Elasticsearch at Shivacha
Search and analytics engine for full-text search and logs.
Overview
Elasticsearch powers full-text search, faceted navigation and log analytics. We design indexes and relevance tuning for product search and use it in observability stacks.
Why we use it
- Full-text search
- Faceting and relevance
- Log analytics
- Vector search support
How we use it
Elasticsearch in our engineering work
Product search
Commerce and marketplace search.
Logs
Observability.
Hybrid retrieval
Keyword and vector for RAG.
Services
Services that use Elasticsearch
E-commerce Development
Custom and headless e-commerce platforms — storefronts, catalogues, checkout, payments, fulfilment and marketplace integrations.
Learn moreRAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreCloud Monitoring & Observability
Observability for cloud systems — metrics, logs, traces, SLOs, dashboards and actionable alerting.
Learn morePairs well with
What we combine with Elasticsearch
Relational, document, search and analytical data stores.
Insights
Related insights
Why most AI pilots never reach production — and the engineering that fixes it
The model is rarely the problem. Retrieval quality, evaluation, integration and governance decide whether an AI pilot becomes a production system.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn more
Build with Elasticsearch.
Tell us about your project, or the engineers you need, and we will propose an approach.