Captain – Multimodal RAG Search That Scales
Captain connects to S3, SharePoint, and 20+ sources to index video, images, PDFs, and audio, then serves AI agents via a clean REST API or MCP server. YC W26.
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13 posts tagged #rag
Browse 13 posts tagged Rag, including practical setup notes, reviews, comparisons, and workflow patterns for engineers working with AI tools.
Captain connects to S3, SharePoint, and 20+ sources to index video, images, PDFs, and audio, then serves AI agents via a clean REST API or MCP server. YC W26.
Deploy a self-hosted RAG pipeline with built-in MCP tools for PostgreSQL, MSSQL, MySQL, SSH, and filesystem indexing. Works with Claude Desktop, Cursor, and any OpenAI-compatible client.
A full-featured desktop AI assistant that runs locally, supports multi-user workspaces, and connects to any LLM provider. MIT licensed, with built-in agents and document chat.
Airweave is an open-source context retrieval layer that syncs 50+ apps, databases, and docs into a unified LLM-friendly search interface for AI agents.
Ragtoolina adds semantic codebase RAG to any MCP-compatible AI coding tool, cutting token usage by 63% with one line of config. Self-hosted option included.
Open-source platform for building multi-task AI agents with a no-code interface, PostgreSQL backend, and support for OpenAI, Anthropic, and Gemini models
Captain (YC W26) automates RAG pipeline setup for S3, GCS, and Google Drive. Index unstructured data in minutes with multimodal search across docs, images, and sheets.
Captain turns your files into accurate, searchable knowledge. Upload any document and get an AI-powered RAG pipeline that scales to thousands of pages.
ZeroEntropy (YC W25) builds embedding models, rerankers, and managed search APIs for RAG pipelines. Uses novel Elo-score training to outperform OpenAI and Cohere on retrieval benchmarks.
Crawl, chunk, and vectorize any website into a queryable knowledge base. REST API with collections, scheduled updates, and built-in RAG for LLM applications.
Ingest documents into persistent navigable memory — tree-like hierarchy reconstruction, multi-modal parsing, and agentic RAG with evidence-based citations.
Chonkie is a lightweight, fast RAG chunking library for Python and TypeScript. Up to 33x faster than LangChain, supports semantic, token, sentence, and code chunking.
Captain automates building and maintaining RAG pipelines for unstructured data. Index S3, GCS, and Google Drive in minutes with one API call.