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Tune boosts, facets, synonyms, query rewriting, and ranking rules from a governed UI.
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AI Search Platform
NextSearch
Hybrid keyword, vector, semantic, and rules-driven search in one enterprise control plane.
NextSearch gives teams the search relevance, retrieval, personalization, analytics, and operational governance they usually stitch together across Elasticsearch, Coveo, Lucidworks, OpenSearch, and Solr.
Positioned to replace
Elasticsearch 9.4, Coveo, Lucidworks, OpenSearch 3.6, Solr 10
Hybrid
BM25 + vector + semantic retrieval
Rules
Query rules, boosts, synonyms, facets
Live
Search quality and latency telemetry
What Makes It Different
A productized NextBrick operating layer built around real enterprise workflows, not a thin wrapper around one vendor.
Vector Search Ready
Run hybrid retrieval for RAG, support search, product discovery, and enterprise knowledge portals.
Search Analytics
Track zero-result queries, top prompts, click behavior, latency, and content gaps.
Multi-Engine Operations
Operate Elasticsearch, OpenSearch, Solr, and vector stores with a consistent control layer.
Reviewed May 10, 2026
NextSearch 1.0 Local Review
The NextSearch 1.0 review documents a Lucene-powered search runtime with implemented core API compatibility, a CLI/server split, benchmark tooling, and a practical parity roadmap against OpenSearch and Elasticsearch.
Review Highlights
- Built on Apache Lucene 10.4.0 modules including core, analysis-common, queryparser, and queries.
- Runtime includes Java server and CLI implementations, Bash/YAML operations, and a Python benchmark harness.
- Core APIs are mostly implemented: create/delete index, _search, _bulk, _doc CRUD, _aliases, _cat/indices, and _cluster/health.
- Query support includes q, match, and multi_match, with bool/range/sort tracked as partial parity work.
- Aggregations include terms and cardinality, with stats and date histogram marked for upcoming parity expansion.
Parity and Deployment Notes
- The reviewed local URL returned a frontend SPA shell, so API parity verification should target the confirmed NextSearch backend listener directly.
- The current license policy allows permissive licenses and blocks copyleft or service-restricted licenses for core dependency intake.
- Security, multi-tenant controls, and distributed durability remain roadmap items for full OpenSearch and Elasticsearch parity.
Enterprise Search Engine Battlecard 1.0
1.94x faster than Elasticsearch at half the p99 latency.
Same hardware, same index, one JAR, no Docker bloat, no enterprise license lock-in.
Battlecard
Versus Elasticsearch, Solr, and OpenSearch
1.85x faster than Solr 10
1.94x faster than Elasticsearch
2.73x faster than OpenSearch
$650K saved vs Elastic Cloud
| Metric | NextSearch | Solr 10 | ES 9.3.1 | OpenSearch |
|---|---|---|---|---|
| QPS | 3,174 | 1,712 | 1,639 | 1,163 |
| p50 latency | 8.65 ms | 14.12 ms | 15.59 ms | 19.32 ms |
| p95 latency | 19.67 ms | 42.79 ms | 39.62 ms | 72.68 ms |
| p99 latency | 39.41 ms | 94.53 ms | 59.75 ms | 134.25 ms |
| RAM snapshot | 327 MiB | 742 MiB | 982 MiB | 918 MiB |
| Image size | Single JAR | 756 MB | 989 MB | 1.25 GB |
Highest QPS on identical hardware running the same workload.
3x lower RAM than major search engines, suitable for edge nodes and smaller VMs.
Apache 2.0 core with no ELv2 lock-in.
AI-native retrieval with dense, sparse, ColBERT, semantic rerank, and agent-callable APIs.
Use Cases
- Support knowledge search
- Commerce product discovery
- RAG retrieval layer
- Enterprise intranet search
Why Teams Choose It
- Own relevance logic
- Avoid search black boxes
- Unify lexical and semantic retrieval
- Measure quality continuously
Product Modules
The product is packaged into clear capabilities so teams can adopt incrementally and expand as the platform matures.
Indexes
Query Rules
Typeahead
Facets
Vector Retrieval
Analytics