No, not under the stated data constraint, because Jev reranking sends candidate result text to TypeSafe’s servers.

• The README says that adding "rerank": {} to _search sends the top results to Jev.
• On the public FiQA test, nDCG@10 increased from 0.24 to 0.36 at a total cost of about $0.64 for 648 searches. This indicates a possible ranking benefit, not an offline benefit.
• The feature remains off until a key is configured, and [rerank] enabled = false disables it explicitly.
• XERJ also provides local /v1/systemone and /_decide, but these require previously labeled examples and are not a drop-in equivalent for general Jev reranking.

Sensitive data should therefore keep Jev disabled; enabling it only makes sense when outbound data is permitted and its cost and latency are acceptable.

Yes, because the README identifies reference coding and coding-agent integration as primary use cases and provides a one-command MCP and skill setup.

• xerj init wires XERJ into Claude Code and Cursor; autoindex can index code, documents, logs, and PDFs.
• xerj search returns ranked passages with file names and line numbers, while xerj def locates definitions by symbol, reducing the need to open complete files.
• The README reports a controlled case where agent output tokens fell 2.7x at the same 16/16 solve rate; this is a case-study result, not a universal guarantee.

If the agent must write, delete, or rebuild indexes, the README does not specify fine-grained MCP permissions, so that security boundary remains to be verified.

It depends: local deployment and the default lexical embedder fit an offline constraint, but neural semantic retrieval requires model files to be staged in advance.

• The README provides an air-gapped deployment recipe and says prebuilt binaries cover Linux, macOS, and Windows on x86-64 and arm64.
• The default lexical embedder runs offline, so basic indexing, full-text search, and some semantic functionality do not require an external service.
• Neural mode requires three model files locally before the first semantic operation; the README does not state their download source, integrity checks, or size.
• Jev reranking sends result text to TypeSafe’s servers, which conflicts with a no-egress policy for internal code and audit material unless it remains disabled.

Basic offline search is therefore feasible, while neural-mode preparation and Jev’s offline deployment details remain blockers.

Yes for narrowing the audit search space, but not as the sole mechanism for vulnerability confirmation or complete program-semantic analysis.

• The README’s WordPress core security-audit case covered 1,492 PHP files using roughly 26,000 tokens, described as about 0.5% of the tokens needed to load the whole tree.
• xerj search returns ranked passages with file and line references, while xerj def returns definition file, line, and signature information.
• XERJ’s code indexing preserves symbols and line numbers rather than treating code only as flat text, which is useful for locating definitions and related passages.
• The project analysis notes that cross-file calls, generated macros, dynamic loading, build conditions, and runtime configuration may not be explained accurately by indexing alone.

It is suitable as the audit retrieval layer, but the final finding should not rely only on retrieved snippets.

Yes: the README provides a stable-Rust requirement, source-build commands, and a deployment model without a JVM.

• Rust is the project’s main language; source builds require a stable Rust toolchain and explicitly use cargo build --release -p xerj-server.
• The README describes the artifact as one static binary with no JVM and no dependencies, with prebuilt targets for Linux, macOS, and Windows on x86-64 and arm64.
• After building, the server can be started with ./target/release/xerj --insecure --data-dir ./data, and the node can then be checked through http://localhost:9200.
• The Elasticsearch compatibility suite also has a runnable Rust command for post-build API verification.

The README does not explain platform-specific toolchains, cross-compilation setup, package signing, or reproducible-build guarantees, so a complex release pipeline still needs additional validation.

It depends: common Elasticsearch REST workflows are largely portable, but XERJ should not be treated as a complete replacement for Elasticsearch internals.

• The README lists indices, documents, bulk, search, aggregations, mappings, kNN, scroll, reindex, and _cat, and says Kibana and official clients can connect directly.
• The conformance suite currently passes 1366 of 1369 cases, so three compatibility gaps remain.
• Scroll is a bounded upfront snapshot. If the exact total exceeds the snapshot window, XERJ returns 400 instead of silently truncating; the README recommends search_after for large result sets.
• Its deployment advantage is a single static binary with no JVM, but the README does not promise Elasticsearch execution plans, plugins, or every edge API behavior.

It is a reasonable fit for standard REST-based local search, but not an automatic replacement for systems dependent on full Elasticsearch semantics.