Captain
Product & Competitive Intelligence
Delivers 95%+ accurate knowledge search across unstructured enterprise data, beating standard RAG.
Company Overview
Builds an API-first enterprise AI platform that uses proprietary RAG pipelines, parallel LLM orchestration, and hybrid search to deliver 95%+ accurate knowledge retrieval across unstructured data (PDFs, images, spreadsheets, cloud files).
Competitive Advantage & Moat
Product Roadmap & Public Announcements
Studio UI and API/SDK access. SOC 2 certification. AWS S3 and Google Cloud Storage integrations. Multi-tenancy with RBAC. Multimodal data ingestion. Deterministic, cited answers. Effectively infinite context via parallel LLM querying. Documentation at docs.runcaptain.com. "Ask PG's Essays" demo site.
Signals & Private Analysis
Closed-core monetization strategy. Agentic orchestration features in active development. Likely upcoming SaaS connectors (Salesforce, Notion) and self-serve developer onboarding for mid-market expansion. Garry Tan personally coached this team — the only W26 company to get that distinction. Applied to YC 3 times with 4 pivots before acceptance.
Product Roadmap Priorities
Automated NLP, OCR, and computer vision pipelines ingest and make searchable PDFs, scanned documents, images, and spreadsheets without manual preprocessing.
Captain teaches AI to read not just typed text but also photos of whiteboards, scanned contracts, and messy spreadsheets—so nothing in your company's data falls through the cracks.
It's like hiring a multilingual assistant who can read handwritten notes, interpret pie charts, and scan legal contracts all before lunch—and then remember exactly where every detail came from.
SOC 2-certified, role-based access controls ensure that AI search results respect enterprise permissions, so users only see answers derived from data they're authorized to access.
Captain makes sure the intern can't accidentally ask the AI about the CEO's salary—every answer is filtered through the same security permissions your company already has in place.
It's like a library where every book has an invisible lock, and the librarian checks your ID badge before pulling anything off the shelf—even if you ask really nicely.
Parallel LLM orchestration enables 95%+ accurate, cited answers across massive unstructured enterprise data sets in real time.
Instead of asking one AI to read your entire filing cabinet, Captain sends dozens of AI readers to search in parallel and then has a senior editor combine their best findings into one perfect answer.
It's like having a team of 50 research librarians each search a different floor of the library simultaneously, then a head librarian cross-checks all their findings before handing you one perfect, footnoted summary.
Company Overview
Key Team Members
- Lewis Polansky, Co-Founder & CEO
- Edgar Babajanyan, Co-Founder & CTO
Founder Force Multiplier
Edgar Babajanyan (CTO, Purdue Network Engineering Dean's List) built production RAG for Confluence documentation, AI agents for customer service, and OCR models at Reality Interactive for 2.5 years. Published NLP research at Purdue, Weaviate vector DB certified, Head of SW Engineering at Purdue Autonomous Robotics Club (software used by Google/Amazon/Unitree). Lewis Polansky (CEO, Purdue Finance Honors) is a self-taught full-stack engineer, founded CyberSpace cybersecurity club (4 years, grew to 3 locations, trained national CyberPatriot semifinalists), Congressional App Challenge winner recognized by House of Representatives, Eagle Scout. Garry Tan's personal coaching pick — the only W26 team coached by the YC CEO.
Funding History
- 2023 | Captain founded.
- 2023 | $2.1M Seed from 19 investors.
- 2024-2025 | SOC 2 certification, Studio UI and API launch.
- 2026 | Accepted into Y Combinator W26 batch.
Competitors
- Enterprise Search: Elastic, Coveo, Microsoft Cognitive Search.
- RAG Platforms: LlamaIndex, LangChain, Vectara, Glean.
- Knowledge Management: Notion AI, Guru, Confluence AI.
- Document Intelligence: Hebbia, Docugami, Unstructured.io.
