Ragdoll AI offers a Knowledge Base‑as‑a‑Service platform that lets developers and businesses create, manage, and deploy AI‑powered knowledge bases without building RAG infrastructure from scratch. Users can connect to multiple data sources—including Google Drive, Notion, S3, PostgreSQL and MySQL—customize retrieval with hybrid Vector or LightRAG algorithms, and test accuracy before publishing via API or built‑in chat. The service handles data integration, pipeline orchestration, scaling, and continuous engine updates, enabling rapid production‑ready deployments.
Funding
Funding not disclosed
Founders
Product
Problem
Developers and businesses need to integrate diverse data sources into AI applications and provide accurate, up-to-date knowledge retrieval, but building and maintaining Retrieval‑Augmented Generation (RAG) pipelines requires extensive engineering effort, infrastructure management, and continuous model updates.
Solution
Ragdoll AI offers a managed Knowledge Base‑as‑a‑Service platform that abstracts the entire RAG workflow. Users connect structured and unstructured data via out‑of‑the‑box connectors, fine‑tune retrieval parameters, and deploy production‑ready RAG through simple API calls or code‑editor plugins. The service handles data chunking, embedding, hybrid retrieval (fast Vector RAG or deeper LightRAG), and synthesis, while automatically scaling infrastructure and applying continuous engine upgrades. Built‑in testing tools let users validate accuracy before launch, and security features such as TLS encryption and GDPR/CCPA compliance protect data. This enables developers to focus on application logic and AI personality design rather than low‑level RAG engineering.
Target Audience
Primary customers are AI developers and product teams building chatbots, copilots, or domain‑specific agents, as well as startups and enterprises that need internal knowledge‑driven AI services.
Features
- Unified connectors for documents, cloud storage, and relational databases (e.g., Google Drive, Notion, S3, PostgreSQL, MySQL) with no data migration required
- Hybrid retrieval options: high‑speed Vector RAG and context‑rich LightRAG selectable per use case
- End‑to‑end pipeline orchestration covering chunking, embedding, retrieval, reranking, and synthesis
- Automatic infrastructure scaling and versioned engine updates without user intervention
- Built‑in testing sandbox that measures retrieval accuracy and performance prior to deployment
- Flat‑rate pricing with unlimited knowledge bases, documents, and searches
- Enterprise controls including multi‑tier permissions, advanced security policies, and SLA‑backed support