Elasticsearch development company
Krapton builds Elasticsearch search and analytics features that return the right results quickly: mappings and analyzers designed for your language and data, relevance measured against real queries, hybrid vector search, and clusters sized to stay healthy.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
Search quality is designed, not installed
Krapton is an Elasticsearch development company for products where search, filtering or log analytics shapes what users see and decide. Installing Elasticsearch is quick; making it return the right result first takes deliberate work. Dynamic mapping turns every string into both text and keyword fields and can lead to mapping explosions; one analyzer rarely suits product codes, names and descriptions alike; from and size pagination fails beyond max_result_window; and dual writes from application code drift out of sync with the primary database. Our engineers define explicit mappings, build custom analyzers with synonyms and stemming per field, and treat Elasticsearch as a derived index fed by change data capture or an outbox, never as the only copy of your data. Relevance is measured with a judged query set and the Ranking Evaluation API before and after each change. For smaller catalogs, PostgreSQL full-text search, Meilisearch or Typesense may be enough, and we will say so plainly.
Elasticsearch development services
Search implementation
Product, document or site search with explicit mappings, multi-field analyzers, autocomplete through search_as_you_type or edge n-grams, typo tolerance via fuzziness, and facets built on keyword fields and aggregations.
Relevance tuning
Judged query sets, the Ranking Evaluation API, field boosts, function_score and rank_feature signals such as popularity or freshness, adjusted through measurement rather than guesswork.
Vector and hybrid search
dense_vector fields with HNSW indexes, semantic_text or external embedding models, and hybrid retrieval combining BM25 with kNN through reciprocal rank fusion for RAG and semantic search.
Ingestion and sync pipelines
Change data capture from PostgreSQL or MySQL through Debezium and Kafka, ingest pipelines for enrichment, the Bulk API with tuned refresh intervals, and alias-based reindexing without downtime.
Logs and observability data
Data streams, index lifecycle management across hot, warm, cold and frozen tiers, searchable snapshots, and Kibana dashboards and alerting for application and security telemetry.
Cluster operations and upgrades
Shard sizing, node roles, snapshot repositories, ECK on Kubernetes or Elastic Cloud setup, rolling upgrades, and moves between Elasticsearch and OpenSearch when licensing or hosting requires it.
Why teams choose Krapton for Elasticsearch
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Relevance measured, not argued
Every ranking change is tested against judged queries and click data, so debates about whether search improved are settled with precision and recall from your own data.
Primary database stays primary
Elasticsearch is fed from your system of record through CDC or an outbox, with full reindex procedures documented, so a lost index is an inconvenience, never data loss.
Mappings planned for change
Indexes sit behind aliases and versioned templates, so analyzer or field changes roll out through a reindex and an atomic alias swap without interrupting search traffic.
Clusters sized from evidence
Shard counts, heap settings and node roles come from measured document sizes, query rates and retention needs, avoiding the oversharding that slows many self-managed clusters.
How a Elasticsearch project runs with Krapton
01
Search discovery
We collect real queries, zero-result searches, business rules and data sources, then define with you what a good result means for your users.
02
Index design
Mappings, analyzers, synonyms and document structure are prototyped on a sample of real data and tested against the judged query set before building further.
03
Pipeline and API build
Sync pipelines, search APIs, facets and interface integration are built in sprints, with relevance and latency checked against baselines on every change.
04
Launch and tuning
After launch we watch query latency, zero-result rates and cluster health, tune ranking from user behavior, and document reindex and recovery procedures.
The Elasticsearch stack we build with
Elasticsearch development: questions answered
What does an Elasticsearch development company deliver?
It delivers working search or analytics, not just a running cluster. That means index mappings and analyzers suited to your data, ingestion that keeps the index in sync with your primary database, search APIs with facets and autocomplete, relevance measured against real queries, and cluster operations covering snapshots, upgrades and lifecycle policies. Krapton also audits existing deployments where results are poor or clusters are unstable.
Do we need Elasticsearch, or is PostgreSQL full-text search enough?
PostgreSQL full-text search is enough for many internal tools and modest catalogs, and it avoids running another system. Elasticsearch earns its place when you need fine-grained relevance tuning, language-specific analyzers, typo tolerance, rich faceting over large datasets, hybrid vector search or log analytics at volume. Meilisearch and Typesense sit between the two for simpler product search. We compare options against your real queries before you commit.
Elasticsearch or OpenSearch: which should we use?
Both descend from the same codebase and handle core search similarly, but they have diverged since the fork. Elasticsearch offers Elastic’s newer features, such as semantic_text, ELSER and retrievers, plus Elastic Cloud. OpenSearch is licensed under Apache 2.0 and powers Amazon OpenSearch Service. Hosting preference, licensing policy and the specific features you need usually decide it, and migration between them is possible when circumstances change.
How do you change mappings on a live Elasticsearch index?
Mostly by reindexing, because existing field mappings cannot be changed in place. Applications query an alias rather than an index name; we create a new index with updated mappings and analyzers, reindex into it while the sync pipeline writes to both, verify counts and sample queries, then swap the alias atomically. Adding a brand-new field is simpler, though existing documents need an update by query to populate it.
What drives the cost of Elasticsearch development services?
Cost depends on data volume, how many sources feed the index, relevance expectations and operational scope. A single product catalog with one sync source is far simpler than multilingual search across several systems with vector retrieval. Retention requirements for log data, high-availability targets, compliance controls and the choice between self-managed clusters and Elastic Cloud also shape both the build effort and the ongoing hosting bill.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.