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Cernio — Business Model Canvas
Version: 2.0
Date: 2026-04-03
Author: Aleks Özkuyumcu (Founder) + Claude Opus 4.6
Source: Founder Handbook (Ch. 1-10, 68-84, 85-100, 101-115), Strategy docs, pricing-analysis.md
Supersedes: _archive_v074/01-business-model-canvas.md (v1.0, sector-specific Turkish textile assumptions — DEPRECATED)
Executive Summary
Cernio is an AI-powered B2B buyer discovery and intelligence platform. It replaces 3-4 hours of manual buyer research with a 30-second AI-powered discovery — for any product, any market, worldwide.
Core promise: “Enter your product + target country → get AI-ranked companies most likely to buy from you.”
The platform is global from day one in capability, sector-agnostic by design, and targets B2B exporters and international sales teams as the initial wedge — a massive, underserved market with $34.65T in annual global merchandise trade and 1.2M+ exporting companies in the US, EU, and Turkey alone.
1. Value Proposition
For B2B Exporters & International Sales Teams
| Problem | Cernio Solution |
|---|
| Finding buyers in foreign markets takes 3-4 hours per product-country combination | AI-ranked buyer list in under 30 seconds |
| Trade directories return 200+ unranked results with no fit assessment | FitScore-ranked results with company type classification (distributor/reseller/end-user/manufacturer) |
| Enterprise tools (ZoomInfo 15K+/yr,Cognism15K+/yr) are too expensive for SMEs | Accessible pricing starting at $49/month |
| No existing tool combines discovery + contact intelligence + lead workflow | Single platform: discover → identify decision makers → track → close |
| Fragmented workflows across Google, Excel, LinkedIn, email, trade fair notes | One system replaces 5+ disconnected tools |
Value Layers (Product Evolution)
| Layer | What It Solves | Status |
|---|
| AI Buyer Discovery | ”Where are my buyers?” — AI-ranked companies for any product + country | ✅ Built (v0.74) |
| Contact Intelligence | ”Who do I talk to?” — Decision maker identification (procurement, import managers) | ✅ Built (v0.74) |
| Lead Workspace | ”How do I track this?” — Light CRM replacing spreadsheets | ✅ Built (v0.74) |
| Trade Fair Capture | ”I met 50 people, now what?” — Business card scan → structured lead | 🔮 Future (R3) |
| Export Intelligence | ”Which market should I enter?” — Market selection, tariff data, competitive landscape | 🔮 Future (R3) |
Why Now? (Structural Shifts)
- Public business data is widely available — Company info exists across websites, directories, LinkedIn, government databases — but it’s fragmented and unstructured.
- AI can structure messy web data — LLMs can interpret descriptions, classify industries, infer company types, extract contacts, and rank relevance. This was impossible 2 years ago.
- B2B sales teams still use fragmented workflows — Most SMEs use Excel + Google + email. Enterprise tools cost $15K+/year and aren’t designed for cross-market buyer discovery.
- Global trade is massive — $34.65T in merchandise trade (2025, WTO). Over 1.2M exporting companies in the US (270K), EU (750K+), and Turkey (180K). No AI-native buyer discovery platform exists.
