Document Processing · Enterprise Knowledge Search
Wuzhi needed an automated way to turn large volumes of paper-based and highly varied documents into accurate, searchable, and structured digital assets for government and enterprise customers without long manual processing cycles.
Reported impact
Wuzhi shortened delivery cycles for document-digitization projects, improved extraction accuracy, cut manual labor, and created reusable search and authoring experiences on top of the same structured knowledge foundation.
Working on Baidu AI Cloud's Qianfan platform, Wuzhi Technology built an end-to-end document-intelligence pipeline for digitizing paper archives and turning them into structured knowledge assets. The system combines OCR, layout analysis, large-model visual understanding, structured extraction, vectorization, and graph-database storage so scanned documents can move from image files into searchable semantic records. After recognition and layout parsing, the large model interprets document meaning, extracts target fields, and stores normalized outputs in a graph-backed knowledge layer. Vector embeddings then support hybrid search and multi-route retrieval, which Wuzhi uses to power downstream applications such as AI authoring, AI search, and AI voice assistants connected to customer knowledge bases and business systems. The result is a single flow for document ingestion, structuring, retrieval, and content generation rather than separate tools for each step.
Paper documents are scanned and passed through OCR and layout analysis, then Qianfan-based vision and language models extract fields and semantics into structured records. Those records are stored in a graph database, embedded through vectorization, and exposed through hybrid search and multi-route retrieval so AI search, writing, and voice-assistant applications can return grounded answers and generated content.
Processed data is stored in a graph database and then used for hybrid search and multi-route retrieval (original: 将数据自动结构化并存储至图数据库…系统支持混合搜索与多路召回功能).
Project efficiency improved 40% and error rates fell by more than 50% (original: 项目的整体处理效率提升了40%…错误率降低了50%以上).
The system combines OCR, layout analysis, large-model visual understanding, structuring, vectorization, and information extraction (original: 结合智能OCR、板式识别、LLM（大模型）视觉理解、结构化处理、向量化和信息抽取等技术).
Writing efficiency improved by more than 30% and retrieval time dropped 40% (original: 文案撰写和修订效率提升了30%以上…文献检索时间减少了40%).
Wuzhi built a full-process document-intelligence solution on Qianfan for paper-document automation and structured use (original: 首创"大模型+文档智能"全流程解决方案…实现纸质文献全流程自动化处理与结构化应用).
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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