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Product Strategy
What you're building, why these features first, and what you're explicitly NOT building.
What Are We Actually Building?
Let's be clear: you could build 100 features. You're building 7 modules. Everything else waits.
Core Principles (These Drive Every Decision)
1. Verification First
Turn away users who don't meet standards. Quality of network > quantity in year one. Verification rigor is your moat.
2. Redacted by Default
All listings start redacted. Users explicitly grant access. Bilateral consent required. Privacy = trust.
3. Structured Data
Every field follows JSON-LD schema. Machine-readable from start. API-first architecture. Built for AI agents.
4. Audit Everything
Immutable logs of all actions. 7-year retention for compliance. User-accessible audit trail. Compliance is a feature.
The MVP: 7 Modules, 6 Months
Here's what you're building. Everything else is noise.
Module 1: Identity & Verification
Purpose: Establish trust foundation for entire platform.
What it does:
- Entity verification — Company registry lookup, LEI verification, domain email check
- Professional verification — LinkedIn linking, employment verification, license checks
- Document vault — Secure upload for pitch decks, track records, encrypted storage
- Trust scoring — 0-100 score based on verification level, response rate, transaction history
Three verification tiers:
| Tier | Requirements | Time | Cost |
|---|---|---|---|
| Basic | Email + LinkedIn + Company registry | Instant | Free |
| Enhanced | Basic + Video ID + Documents + LEI | 24-48 hrs | €250 |
| Premium | Enhanced + Full KYC/AML + Beneficial ownership | 3-5 days | €750 |
💡 Why This Matters
Verification isn't a feature—it's the entire moat. When everyone else races to add users, you're turning people away. That exclusivity creates value. Premium verification at €750 also generates revenue from day one.
Module 2: Investment Thesis Builder
Purpose: Enable precision matching by capturing structured investment criteria.
12 core fields (all structured, machine-readable):
- Deal type — Buyout, growth, secondary, co-investment, etc.
- Sector — 3-level taxonomy (Industry > Vertical > Niche)
- Geography — Country, region, priority ranking
- Ticket size — Min/max range with flexibility indicator
- Structure — Equity, debt, hybrid, preferred
- Stage/maturity — Pre-revenue to mature, profitable vs. burning
- IRR target — Minimum and target returns
- Hold period — Min/target/max years
- Leverage tolerance — None, moderate, aggressive
- ESG requirements — SFDR Article 8/9, exclusions
- Co-investment preference — Lead, follow, either, syndicate
- Decision timeline — Fast (2-4 weeks) to patient (8-12 weeks)
Why structured matters: These aren't free-text fields. They're dropdowns, ranges, multi-selects. Why? Because machines need to match them. AI agents need to read them. Analytics need to aggregate them.
Module 3: Deal Listing & Discovery
Purpose: Enable users to post opportunities with granular confidentiality control.
Two listing types:
Redacted Listing (Recommended)
Visible: Sector, geography, deal type, ticket size range
Hidden: Company name, specific financials, management details
Access: Request required, bilateral consent needed
Open Listing
Visible: Everything—full details
Hidden: Nothing
Access: Immediate for all verified users
20 structured fields per listing: Deal type, stage, size, sector, geography, revenue, EBITDA, structure, ownership %, control position, leverage, growth drivers, competitive advantages, management quality, exit strategy, timeline, key contacts, documents.
Module 4: Matching Engine
Purpose: Algorithmically score compatibility between theses and listings.
10 scoring dimensions (weighted):
| Dimension | Weight | Logic |
|---|---|---|
| Deal Type Match | 15% | Perfect: 100, Partial: 50, None: 0 |
| Sector Alignment | 15% | L1: 40, L2: 70, L3: 100 |
| Geographic Overlap | 12% | HQ match: 100, Ops: 75, Adjacent: 50 |
| Ticket Size Fit | 14% | In range: 100, 50-150%: 70, else: 0 |
| Structure Compatibility | 10% | Exact: 100, Compatible: 70, No: 0 |
| Verification Level | 10% | Premium: +15, Enhanced: +10, Basic: +5 |
Match quality tiers:
- Excellent (85-100): Immediate push notification + email
- Good (70-84): Daily digest email
- Moderate (50-69): Feed only, no notification
- Poor (<50): Hidden unless manual search
📌 Why Scoring Matters
The algorithm isn't just a feature—it's your noise filter. Users trust you because you only show relevant matches. Bad matches = lost trust = churn. The algorithm protects the user experience.
