Captain

What do they actually do

Captain provides an API-first search and retrieval service that lets teams query large collections of unstructured documents—like PDFs, files in S3, spreadsheets, and scanned images—in plain English. It handles ingestion, OCR, chunking, and indexing for you, then returns answers with references back to the source documents. Teams can use a hosted Studio or call the API/SDK directly, including OpenAI-style tool/function calling for LLM workflows (runcaptain.com • docs.runcaptain.com).

The product positions itself as a more reliable, auditable alternative to building custom RAG/embedding pipelines. It integrates with many enterprise data sources (the site advertises "1,000+" connectors) and is set up for gated, enterprise-style access and deployments (runcaptain.com • runcaptain.com/studio).

Who are their target customer(s)

• Legal and contract review teams: They need precise, citation-backed answers from contracts and legal docs and cannot accept hallucinations or fuzzy relevance common in ad‑hoc RAG pipelines (docs.runcaptain.com • runcaptain.com).
• Compliance and audit teams: They must run repeatable queries and produce verifiable evidence for regulators, requiring deterministic retrieval with provenance rather than brittle, hand‑tuned embedding stacks (docs.runcaptain.com • runcaptain.com).
• Support and knowledge‑base teams: Agents and customers need fast, accurate answers from FAQs, docs, and ticket history; existing search/RAG often returns inconsistent or irrelevant responses and takes ongoing maintenance (docs.runcaptain.com).
• Research teams (R&D, academic): They query large corpora of papers/reports and need exact facts with source links across diverse file types (PDFs, spreadsheets, scans); current pipelines are fragile and slow to scale (docs.runcaptain.com).
• Enterprise engineering/platform teams: They don’t want to build and operate custom embedding indexes and RAG infra across many sources; they want a managed retrieval layer with connectors and auditable results (runcaptain.com • runcaptain.com/studio).

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led outreach to legal, compliance, and large support teams with a paid 4–6 week pilot: ingest a representative document set, deliver sample answers with full citations, and share before/after accuracy and auditability metrics. Turn each pilot into a case study and a repeatable onboarding playbook.
• First 50: Use references from early pilots to run a small sales/SDR motion with vertical playbooks (legal, audit, support, research) and templated demo kits. Offer standardized pilot packages that cover implementation while keeping time‑to‑value short; begin select channel relationships with systems integrators and legaltech consultancies.
• First 100: Run a two‑track motion: (1) lower‑touch pilots and self‑serve onboarding for smaller teams using prebuilt connectors, and (2) staffed enterprise sales + CS for larger accounts needing custom ingestion/governance. Add marketplace listings, publish case studies/audit evidence, and launch a reseller program.

What is the rough total addressable market

Top-down context:

Conservative core TAM for a production-grade, non‑RAG document retrieval layer is roughly $20–25B, combining enterprise search (~$4.9B 2023), knowledge management software (~$20.2B 2024, counted conservatively), and intelligent document processing (high‑single to low‑double‑digit billions) (Grand View – enterprise search • Grand View – knowledge management software • Fortune BI via SolutionsReview – IDP). An expanded upper bound including adjacent compliance/GRC and legal‑tech markets brings the total to roughly $80–90B (Mordor – compliance software • Fortune BI – legal technology).

Bottom-up calculation:

Focus on mid‑to‑large enterprises with heavy unstructured content and compliance needs (≈50,000 global). If Captain lands one primary deployment per enterprise at an average ACV of ~$200k–$250k (retrieval layer + connectors + support), that implies ~$10–12.5B. If adoption extends to two deployments per enterprise (e.g., legal and support), the bottom‑up expands toward ~$20–25B.

Assumptions:

• ~50,000 global enterprises with sizable unstructured data and governance requirements
• Average ACV ~$200k–$250k for enterprise retrieval (includes connectors/ingestion/support)
• 1–2 deployments per enterprise across major functions (e.g., legal, compliance, support)

Who are some of their notable competitors

• Pinecone: Managed vector database for embeddings used in semantic search/RAG; teams still build embeddings and pipeline logic themselves, unlike Captain’s managed retrieval layer (Pinecone docs).
• Weaviate: Open-source vector database with vector search and model integrations; embedding‑first approach versus Captain’s non‑embedding, provenance‑focused retrieval (Weaviate docs).
• LlamaIndex: Developer framework for connectors, indexes, and RAG pipelines; a build‑it‑yourself toolkit rather than a hosted, auditable retrieval service with enterprise connectors/Studio (LlamaIndex docs).
• Elastic / Enterprise Search: Enterprise search platform (Elasticsearch + connectors) for internal search/analytics; powerful general‑purpose search, but not a drop‑in managed retrieval layer with deterministic provenance focus like Captain.
• Algolia: Hosted search‑as‑a‑service for website/docs search (incl. DocSearch, NeuralSearch); customers manage indexing and relevance, versus Captain’s file‑first, audited retrieval for LLMs (DocSearch).