miniSoft · miWork
Enterprise document search and RAG
Connect scattered documents and knowledge to an AI-assisted workflow.
Retrieval-augmented generation searches for relevant material and uses it to inform an AI response. miniSoft reviews the documents, business data and real questions to define how retrieval and answers should be evaluated.
PDFs, HWP/HWPX and Office files containing tables and images may require extraction and structuring first. miWork OCR and document-processing capabilities can support that workflow; representative files are used to validate extraction quality.
What we review first
- Document formats, quality, OCR and structuring requirements
- Knowledge scope and document update workflows
- Document access permissions and external-transfer restrictions
- Retrieval evidence and response quality on representative questions
Document AI delivery steps
- Collect representative documents and real business questions
- Design extraction, document structure and retrieval units
- Compare retrieved evidence and answers to evaluate quality
- Define updates, access and operations before integration
Technical background and related products
Our Labs articles explain RAG, Graph RAG and OCR for document retrieval and structuring. The miWork site introduces the related enterprise AI and document-processing products.
Business capabilities and references →Does RAG eliminate incorrect answers?
No. Answers still depend on retrieval and document quality. Evaluate evidence and answers using real questions and define how the system responds when supporting information is unavailable.