Module 5: Access & Introduction Workflow
Purpose: Facilitate bilateral consent and structured introductions.
The flow:
- User sees redacted listing with high match score
- Clicks "Request Access" → prompted for context message (200-500 chars)
- Request sent to listing owner with requester profile, match score, context
- Owner reviews → Accept or Decline
- If accepted: Full listing revealed, introduction template generated, calendar integration, NDA library
- If declined: Generic message, no details leaked
What gets logged: Every action. Timestamp, parties involved, documents shared, next steps. Immutable audit trail.
Module 6: Search & Filtering
Purpose: Enable users to discover listings beyond algorithmic matches.
Filter options: All 12 thesis dimensions (deal type, sector, geography, ticket size, structure, stage, IRR target, hold period, leverage, ESG, co-invest preference, decision timeline).
Plus: Transaction stage (teaser/LOI/closing), listing age, verification level, response rate.
Saved searches: Name it, set alert frequency (daily/weekly/real-time), edit anytime.
Module 7: Analytics Dashboard
Purpose: Provide users with insights into platform activity and deal flow.
Key metrics:
- Deal flow (new listings matching your thesis over 30/60/90 days)
- Match quality (average score, distribution by tier)
- Response rates (yours as owner, yours as requester)
- Network growth (new connections, active connections)
- Time saved estimate (based on matches delivered vs. manual sourcing)
What You're Explicitly NOT Building (Yet)
⚠️ Scope Discipline
Here's what investors will ask for. Here's why you're saying no (for now):
| Feature | Why Not Now | When |
|---|---|---|
| AI-assisted matching | Need structured data first. Rules-based works for MVP. | Month 12+ |
| Mobile apps | Desktop-first = institutional signal. Mobile nice, not critical. | Month 18+ |
| Transaction execution | Intro platform, not transaction platform. Avoid regulatory complexity. | Never |
| White-label analytics | High margin but distracts from core. Post-MVP feature. | Month 24+ |
| Messaging/chat | Email works. Don't reinvent communication. Keep it simple. | Month 18+ |
The MVP Tradeoff
We COULD build AI matching first (sexy for investors). We COULD build mobile apps (expected by consumers). We COULD build white-label analytics (high margin).
We're NOT doing any of that in Year 1. Why? Because verification beats features. Desktop-first signals institutional. AI comes AFTER data foundation. This isn't about what's possible—it's about what creates defensible value fastest.
Development Timeline: 6 Months, 12 Sprints
| Sprints | Focus | Deliverable |
|---|---|---|
| 1-2 | Foundation | Auth, profiles, verification flow, database schema |
| 3-4 | Core features | Thesis builder, listings, basic matching, access requests |
| 5-6 | Discovery | Search, filters, notifications, introduction templates |
| 7-8 | Polish | UI/UX refinement, security hardening, performance optimization |
| 9-10 | Compliance | Admin dashboard, compliance reporting, audit logs, analytics |
| 11-12 | Launch prep | Load testing, security audit, legal review, beta onboarding |
Milestone gates: No sprint starts until previous sprint's deliverables are complete and tested. No compromises on security or data structure. Quality > speed.
- 7 modules, not 100 features. Verification, thesis builder, listings, matching, access workflow, search, analytics. Everything else waits. Scope discipline is survival.
- Structured data is the foundation. Every field is machine-readable. JSON-LD schema throughout. API-first architecture. This enables AI agents later and creates defensibility now.
- Verification is the moat, not features. Turn away unverified users. Quality > quantity. Premium verification generates revenue from day one. This is what competitors can't copy.
- 6 months to MVP, no shortcuts. 12 sprints, clear milestone gates, quality over speed. Realistic timeline that accounts for security, compliance, and polish.