How Team Matching Works
Team matching uses AI to analyze RFP requirements and recommend the best team members based on their CVs, certifications, and project experience.
The Matching Process
Step 1: Requirement Analysis
When you upload an RFP, the AI identifies:
- Required roles and positions
- Skill requirements
- Certification requirements
- Experience levels needed
- Industry-specific qualifications
Step 2: Asset Scanning
The AI reviews your asset library:
- Team member CVs
- Certifications and credentials
- Past project experience
- Skills and expertise areas
- Years of experience
Step 3: Match Scoring
Each team member receives a match score (0-100):
| Score Range | Meaning |
|---|---|
| 90-100 | Excellent match - exceeds requirements |
| 75-89 | Strong match - meets all key requirements |
| 60-74 | Good match - meets most requirements |
| 40-59 | Partial match - gaps exist |
| Below 40 | Weak match - significant gaps |
Step 4: Recommendations
The AI recommends:
- Best-fit team members for each role
- Alternative candidates
- Gap areas to address
- Suggested role assignments
Understanding Match Scores
What Increases Scores
- Direct experience - Similar projects
- Certifications - Required credentials
- Skills match - Listed skills matching requirements
- Industry experience - Relevant sector work
- Role experience - Time in similar positions
What Decreases Scores
- Missing certifications - Required but not held
- Experience gaps - Insufficient years
- Industry mismatch - No relevant sector experience
- Skill gaps - Missing key competencies
Using Team Recommendations
Viewing Recommendations
- Open your proposal
- Go to Team tab
- Review AI-suggested team
- See match scores and reasoning
Accepting Suggestions
- Review the recommended team
- Check match explanations
- Click Accept for appropriate matches
- The CV is linked to the proposal
Overriding Suggestions
You can override AI recommendations:
- Click Change next to a role
- Search for alternative team members
- View their match scores
- Select your preferred choice
- Optionally add override reason
Improving Match Quality
Better CVs
Upload comprehensive CVs with:
- Detailed project descriptions
- Complete skill listings
- All certifications
- Industry experience highlighted
- Quantifiable achievements
CV Maintenance
Keep CVs current:
- Update quarterly
- Add new projects promptly
- Refresh certifications
- Remove outdated information
Tagging and Categorization
Improve matching by:
- Using consistent skill tags
- Assigning correct business lines
- Adding relevant keywords
- Categorizing by expertise area
Team Analytics
Match Trends
Track over time:
- Average match scores
- Most frequently matched team members
- Common skill gaps
- Certification utilization
Performance Correlation
Analyze relationships between:
- Team match scores and win rates
- Specific team members and outcomes
- Role fill rates and proposal success
Gap Analysis
Identify organizational gaps:
- Frequently missing certifications
- Skill shortages
- Experience gaps
- Capacity constraints
Best Practices
Trust but Verify
- Review AI recommendations critically
- Consider factors AI might miss
- Account for team availability
- Balance workload across team
Strategic Teaming
- Build strong core teams
- Develop specialists for key areas
- Plan for certification renewals
- Cross-train where possible
Continuous Improvement
- Update CVs after each project
- Track certification expirations
- Invest in skill development
- Monitor match score trends
Troubleshooting
Low match scores across the board?
- Review CV completeness
- Check if requirements are unusual
- Consider teaming partners
- Update skill tags
Wrong person being recommended?
- Check CV accuracy
- Verify business line assignment
- Update role history
- Review skill tags
AI missing obvious matches?
- Ensure CV is uploaded and processed
- Check for spelling variations
- Add alternative skill names
- Verify certification names match