Tender Document Smart Interpreter System

Specific Problems It Solves

In bidding workflows, teams often grapple with fragmented information, complex interpretation, and time-sensitive decisions:
- Inefficient information gathering: Tender announcements are scattered across government portals, corporate platforms, and other sources, making manual consolidation slow and prone to omissions.
- High interpretation barrier: Tender documents frequently span dozens of pages with technical specifications, qualification clauses, and scoring details, challenging non-experts to extract key points quickly.
- Delayed opportunity response: Short bidding windows necessitate rapid analysis of procurement unit preferences, competitor winning dynamics, or market trends—delays risk missing chances.

How the Skill Works

Built on the Shibohua Technology cross-industry bidding data API, the skill provides intelligent interpretation through core capabilities:
1. Natural language query translation: Users can describe needs in colloquial terms (e.g., "Find education procurement intents in Shanghai over the past year"). The skill invokes the SearchProjectForAI interface to automatically decompose vague requests into structured query conditions (region, industry, time, etc.).
2. Multi-dimensional data retrieval:
- Uses getZTBProjectDetail to fetch announcement body text, extracting unstructured data like project background and technical requirements.
- Employs getZTBStructreDetail to pull structured fields such as procurement units, winning amounts, and contact information.
- Supports getCollectUrl to trace original source links for verification.
3. Intelligent analysis and output:
- First returns a concise summary (hit count, time range), then lists key projects in a table (project name, purchaser, amount, etc.).
- For analysis queries (e.g., procurement preferences), adds trend insights, supplier opportunities, and competitor moves.
- All outputs strictly adhere to raw data, preserving fields like amounts and timelines without fabrication.

Applicable Boundaries and Considerations

• Data source dependency: All queries rely on the Shibohua Technology API; ensure the BBIAO_API_KEY environment variable is configured correctly with sufficient credits. If error code 0100590006 is returned, manual top-up is required before retry.
• Interface scope: Only interfaces listed in the documentation (e.g., SearchProjectForAI, getZTBStructreDetail) are allowed—no additional industry-specific interfaces are introduced.
• Interpretation accuracy: Based on existing data parsing, it may be affected by update frequency; absolute accuracy or rule circumvention is not promised.
• Security norms: API keys must be securely stored via environment variables and never exposed in logs, errors, or request URLs; user-provided keys are reused long-term without repeated prompts.