2. Customer Segments
Primary Segment: B2B Exporters & International Sales Teams
| Attribute | Value |
|---|
| Company size | 5-200 employees |
| Business model | B2B (selling to other businesses) |
| Sales model | Outbound — actively seeking buyers in new markets |
| Internal research capacity | Low to medium (no dedicated RevOps team) |
| Current tools | Excel, Google, email, trade fairs, LinkedIn |
| Geography | Global — initial GTM focus on Turkey, then EU and US |
| Annual export revenue | 500K−50M |
Ideal Customer Profile (ICP)
| Persona | Role | Pain Point | Activation Trigger |
|---|
| Founder / Owner | Strategic market expansion, trade fair participation | Limited time, fragmented research | ”I spend days finding buyers in new markets” |
| Export / Sales Manager | Finding potential buyers, sending catalogs, follow-ups | Repetitive manual research, scattered info | ”I Google the same things every week” |
| Business Development | Lead qualification, pipeline, partner relationships | Poor lead memory, unclear prioritization | ”I lose track of contacts after fairs” |
Anti-Persona (Not Target)
- Enterprise companies with existing ZoomInfo/Salesforce stack
- Domestic-only businesses with no cross-market needs
- B2C / direct-to-consumer companies
- Companies with dedicated 10+ person sales research teams
Initial Vertical Markets
| Industry | Why First |
|---|
| Specialty chemicals | Global trade, distributor-dependent, founder domain expertise |
| Industrial machinery | High deal value, long sales cycles, discovery bottleneck |
| Textile auxiliaries | Dense export ecosystem, strong Turkey presence |
| Packaging materials | Universal demand, export-heavy |
| Auto parts & accessories | Massive global trade, fragmented buyer base |
Expansion Segments (Future)
| Segment | Use Case | Timeline |
|---|
| Domestic B2B sales teams | Finding buyers within their own country | Phase 2 |
| Procurement teams | Finding suppliers and partners | Phase 2 |
| Importers & distributors | Finding suppliers in new regions | Phase 2 |
| Trade agencies | Matchmaking exporters and importers | Phase 3 |
| Industry associations | Member service — buyer discovery as a benefit | Phase 3 |
3. Channels
Acquisition Channels (Prioritized)
| Channel | Phase | Role | Expected % of Users |
|---|
| PLG (self-serve signup) | 1-3 | Users find, try, and buy without sales call | 30-40% at scale |
| Trade fair demos | 1-2 | Live demo → wow moment → free trial | 15-20% |
| LinkedIn content + outreach | 1-3 | Founder + brand account, weekly content | 20-25% |
| Referrals + word of mouth | 2-3 | Organic from satisfied users | 10-15% |
| SEO / blog content | 2-3 | Long-tail export-related queries | 5-10% |
| Export association partnerships | 2-3 | TIM, DEIK, CEFIC, VDMA co-marketing | 5-10% |
Geographic Entry Sequence
| Phase | Market | Trigger | Expected Users |
|---|
| 1 | Turkey | Founder network, first 100 customers | 10-100 |
| 2 | MENA + Central Asia | Trade corridor overlap, low CAC | 100-500 |
| 3 | EU (Germany, Italy, Spain) | Largest extra-EU exporter base, PLG traction | 500-2K |
| 4 | US + UK | English-language content flywheel, highest ARPU | 2K-10K |
| 5 | Southeast Asia + LatAm | Emerging export economies, viral growth | 10K+ |
Distribution Model
Free tier → PLG acquisition (self-serve, no sales call)
Pro tier → PLG conversion (upgrade from free) + direct sales (trade fairs)
Team tier → Inside sales (founder-led initially)
Enterprise → Custom sales process (future)
4. Customer Relationships
Relationship Types by Segment
| Segment | Relationship | Mechanism |
|---|
| Free users | Self-serve + automated | In-app guidance, email onboarding sequence |
| Pro users | Self-serve + community | Email support, community forum, knowledge base |
| Team users | High-touch | Onboarding call, dedicated Slack channel |
| Enterprise | White-glove | Account manager, custom onboarding, SLA |
| Pilot users (beta) | Co-builder | Direct WhatsApp/Slack, weekly feedback, feature priority |
Retention Mechanisms
| Feature | Retention Mechanism |
|---|
| Saved leads pipeline | User builds a living prospect list — leaving means losing data |
| Follow-up reminders | ”Contact this distributor by Friday” — keeps users coming back |
| Discovery alerts (future) | “3 new buyers matching your profile found this week” |
| Data compounds over time | Contact enrichment improves with usage — more data = more value |
| CRM integration (future) | Embedded in daily workflow — maximum switching cost |
Key Metrics
| Metric | Target |
|---|
| Activation (first search + save in session 1) | > 60% |
| Week 1 retention | > 60% |
| Month 1 retention | > 40% |
| Free → paid conversion (30 days) | > 5% |
| NPS | > 40 |
5. Revenue Streams
Primary: Hybrid SaaS + Credit Model
Note: Pricing is under re-evaluation. Figures below are from BILL-19 analysis (March 2026) and will be finalized after BIZ-2 (Revenue & Cost Structure) and BIZ-7 (Production Simulation) complete pipeline v2 cost modeling.
