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Give AI agents accurate knowledge search that scales
Captain delivers the most accurate file search engine built for AI agents. We’ll index data from the sources folks already use like S3, SharePoint, and Google Drive, and easily scale multimodal, petabyte-level content search. We’re the Snowflake for Unstructured Data. Captain tops the Open-RAG-Benchmark with over 20% higher accuracy than standard RAG pipelines. We achieve this through robust data processing techniques like embedding normalization across modalities, ensuring that representations cluster by semantic content rather than data type.
Captain provides accurate, multi-modal knowledge search capabilities with scalable indexing and retrieval. It emphasizes high accuracy across video, documents, images, and supports enterprise-grade security and governance. The product targets teams needing fast, authenticated access to internal data across cloud and on-prem sources.
Captain offers a universal indexing and search platform with auto OCR, embeddings, and multi-modal indexing for files (documents, images, audio, video). It provides API-based querying, a vector database, and re-ranking for improved accuracy. It integrates with cloud storage and collaboration tools, supports granular role-based access, SOC 2 security, and on-premise BYOC options. Pricing is credit-based, with different tiers (Free, Startup, Enterprise) and a usage model that charges per indexing credit and per query, plus optional bulk discounts and dedicated support for enterprise.
Who it’s for: Teams and enterprises needing accurate, scalable knowledge search across video, docs, images, and multi-modal data; organizations requiring RBAC, SOC 2 compliance, and integration with cloud/data lake ecosystems.
Backed by Y Combinator, mentions of 2026 updates, enterprise pricing, onboarding and talk to engineer options; active pricing page and production-ready infrastructure details.
CEO @ Captain, Prev. IEEE, Founded CyberSpace (CTF hacking group), Recognized by the U.S. Congress for leadership in community software projects, Avid designer, Dropped out of Purdue
CTO @ Captain | Published AI NLP Researcher | Built OCR engines @ Boar's Head | Scaled high-performance RAG pipelines for the past 4 years | Built a Cross Region L2 Datacenter
Search files with much higher accuracy than RAG (Avg improves 78% → 95%)
Captain offers a general-purpose knowledge search engine for large text and multimodal files, designed to improve retrieval accuracy beyond RAG. It uses an infinite context window, distributed retrieval across multiple LLMs, and a map-reduce step to deliver a single output, targeting enterprise AI teams and knowledge workers seeking higher-accuracy unstructured data search.
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