From index design to
production

Index Design &
Mapping

We analyze your search requirements, design index mappings with appropriate field types and analyzers, configure ILM policies, and plan cluster topology for your data volume and query throughput.

Agile
Development

Our enterprise solution engineers work in 2-week sprints with incremental search functionality. You see search quality improving every sprint.

Testing &
CI/CD

Search relevance testing with ranked evaluation API, load testing with Rally, and pipeline validation. Our QA specialists and DevOps engineers ensure every build is production-ready through automated pipelines.

Deployment &
Monitoring

Elastic Cloud for managed hosting, self-managed on Kubernetes with ECK Operator, or on-premises. Monitoring with Kibana Stack Monitoring, Elastic APM, and Watcher alerts.

Hire Elasticsearch Developers

Elasticsearch engineers ready
to join your team

Deliver powerful search experiences with dedicated Elasticsearch engineers who design scalable search and analytics solutions.

Hire Elasticsearch Developers

Why product Enhancement

Improve with intent,
not impulse

AI-assisted
search tuning

AI tools analyze search queries, detect low-relevance results, suggest analyzer improvements, and recommend field weight adjustments for better ranking.

AI-powered
testing

Automated search relevance testing, query performance regression detection, and AI-generated test query sets covering edge cases and common user search patterns.

Cluster
optimization

AI-driven shard sizing, index rollover optimization, hot-warm-cold data tiering recommendations, and resource usage forecasting for cost optimization.

Intelligent
automation

Automated index template management, ILM policy recommendations, anomaly detection on cluster health, and smart data retention policies.

FAQ

Frequently Asked
Questions

Elasticsearch provides lightning-fast full-text search with relevance scoring, faceted navigation, and aggregations out of the box. Its REST API, rich query DSL, and deep ecosystem (Kibana, Logstash, Beats) make it the standard for search-powered applications.

We configure custom analyzers with appropriate tokenizers and filters, tune BM25 parameters, implement function score queries for boosting, use synonyms and stemming, and validate improvements with ranked evaluation API.

Yes. We deploy Elasticsearch clusters, configure Logstash pipelines with grok and mutate filters, set up Filebeat and Metricbeat agents, build Kibana dashboards with visualizations and Canvas reports, and configure Watcher alerts.

We design with proper shard strategies (oversharding for growth), implement hot-warm-cold architectures with ILM, use cross-cluster replication for disaster recovery, and monitor with cluster allocation explain and cat APIs.

Absolutely. We use dense_vector fields with HNSW indexing, integrate with ELSER for sparse vector semantic search, implement hybrid search combining BM25 and kNN, and use Elasticsearch as a vector store for RAG pipelines with LLMs.