| Plan | Price (Monthly) | Price (Annual, -20%) | Target |
|---|
| Free | $0 | — | WOW moment + activation (discovery open, contact reveal gated) |
| Pro | $49/mo | 39/mo(468/yr) | Individual exporters |
| Team | $149/mo | 119/mo(1,428/yr) | Export teams (2-10 members) |
| Enterprise | Custom | Custom | Large organizations |
Secondary: Credit Packs (Top-Up)
Users who exceed plan limits can purchase additional credits.
| Pack | Credits | Price | $/Credit |
|---|
| Small | 50 | ~$12 | $0.24 |
| Medium | 200 | ~$45 | $0.225 |
| Large | 1,000 | ~$200 | $0.20 |
Future Revenue Streams
| Stream | Description | Timeline |
|---|
| Team seat expansion | Additional team members at per-seat pricing | R1 (billing launch) |
| Intelligence modules | Market discovery, tariff data, competitive landscape | R3 |
| API access | Enterprise programmatic access | R3+ |
| Trade fair premium features | Lead capture, enrichment, booth analytics | R3 |
| Data partnerships | Anonymized market signals, industry reports | R4+ |
Revenue Model Summary
Year 1: Free + Pro subscriptions + credit packs (PLG-driven)
Year 2: + Team plans + seat expansion + early enterprise
Year 3: + Intelligence modules + API + partnerships
6. Key Resources
Technology
| Resource | Description |
|---|
| AI Discovery Pipeline | Multi-stage pipeline: product analysis → query generation → web search → normalization → classification → scoring → ranking |
| FitScore Algorithm | AI-driven buyer fit scoring: industry match + distributor probability + additional signals |
| Provider-Agnostic AI Layer | Supports Gemini, OpenAI, Claude, Perplexity — switch based on cost/quality/capability |
| Multi-Tenant SaaS Platform | Next.js 16 + Supabase (PostgreSQL) + Tailwind CSS |
Data
| Asset | Description |
|---|
| Buyer graph | Discovered companies + classifications + relationships (compounds with usage) |
| Contact graph | Decision makers + roles + contact details |
| Search history | Query patterns + result feedback → improves future discovery quality |
| Segment intelligence | Industry-specific classification rules and scoring models |
Human
| Resource | Role |
|---|
| Founder (Aleks) | Product, engineering, sales, GTM — solo builder |
| AI coding assistants | Development acceleration (Claude Code, GitHub Copilot) |
| Domain expertise | 10+ years B2B export industry experience |
Infrastructure
| Component | Technology |
|---|
| Application hosting | Hetzner VPS (to be determined — BIZ-7 will model) |
| Database | Supabase (managed PostgreSQL) |
| Payments | Lemon Squeezy (Merchant of Record) |
| AI providers | Gemini (default), Perplexity (web search), OpenAI, Claude |
| Deployment | Docker Compose on VPS (Coolify) |
7. Key Activities
Product & Engineering
| Activity | Frequency |
|---|
| AI pipeline improvement (accuracy, speed, coverage) | Continuous |
| Feature development (Ring 1 → Ring 4 roadmap) | Sprint-based |
| Infrastructure scaling (user growth) | Milestone-triggered |
| Provider cost optimization (model routing) | Monthly review |
| Security hardening (auth, RLS, data isolation) | Per-ring |
Sales & Marketing
| Activity | Frequency |
|---|
| LinkedIn content creation | 3-4x/week |
| Trade fair attendance + live demos | 2-5 fairs/year |
| PLG funnel optimization (signup → activation → conversion) | Continuous |
| User feedback collection and analysis | Weekly |
| Community building (export managers network) | Ongoing |
Operations
| Activity | Frequency |
|---|
| AI cost monitoring and optimization | Weekly |
| Usage analytics and churn analysis | Weekly |
| Customer support (email, community) | Daily |
| Billing and subscription management | Automated (Lemon Squeezy) |
8. Key Partners
| Partner Type | Examples | Value Exchange |
|---|
| AI providers | Google (Gemini), OpenAI, Anthropic, Perplexity | API access → compute costs |
| Payment processor | Lemon Squeezy (MoR) | Payment processing → fee (5% + $0.50) |
| Infrastructure | Hetzner (VPS), Supabase (DB) | Hosting → monthly fee |
| Export associations | TIM (Turkey), CEFIC (chemicals), VDMA (machinery) | Access to member base → tool value for members |
| Trade promotion agencies | JETRO, KOTRA, UKTI, IGEME | Government-backed distribution → credibility |
| Trade fair organizers | Messe Frankfurt, Reed Exhibitions | Demo access → leads |
| Integration partners (future) | HubSpot, Salesforce, Pipedrive | CRM sync → retention + reach |
9. Cost Structure
Note: Detailed cost modeling in BIZ-2 (Revenue & Cost) and BIZ-7 (Production Simulation). Figures below are directional.
