Technical Whitepaper
Comprehensive documentation for grant evaluators, investors, and technical assessors
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Complete technical and business documentation (PDF, 2.5 MB, 48 pages)
📄 Download PDFLast updated: January 2026 • Version 2.1
Executive Summary
PartnerBook is an AI-powered platform that solves the €250B problem of missed business partnerships in Europe. Using graph neural networks and natural language processing, we automatically identify and facilitate optimal B2B partnerships across 25+ countries.
Current Status: TRL 6-7 with 1,500+ registered companies, 12,000+ successful connections, and proven unit economics (12.9:1 LTV:CAC). Seeking €2M EIC Accelerator funding to scale to 10,000 companies by 2027.
Whitepaper Contents
1. Executive Summary
- Problem statement
- Solution overview
- Market opportunity
- Financial highlights
- Funding requirements
2. Technology Architecture
- System architecture diagrams
- Technology stack breakdown
- Database schema design
- API specifications
- Scalability considerations
- Security infrastructure
3. AI & Machine Learning
- Graph Neural Network implementation
- NLP algorithms for profile matching
- Collaborative filtering system
- Trust scoring algorithm
- Model training methodology
- Performance benchmarks (87% accuracy)
4. Security & Privacy
- Swiss data hosting details
- GDPR compliance measures
- End-to-end encryption
- Authentication & authorization
- Audit logs and monitoring
- Third-party security audits
5. Market Analysis
- TAM/SAM/SOM calculations
- Competitive landscape
- Target customer segments
- Market trends & drivers
- Go-to-market strategy
- Partnership opportunities
6. Financial Projections
- 5-year revenue forecast
- Unit economics breakdown
- CAC & LTV analysis
- Burn rate & runway
- Path to profitability
- Funding use breakdown
7. Team & Advisors
- Founder backgrounds
- Key team members
- Advisory board
- Organizational structure
- Hiring roadmap
- Equity allocation
8. Roadmap & Milestones
- Product development roadmap
- Geographic expansion plan
- Key milestones (2026-2028)
- Risk mitigation strategies
- Success metrics & KPIs
- Exit strategy considerations
Appendices
- A. Customer testimonials
- B. Case studies
- C. Technical specifications
- D. Regulatory compliance
- E. References & citations
- F. Glossary of terms
Key Technical Highlights
🤖 AI Innovation
Novel application of Graph Neural Networks (GNN) to business relationship prediction:
- Node Embeddings: 256-dimensional representations of companies
- Edge Prediction: 87% accuracy in partnership success prediction
- Training Data: 12,000+ historical connections, 50,000+ company profiles
- Real-time Inference: <50ms latency for recommendation generation
🔬 Scientific Validation
- Peer-reviewed publication pending in ACM KDD 2026
- A/B testing shows 3.4X higher engagement vs. baseline
- Partnership success rate: 68% vs. 23% industry average
- Customer satisfaction (NPS): 67 (considered excellent)
⚡ Technical Performance
- Uptime: 99.93% (2025 average)
- Response Time: p95 < 200ms for all API endpoints
- Scalability: Current architecture supports 100K+ concurrent users
- Data Processing: 1M+ profile updates processed daily
📋 For Grant Evaluators
Why This Whitepaper Matters:
This document provides complete technical transparency required for grant evaluation:
✓ TRL Verification
Detailed evidence of TRL 6-7 achievement including deployment logs, customer data, and system metrics.
✓ Innovation Assessment
Novel AI algorithms with performance benchmarks and comparison to state-of-the-art alternatives.
✓ Commercial Viability
Proven business model with real customer traction, financial projections, and unit economics.
✓ Scalability Proof
Technical architecture designed for 100X scale with detailed capacity planning and cost analysis.
✓ Impact Potential
Quantified European market impact with job creation estimates and GDP contribution projections.
✓ Risk Assessment
Comprehensive risk analysis covering technical, market, regulatory, and competitive risks with mitigation strategies.
Academic & Industry References
The whitepaper includes citations from:
- Journal of Machine Learning Research (JMLR)
- ACM Conference on Knowledge Discovery and Data Mining (KDD)
- European Commission SME Performance Review
- Gartner B2B Technology Research
- McKinsey Global Institute Reports
- World Economic Forum Digital Transformation Studies
Supplementary Materials
In addition to the main whitepaper, the following materials are available:
📊 Financial Model
Interactive Excel model with 5-year projections, sensitivity analysis, and scenario planning.
Request Access📈 Pitch Deck
Investor presentation with market opportunity, traction, and funding requirements.
Request Deck🔬 Technical Deep-Dive
Extended technical documentation for CTO/technical due diligence (80+ pages).
Request Access🔐 Confidential Data Room
For serious evaluators and investors, we provide access to a secure data room containing:
- Anonymized customer contracts and testimonials
- Detailed financial statements (P&L, balance sheet, cash flow)
- Source code repository access (read-only)
- Security audit reports and penetration test results
- Legal documents (incorporation, IP assignments, agreements)
- Team background checks and reference letters
Request data room access: Contact us with your NDA and evaluation timeline.
Frequently Asked Questions (Evaluators)
Q: How is this different from LinkedIn?
A: LinkedIn is a general professional network optimized for recruiting and content. PartnerBook is specifically built for B2B partnerships with AI that understands business complementarity, not just job titles. We achieve 68% partnership success rate vs. LinkedIn's ~5% for cold outreach.
Q: What is your defensibility / moat?
A: Three layers: (1) Network effects - value grows exponentially with users, (2) Data moat - 12,000+ labeled partnerships for training superior AI models, (3) Switching costs - integrated into customer's partnership workflows.
Q: How do you ensure data privacy?
A: Swiss-hosted servers subject to Swiss Federal Data Protection Act (stronger than GDPR). End-to-end encryption, zero-knowledge architecture for sensitive data, SOC 2 Type II audit in progress. No data sharing without explicit user consent.
Q: What happens if AI recommendations are wrong?
A: Users always have final control. AI provides ranked suggestions, users choose whom to contact. Continuous learning from feedback (thumbs up/down) improves recommendations over time. 87% accuracy means 13% false positives, which users can ignore.
Q: Can you achieve profitability?
A: Yes. Current unit economics show 12.9:1 LTV:CAC with 2.8-month payback. At 5,000 paying customers (achievable by Q2 2027), we reach operational break-even. Path to profitability detailed in Section 6 of whitepaper.
Q: What are the key risks?
A: (1) Market risk - SMEs may be slow to adopt new technology (mitigated by proven traction), (2) Competition from big tech (mitigated by privacy focus and niche specialization), (3) Regulatory changes (mitigated by proactive compliance). Full risk analysis in whitepaper Section 8.
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