TeamScope AI

How TeamScope AI Works

TeamScope AI is an automated evaluation system designed to assess the overall strength and credibility of crypto and Web3 project teams. It combines AI-driven analysis, ethical data sourcing, and a quantitative scoring framework to deliver transparent, privacy-respecting evaluations.

🔍 1. Data Input

Users provide key information about a project’s team, such as:

• Member roles (e.g., CTO, developer, advisor)

• Experience levels or focus areas

• Publicly available project materials (e.g., website, GitHub, blog, whitepaper)

Example:

Copy

Project: PlasmaEco  
Team:
- CTO with 10 years AI experience (previous big-tech background)  
- Solidity developer (3 years DeFi)  
- Marketing lead (2 years Web3)

🌐 2. Web Validation

When additional context is needed, TeamScope AI uses live web browsing to gather publicly available project-level information — for example:

• Technical documentation or repositories

• Blog posts and press releases

• Exchange listings or media coverage

No personal data or private information is accessed. All insights are summarized at the project level — never identifying individual contributors.

🧩 3. Privacy & Ethical Safeguards

TeamScope AI follows strict privacy principles:

• ❌ No personal identifiers (names, social handles, or LinkedIn URLs)

• ✅ Only aggregate summaries (“one engineer with prior big-tech experience”)

• 🔒 All evaluations are based on public, professional project data

• ⚖️ Designed to align with GDPR-style data protection standards

This ensures every analysis respects both transparency and individual privacy — two core values in decentralized ecosystems.

📊 4. Scoring Framework

TeamScope AI evaluates each team using a weighted scoring model (0–10 scale per category):

🧮 Weighted Formula

Overall Score=0.30T+0.25E+0.20C+0.15D+0.10A\text{Overall Score} = 0.30T + 0.25E + 0.20C + 0.15D + 0.10AOverall Score=0.30T+0.25E+0.20C+0.15D+0.10A

Scores are automatically calculated using this formula and rounded to one decimal place.

🤖 5. AI Evaluation Process

1. Parse Input → AI reads and structures the provided project/team data.

2. Fact-Check (Optional) → Browsing module verifies public claims (e.g., existence of GitHub activity or prior partnerships).

3. Apply Scoring Logic → Each category is rated 0–10 based on data confidence.

4. Compute Weighted Score → Final numerical rating is calculated.

5. Generate Summary & Recommendations → AI explains key findings and suggests improvements.

🧠 Example Output

📘 6. Transparency and Limitations

TeamScope AI provides a project-level analytical perspective, not an absolute truth. Its insights are designed to:

• Support due diligence

• Encourage transparency

• Highlight development patterns

It does not:

• Verify identities or perform background checks

• Access or display personal data

• Make investment recommendations

🛠️ 7. Technology Stack

• OpenAI GPT Model: Analysis, summarization, scoring logic

• Web Browsing Module: Fact-checking with public data

• Python Code Interpreter: Weighted score calculations

• Custom Prompt Framework: Ethical and privacy filters

🌱 8. Vision

TeamScope AI aims to make crypto project due diligence:

• More accessible (automated evaluation in seconds)

• More transparent (clear scoring formula)

• More ethical (privacy-first and evidence-based)

It’s a step toward responsible AI analysis for decentralized innovation.