Fixed Costs (Monthly)
| Category | Estimated Cost | Notes |
|---|
| Infrastructure (VPS + DB) | ~€30-50/mo | Hetzner VPS + Supabase free tier (initially) |
| Domain + SSL | ~$2/mo | Amortized annual cost |
| Email service | ~$0/mo | Free tier initially |
| Founder living cost | Not included | Bootstrap — no salary drawn |
Variable Costs (Per-User)
| Cost Component | Per Discovery | Per Headhunt | Per Enrichment |
|---|
| LLM token cost | $0.001-0.03 | $0.002-0.02 | $0.001-0.03 |
| Web search API | $0.005-0.035 | $0.005-0.035 | Optional |
| Total (current pipeline) | $0.009-0.06 | $0.007-0.04 | $0.001-0.03 |
Pipeline v2 impact: Current 3-stage pipeline will expand to 10 stages (handbook spec). This will increase per-discovery cost — estimated 2-4x. BIZ-7 will model this in detail.
Cost Structure Characteristics
- ~90%+ gross margin at current pipeline complexity with balanced model selection
- Variable costs dominate — scales linearly with usage
- Fixed infrastructure costs are minimal (< $100/mo at launch)
- AI provider costs are the primary COGS — model routing optimization is critical
- No employee costs (solo founder) — this is the key bootstrap advantage
10. Competitive Positioning
Note: Detailed competitive analysis in BIZ-4 (SWOT) and BIZ-9 (Competitive Positioning). Summary here.
Competitive Landscape
Cernio sits at the intersection of six categories. No single competitor covers the full discovery-to-engagement chain for industrial B2B:
| Category | Examples | Cernio Advantage |
|---|
| Trade directories | Kompass, Europages, ThomasNet | AI-ranked results vs. 200+ unranked listings |
| Sales intelligence | Apollo, ZoomInfo, Cognism | SME-affordable (49vs15K+), export-native, company type classification |
| CRM platforms | HubSpot, Salesforce | Discovery-first (CRM assumes leads exist) |
| Import/export data | ImportGenius, Panjiva | AI interpretation vs. raw customs data |
| AI lead generation | Clay, Persana, Instantly | Buyer-focused (not outreach-focused), industry-agnostic discovery |
| Industry intelligence | ChemAnalyst, ICIS | Horizontal platform vs. single vertical |
Unique Differentiators
- Company type intelligence — AI classifies companies as distributor/reseller/end-user/manufacturer. No competitor does this at query time.
- Product-to-buyer matching — “Enter your product → find who buys it.” Not “search a database of contacts.”
- Export-native workflow — Discovery → contact → lead → follow-up in one system. Built for how exporters actually work.
- Global + affordable — Works for any product, any country. Priced for SMEs (49/mo),notenterprises(15K+/yr).
- Compounding data moat — Every search enriches the buyer graph. Quality improves with usage. Network effects emerge.
