How Postal Savings Bank Built a Standardized, AI‑Powered Demand Knowledge Base with a Closed‑Loop Workflow

1. Challenges and Pain Points

Demand documents are the blueprint of software development, but traditional demand management suffers from four major problems: inconsistent document quality, difficulty reusing knowledge, limited large‑model applicability, and one‑way knowledge flow, leading to frequent changes and rework.

2. Standard System and “Three‑Dimensional Collaboration + Closed‑Loop Iteration” Solution

Postal Savings Bank designed a top‑down standard covering data quality, technology selection and security compliance, and built a “three‑dimensional collaboration + closed‑loop iteration” framework. The framework integrates historical demand data, parses heterogeneous documents, and creates a full lifecycle pipeline from demand authoring to feedback‑driven optimization, achieving T+1 knowledge freshness.

Key standards include:

Data quality: authority, scenario relevance, timeliness with automated upstream data feeds and version management.

Technical selection: high‑coverage document parsing, adaptive text slicing, enterprise‑grade vector storage, hybrid retrieval (vector similarity + keyword + tag filtering), and feedback data processing.

Security compliance: on‑premises deployment, data desensitization, and strict access control.

3. Core Modules and Workflow

The knowledge base consists of three core modules—knowledge management, tag management, and knowledge retrieval—plus a feedback‑iteration mechanism.

Knowledge Management handles multi‑source data ingestion, format‑agnostic parsing, adaptive slicing, vectorization, and storage in an enterprise vector database, with daily T+1 updates.

Tag Management builds a three‑layer tag hierarchy (business, document, paragraph) and automatically tags extracted sections such as “business overview”, “functional requirement”, “report requirement”, etc., turning a flat knowledge pool into a multidimensional semantic network.

Knowledge Retrieval offers hybrid search that combines vector similarity, keyword matching, and tag filtering, delivering precise, context‑aware results in seconds.

Feedback Iteration collects user satisfaction scores, error reports, and suggestion data, prioritizes them, and feeds the improvements back into the knowledge management module through a “small‑scale test → effect verification → full rollout” process.

4. Practical Outcomes

To date the system has integrated three core data sources, parsed seven common file formats, and structured 17,000 historical demand documents. It now supports automatic demand draft generation, duplicate detection, semantic retrieval, and content‑level optimization, dramatically lowering authoring effort, preventing redundant work, and improving document quality.

5. Conclusion

The closed‑loop practice demonstrates that data‑driven, standardized knowledge assets can power intelligent software development in the banking sector, creating a virtuous cycle where richer knowledge leads to better AI assistance, which in turn enriches the knowledge base.