2134v1.0.0

A company creates a RAGFlow dataset to store product documentation and troubleshooting guides. Support agents can then search the dataset to quickly retrieve relevant chunks and answer customer queries accurately.

A law firm ingests contracts and case files into a RAGFlow dataset. Parsing extracts key clauses and entities, and the search function enables rapid retrieval of relevant legal precedents or terms across large document collections.

Researchers upload PDFs of academic papers into a dataset, parse them to extract abstracts and references, and then use search to find specific studies or topics across hundreds of documents without manual sifting.

A corporation maintains a dataset of internal policies, onboarding material, and project reports. Employees can search for information, and the system provides direct chunk-level answers to common procedural questions.

Healthcare professionals upload medical journals and treatment guidelines into a dataset. Parsing extracts structured data, and the search function allows quick access to specific symptoms, dosages, or study results for clinical decision support.

Offer RAGFlow-powered knowledge bases as a monthly or annual subscription, where clients get dedicated datasets with ingestion, parsing, and retrieval capabilities. Revenue scales with the number of datasets and documents managed.

Expose the RAGFlow retrieval functionality as an API, charging per search or per chunk retrieved. This model suits developers who need to integrate domain-specific knowledge retrieval into their apps without managing infrastructure.

Offer end-to-end consulting to deploy and customize RAGFlow datasets for enterprise clients, including data migration, custom parsing rules, and integration with existing systems. Revenue comes from project fees and ongoing support contracts.