11. Unfair Advantages
| Advantage | Why It’s Defensible |
|---|
| Compounding buyer graph | Every discovery search enriches the global database — more searches = better results for everyone. Competitors with static databases cannot replicate this. |
| Domain expertise | Founder’s 10+ years in B2B export industry. Understands the workflow, pain points, and decision criteria that generic SaaS builders miss. |
| AI-native architecture | Built from scratch for AI discovery, not bolted onto an existing CRM or directory. Architecture supports rapid provider switching and cost optimization. |
| Blue ocean positioning | No direct competitor in “AI-powered B2B buyer discovery for exporters.” Adjacent categories solve fragments. Category creation opportunity. |
| Bootstrap economics | Solo founder + AI coding assistants + cloud infrastructure = near-zero burn rate. Can iterate for years without external funding. |
Canvas Summary (One Page)
┌─────────────────────────────────────────────────────────────────────────┐
│ CERNIO — BUSINESS MODEL CANVAS │
├──────────────┬──────────────────┬───────────────┬───────────────────────┤
│ KEY PARTNERS │ KEY ACTIVITIES │ VALUE PROP │ CUSTOMER │
│ │ │ │ RELATIONSHIPS │
│ • AI provid. │ • Pipeline dev │ Product + │ │
│ (Gemini, │ • Feature dev │ Country → │ • Self-serve (Free) │
│ OpenAI, │ • PLG optimize │ AI-ranked │ • Community (Pro) │
│ Perplexity)│ • Trade fairs │ buyers in │ • High-touch (Team) │
│ • Lemon │ • LinkedIn mktg │ 30 seconds │ • White-glove (Ent.) │
│ Squeezy │ • Cost optimize │ │ │
│ • Hetzner │ │ SME-affordable│ CUSTOMER SEGMENTS │
│ • Supabase ├──────────────────┤ ($49 vs $15K) │ │
│ • Export │ KEY RESOURCES │ │ • B2B exporters │
│ assoc. │ │ Company type │ (5-200 employees) │
│ • Trade fair │ • AI pipeline │ intelligence │ • Export/sales mgrs │
│ organizers │ • Buyer graph │ (dist/resell/ │ • Founders/CEOs │
│ │ • FitScore algo │ end-user/mfr)│ • BDMs │
│ │ • Domain expert. │ │ │
│ │ • Next.js + Supa │ Discovery → │ Verticals: chemicals, │
│ │ │ Contact → │ machinery, textiles, │
│ │ │ Lead → Close │ packaging, auto parts │
├──────────────┴──────────────────┼───────────────┴───────────────────────┤
│ COST STRUCTURE │ REVENUE STREAMS │
│ │ │
│ • AI API costs (~$0.01-0.06/q) │ • SaaS subscriptions (Free/Pro/Team) │
│ • Infrastructure (~€30-50/mo) │ • Credit packs (top-up) │
│ • Lemon Squeezy fees (5%) │ • Seat expansion (Team) │
│ • Trade fair travel │ • Intelligence modules (future) │
│ • No salary (bootstrap) │ • API access (future) │
│ │ • Data partnerships (future) │
│ Gross margin: ~90%+ │ │
└─────────────────────────────────┴───────────────────────────────────────┘
Document Dependencies
| For Deeper Analysis… | See Document |
|---|
| Detailed pricing, costs, AI model comparison | BIZ-2: Revenue & Cost Structure |
| Financial projections (Y1/Y2/Y3) | BIZ-3: Financial Projections |
| SWOT analysis with web research | BIZ-4: SWOT Analysis |
| TAM/SAM/SOM market sizing | BIZ-5: ROI & Market Sizing |
| Investor analysis | BIZ-6: Investor Analysis |
| Infrastructure cost modeling | BIZ-7: Production Simulation |
| Go-to-market strategy | BIZ-8: GTM Strategy |
| Competitive differentiation matrix | BIZ-9: Competitive Positioning |
| Document | Location |
|---|
| Founder Handbook (source of truth) | docs/handbook/INDEX.md |
| Strategy Documents (Ring 1-4) | docs/strategy/INDEX.md |
| Pricing Analysis (BILL-19) | docs/pricing-analysis.md |
| Billing Strategy | docs/strategy/04-billing-credits.md |
| Archived v1.0 BMC | docs/business/_archive_v074/01-business-model-canvas.md |
