Architecture & capacity planning
Right-size domains and self-managed clusters, define node roles, model shard growth, and design for availability before traffic reaches production.
Search engineering, end to end
OpenSearch consulting built for production
Design, migrate, optimize, and operate OpenSearch with engineers who understand the full search stack—from Lucene internals and shard topology to hybrid retrieval and incident response.
- Amazon OpenSearch Service
- Self-managed OpenSearch
- Elasticsearch migrations
- Neural & hybrid search
Architecture meets operations
Live cluster evidence, not slideware
OpenSearch consulting services
One team from architecture to operations
OpenSearch problems rarely stay in one layer. We connect infrastructure, data modeling, retrieval quality, security, and support into one accountable delivery plan.
Elasticsearch migration
Assess compatibility, translate mappings and pipelines, validate relevance, rehearse cutover, and move workloads with an explicit rollback plan.
Performance & cost optimization
Profile slow searches and ingestion, correct shard strategy, tune queries and caches, and align storage tiers with workload economics.
Relevance, vector & hybrid search
Improve lexical ranking, add semantic retrieval, combine BM25 and vector signals, and evaluate quality against business-relevant query sets.
Security & compliance
Implement TLS, encryption, fine-grained access control, SAML or OIDC, audit logging, tenant boundaries, and production security reviews.
Production support
Resolve cluster incidents, unassigned shards, indexing failures, latency regressions, upgrade risk, and recurring operational problems.
Delivery model
A migration plan you can defend
Moving from Elasticsearch—or correcting a fragile OpenSearch deployment—requires more than copying indexes. Our process makes compatibility, quality, performance, and rollback decisions visible before launch.
Request an architecture review- 01
Baseline the workload
Inventory versions, data volume, query and ingest patterns, integrations, SLAs, security requirements, and the failure modes your team sees today.
- 02
Design the target state
Produce a concrete architecture, sizing model, backlog, acceptance criteria, migration sequence, observability plan, and recovery path.
- 03
Prove it under load
Exercise representative data and queries, compare relevance, benchmark latency and throughput, and close gaps before production cutover.
- 04
Launch and operate
Execute the rollout, monitor cluster behavior, document runbooks, transfer knowledge, and provide ongoing OpenSearch support when needed.
NextSearch delivery workspace
See the system your consultants are working on
NextSearch brings cluster health, index administration, Query DSL testing, lexical and vector retrieval, connected data workspaces, and an AI copilot into one operator experience.
During an OpenSearch engagement, that means teams can inspect behavior, compare queries, explore mappings, and validate changes in a focused workspace instead of managing the project through disconnected screenshots and spreadsheets.
Engineering evidence
OpenSearch expertise demonstrated upstream
NextBricks contributed the published fix for OpenSearch pull request #22789, addressing test-cluster distribution isolation and Gradle immutable transform-cache safety.
67/67 integration tests passed
4/4 TestClusters scenarios passed
Repeated-cache reuse verified
Multi-node isolation verified
The technical brief documents the problem, reviewer feedback, implementation tradeoffs, functional scenarios, environment prerequisites, and final validation boundary. It is concrete evidence of how our engineers reason about OpenSearch reliability.
OpenSearch consulting FAQ
Straight answers before an engagement
What does an OpenSearch consultant help with?
An OpenSearch consultant helps teams make architecture, migration, security, relevance, performance, cost, and operational decisions. Nextbrick engagements can range from a focused assessment to hands-on implementation and ongoing production support.
Can Nextbrick migrate Elasticsearch to OpenSearch?
Yes. We assess version and feature compatibility, translate mappings and ingest pipelines, replay representative queries, compare relevance and performance, rehearse data movement, and plan cutover and rollback. We support both Amazon OpenSearch Service and self-managed OpenSearch targets.
Do you support Amazon OpenSearch Service and self-managed OpenSearch?
Yes. We work with Amazon OpenSearch Service as well as self-managed OpenSearch on AWS, Azure, Google Cloud, Kubernetes, and on-premises infrastructure. The architecture and operating model are tailored to the workload and ownership requirements.
Can you improve slow OpenSearch queries and high cluster costs?
Yes. We investigate query profiles, shard count and allocation, mappings, analyzers, refresh and merge behavior, cache use, hot threads, JVM pressure, storage, and ingestion patterns. Recommendations are validated against real workload evidence rather than generic tuning rules.
Do you implement neural and hybrid search in OpenSearch?
Yes. We design embedding pipelines, vector indexes, filters, hybrid retrieval, score normalization, reranking, and evaluation workflows. The goal is measurable search quality without losing the precision, latency, and controls expected from production search.
What is NextSearch?
NextSearch is Nextbrick's search operations and migration workspace. It brings cluster health, index administration, Query DSL testing, lexical and vector retrieval, connected data workspaces, and an AI copilot into one operator experience used to support consulting and evaluation work.
Make your OpenSearch platform easier to trust
Bring us the cluster, migration, relevance, or production problem your team needs to solve. We’ll help define the safest next move.
Start an OpenSearch assessment