AI-Driven Tender Parser and Bidding Assistant

Engineering Challenges in Bid Document Processing

In tendering processes, engineers often grapple with voluminous unstructured bidding documents (PDFs, Word files), requiring manual extraction of response checklists, identification of potential risk points (e.g., qualification mismatches, timeline conflicts), compilation of material requirements, and drafting suggestions for bid submissions. This manual approach is time-consuming and prone to omissions, especially with lengthy or complex documents.

How the Skill Works: Core Capabilities and Key Steps

The Tender Document Parsing and Bidding Assistance AI leverages Shibo Technology's industry-wide bidding data API (via endpoints like /outer-gateway/bid/) to automate these tasks. Its workflow is as follows:

1. Document Parsing and Information Extraction: The skill first fetches tender file attachments and structured data using interfaces such as getZTBProjectFiles and getZTBStructreDetail. It then references references/national-keywords.json for trigger keywords and references/api-mapping.md for API mapping, automatically selecting optimal endpoints (e.g., searchProjectApi for searches, getZTBProjectDetail for main content).

2. Analysis and Output Generation: Parsed data undergoes AI text processing (via aiSearchSubmitPolling or ztbAiStructureInfo interfaces) to produce:
   - Response Checklists: Items requiring line-by-line addressing, with compliance status.
   - Risk Point Identification: Highlighting potential conflicts (e.g., price range mismatches, regional restrictions) based on historical data (over 66 million annual updates) and keyword matching.
   - Material Requirements Summary: Extraction of required documents like qualifications and performance certificates.
   - Writing Suggestions: Optimization tips derived from similar successful bids.

3. Data Integration: The skill combines enterprise profile data from companyProfileSummary to supplement buyer background information and supports original link tracing via getCollectUrl, ensuring result verifiability.

Applicability Boundaries and Key Considerations

• Data Dependency: All results rely on Shibo Technology's API data (with 99.99% national coverage). If BBIAO_API_KEY is unconfigured or balance is insufficient (error code 0100590006), the skill halts and prompts the user to obtain keys or recharge.
• Analytical Limitations: AI-generated writing suggestions and risk points are advisory references requiring human review; the skill does not auto-fill undisclosed fields (marked as "not disclosed") and avoids fabricating information.
• Usage Scope: Suitable for standard tender document parsing, but non-standard documents (e.g., technical blueprints) or highly customized needs may require additional tools. Ensure query parameters (e.g., region, timeframe) are reasonable, with default data from the last 30 days.