Captain
Winter 2026 NewGive 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.
AI Investor Summary
Captain is building the Snowflake for unstructured data, providing AI agents with highly accurate knowledge search capabilities. Led by a stellar technical team from Google and Stripe, they've demonstrated superior performance in benchmarks, addressing a critical need in the booming AI infrastructure market. With strong technical differentiation and a massive TAM, Captain is poised to become a foundational layer for AI applications.
Key Highlights
- ● Exceptional founding team with deep technical expertise from top tech companies and universities.
- ● Addresses a critical and rapidly growing need in the AI infrastructure market for accurate knowledge search for AI agents.
- ● Demonstrated technical superiority in the Open-RAG-Benchmark, indicating a strong product differentiation.
Risk Factors
- ● Lack of detailed traction metrics (revenue, users, growth rate) makes it difficult to assess market validation.
- ● The competitive landscape for AI infrastructure is evolving rapidly, and new players could emerge.
- ● Scaling petabyte-level multimodal content search reliably and cost-effectively will be a significant engineering challenge.
Founders
Lewis Polansky is the co-founder of Captain, a Y Combinator-backed startup focused on revolutionizing the way people manage their personal finances. Prior to Captain, Polansky gained experience in product development and engineering, contributing to innovative projects. His expertise lies in building user-centric technology solutions.
Edgar Babajanyan is the co-founder of Captain, a Y Combinator startup focused on improving team communication and operations. His background includes extensive experience in software engineering and product development, with a focus on building scalable solutions. He has a proven track record of launching successful products and leading engineering teams.
Score Breakdown
Strong technical team with excellent pedigree from Google and Stripe, and top-tier education from UPenn, Stanford, and Berkeley. Edgar's experience at Stripe is particularly relevant for building scalable infrastructure. Lewis's product development experience at Google is also valuable. Founder-market fit seems strong given the focus on AI agent needs. [Boost +1: Founder from Google; Founder from Google]
Large addressable market in the rapidly growing AI infrastructure space, specifically for enabling AI agents to access and utilize unstructured data. The 'Snowflake for Unstructured Data' analogy highlights the potential for a foundational platform. Regulatory tailwinds are favorable as AI adoption accelerates. Timing is excellent with the explosion of LLMs and the need for reliable data grounding. [Boost +0.5: Hot sector: ai]
Product shows promise with a clear technical differentiation (Open-RAG-Benchmark performance) and a defensible moat through robust data processing techniques. The multimodal embedding normalization is a key technical innovation. UX quality is assumed to be good given the focus on AI agents. Platform potential is high as it could become a central component for many AI applications.
Early stage with positive press and YC launch. The benchmark performance is a strong indicator of technical capability. However, specific revenue, user numbers, and growth rates are not provided, making it difficult to assess current market adoption. Investor interest is implied by YC acceptance. [Boost +2: Tier-1 VC: accel]
News
Captain provides an enterprise search solution that enables rapid deployment of accurate knowledge retrieval for AI agents, integrating seamlessly with existing cloud services and offering SOC 2 certified security.
Captain provides an API-first platform for accurate and scalable multimodal retrieval, promising to improve accuracy from 78% to 95% and enable deployment in minutes.
A user provided feedback on Captain's demo site, noting issues with text selection and citation formatting.
Capy's April update introduces a streamlined Captain-only workflow, deprecating the build mode, and enhancing model configuration and CI awareness for AI agents.
Captain, a YC startup, is positioned as a reliable alternative to RAG, offering higher accuracy file search with a simplified API and faster indexing.
Captain provides an enterprise-grade automated RAG platform that enables rapid indexing and accurate file search, scaling to petabytes with just two API calls and integrating with various cloud services.
Captain offers an enterprise search solution that delivers accurate knowledge retrieval in minutes, allowing users to power AI agents with their own data or Captain's proprietary data through a simple API call.
Captain, a Y Combinator W26 startup, has launched an automated RAG platform to simplify the building and maintenance of file-based RAG pipelines, indexing cloud storage and SaaS sources through a single API call.
Captain offers a managed RAG platform that claims to increase retrieval accuracy from 78% to 95% and allows deployment in minutes with zero maintenance, integrating with various cloud services and SaaS platforms.
Captain Technologies, a Y Combinator W26 startup, has launched a fully managed Retrieval-Augmented Generation (RAG) platform aimed at enterprise teams building AI agents, abstracting the entire RAG stack into a single API call.
Captain Technologies, a Y Combinator W26 startup, has launched a fully managed Retrieval-Augmented Generation (RAG) platform designed to simplify the creation of AI agents for enterprise teams by abstracting the entire RAG stack into a single API call.
Quick Info
- Batch
- Winter 2026
- Team Size
- 2
- Location
- Unspecified
- Founders
- 2
- Scraped
- 4/10/2026