dvcrn Bid Document Review (Lite)
Review bid/tender documents for errors, contradictions, compliance issues, and fraud indicators using AI analysis.
What it does:
- Extracts text + tables from .docx and .doc bid documents
- Cross-references procurement requirements vs bid responses
- Checks pricing consistency, qualification claims, technical parameters
- Identifies contradictions, missing information, expired certificates
- Generates a structured audit report with 🔴/🟡/🟢 risk ratings
Ideal for:
- Bid managers reviewing submissions before deadline
- Procurement officers auditing received bids
- Companies reviewing competitor bids (post-award disclosure)
- Quality assurance on your own tender responses
Quick Start
Place your bid documents in a working directory:
- Procurement/tender document (the requirements)
- Bid/response document (what was submitted)
Tell your agent:
Review the bid documents in [directory]. The procurement document is [file1] and the bid response is [file2].The agent will:
- Extract all text and tables
- Analyze against the checklist below
- Generate a structured report
Text Extraction
For .docx files:
python3 {baseDir}/scripts/extract_text.py input.docx output.txt
For .doc (legacy) files:
python3 {baseDir}/scripts/extract_doc_text.py input.doc output.txt
Review Checklist
1. Pricing & Commercial
- Total price within maximum limit
- Unit prices within per-item limits
- Tax rate calculations correct
- Amount in words matches figures
- No abnormally low pricing (dumping risk)
- Payment terms match requirements
2. Mandatory Requirements (★ items)
- ALL mandatory/starred parameters responded to
- Responses meet or exceed minimums
- Supporting evidence provided for each claim
- No contradictions between different sections
3. Qualification & Eligibility
- Business license valid and matching
- Required certifications in-date
- Performance track record meets minimum
- Credit/reputation checks provided
- Authorization letters (if agent/distributor)
4. Technical Response
- All technical parameters addressed
- Claims supported by test reports/certificates
- Product model matches throughout document
- Standards referenced are current (not withdrawn)
- Delivery timeline realistic vs. claimed
5. Document Integrity
- Bidder name consistent throughout
- Signatures and seals present where required
- Dates logical (no future dates, no pre-bid dates)
- Page numbering sequential
- No template placeholders left unfilled (e.g. [X], [TBD])
6. Common Red Flags
- Identical test results matching nominal values exactly (fabrication indicator)
- Contracts with missing signatures or blank fields
- Expired certificates/qualifications submitted as valid
- Third-party materials without clear authorization
- Inconsistent company names across documents
Report Format
Generate the report in Markdown with this structure:
# Bid Review Report
## Project Info
- Procurement: [name]
- Bidder: [name]
- Date: [date]
## Summary
| Category | Status | Notes |
|----------|--------|-------|
| Pricing | ✅/⚠️/❌ | ... |
| Mandatory params | ✅/⚠️/❌ | ... |
| ... | ... | ... |
## 🔴 Critical Issues (may cause disqualification)
### 1. [Issue title]
- Location: [where in document]
- Detail: [what's wrong]
- Impact: [consequence]
- Recommendation: [fix]
## 🟡 Medium Issues (affects scoring)
...
## ✅ Positive Findings
...
## Checklist Summary
[Completed checklist with pass/fail for each item]
Dependencies
- Python 3.6+
- python-docx (
pip3 install python-docx) - olefile (
pip3 install olefile) — for .doc files only
Limitations (Lite Version)
This lite version covers text-based review only. It does not include:
- Image extraction and visual analysis (certificates, contracts, photos)
- Automated image fraud detection (watermarks, stock photos, expired seals)
- PDF report generation
- Image compression/optimization for API cost savings
For full image review capabilities, see the complete bid-review skill.
Built From Real Experience
This skill was developed from reviewing 3 real bid documents (1,800+ pages, 2,600+ images) in the road maintenance equipment industry. Every checklist item comes from an actual issue found in production reviews.
Issues discovered include: fake test data, stolen web images with visible watermarks, technical parameters contradicting government records by 4 tons, expired certificates submitted as valid, and 30+ pages of copied filler content.