Docubix
Skip building your own RAG pipeline - ship a cited AI assistant this week
About Docubix
Docubix targets a decision every team shipping an AI feature eventually faces: build the retrieval pipeline or buy it. Building means embeddings, a chunking strategy that does not split sentences in stupid places, a vector store, retrieval tuning, and citation logic that reliably points at the right passage. That is several weeks of work before the feature does anything a user notices, and none of it is your product. Docubix provides it as an API, so a document-grounded assistant with working citations becomes a week of integration rather than a quarter of infrastructure.
Citations are treated as a first-class output rather than an afterthought, which is the right emphasis: a documentation assistant that answers confidently without showing its source is a liability, because a support agent will repeat what it says to a customer. Docubix returns the source document and page with every answer. Setup is three steps from folder to working assistant. The named use cases are SaaS products and apps, customer support teams, internal wikis and onboarding, and course platforms. Plans are metered by knowledge bases, document count, monthly queries and storage, with API access and citations available even on the permanently free tier.
Upload your documents into a knowledge base; the free tier allows one base with up to twenty documents, and Pro allows ten bases with up to a thousand. Docubix handles the parts you would otherwise build, including chunking, embedding and indexing, with no configuration decisions required about chunk size or overlap. Query through the API and answers come back grounded in your documents with the source and page attached, so anything surprising can be checked in one click. Because API access and citations are included on the free tier, the integration can be built and tested end to end before any spend, and scaled up when query volume justifies it.
- •Managed RAG Pipeline - Embeddings, chunking and indexing handled, so none of it appears in your codebase
- •Citations on Every Answer - Source document and page returned with each response, which is what makes the output usable in support contexts
- •Three-Step Setup - From a folder of documents to a working assistant without a configuration project
- •API Access on Free Tier - Build and test the full integration before paying anything
- •Multiple Knowledge Bases - Ten separate bases on Pro, so different products or audiences stay isolated
- •Documented Capacity Limits - Documents, queries and storage stated explicitly per plan rather than described vaguely
- •Broad Use Case Fit - SaaS product assistants, support teams, internal wikis and course platforms
- •Free Tier That Is Not a Trial - 20 documents and 100 queries monthly, permanently
For founders and small engineering teams shipping an AI feature who have costed out building retrieval themselves and would rather spend those weeks on their actual product. It suits support teams wanting a documentation assistant whose answers can be verified before being relayed to a customer. Internal tooling teams get the same benefit for wikis and onboarding material. Course platforms can make their content queryable. Teams with very high query volumes or unusual retrieval requirements will eventually outgrow a managed service, but that is a good problem to have later rather than a reason to build first.
