BlogComparisons

Captain vs. Elasticsearch

Edgar BabajanyanCTO & Co-Founder

5 min read

One component, or the whole platform

Elasticsearch indexes the text Tika hands it. It does not read a scanned page, label a chunk as a table or a footnote, describe a figure, mask a name, or promote a collection from staging to production. Its multimodal field is a technical preview, not recommended for production. Teams cover each gap with another vendor or a pipeline they own, and every seam between them is a contract, an integration and an on-call rotation.

Captain runs the whole retrieval layer, from your bucket to the ranked result.

Point Captain at the bucket and it takes over from there. It parses every file it finds, scans and handwriting included, chunks by layout, embeds, and keeps the index in step as files are added, changed or removed. PII is masked on the way in. Queries come back as reranked, cited chunks, and the tuning keeps up with the documents: evals drawn from your own files score every adjustment before it lands, so search gets better without a relevance project on your side.

Why choose Captain over Elasticsearch?

One platform covers what a team otherwise assembles around Elasticsearch: OCR and figure descriptions, layout-labeled chunks, cross-modal reranking, an entity model for PII, managed sync with rollback, and environment promotion.

Parsing included
Each file takes the pipeline it needs: text, page layout, or a vision model for scans and figures. A JavaScript parsing script for JSON files when you need one.
Metadata at ingest
Every chunk arrives with its layout role, page, bounding box and place in the document, with nothing for you to model.
Layout-aware chunking
Tables, headings and figures keep their shape through indexing.
20+ sources natively
Indexed Search across S3, GCS, Azure, Drive, SharePoint, Notion, Dropbox and Snowflake; Live Search across Slack, Linear, Jira, Confluence, Gmail, HubSpot and NetSuite.
Native multimodal
Captain indexes images, audio and video directly and reranks across all of them.
Operations included
Sync keeps the index in step with your files. Jobs roll back, collections copy to production, and PII is masked before anything is embedded.

How the platforms compare

Chunking

Captain

Each chunk ends where the layout does: a table, a heading, a paragraph

Elasticsearch

The semantic_text field's fixed-size default, or a chunking library you wire in

Parsing & structure

PDFs, scans and images

Elasticsearch

Apache Tika through the attachment processor; scans need Tesseract or an OCR service you run

Graph relations

Captain

Typed chunk relations (a claim linked to its risk statement, a figure to its caption), with direction control

Retrieval

Configuration

Captain

One parameter, semantic_ratio, from pure keyword to pure semantic

Operations

Compliance

Captain

SOC 2, HIPAA, GDPR, more on request

Edgar Babajanyan

CTO & Co-Founder · Captain

Edgar is co-founder and CTO of Captain, building self-tuning file search for AI agents.