Skip to main content

​
Cernio — Business Model Canvas

Version: 1.0 Date: 2026-03-25 Source: Founder Handbook (Ch. 1-10, 68-84, 85-100, 101-115) Purpose: Structured business model reference for investor presentations and PM knowledge base

​
1. Value Proposition

​
Core Promise

“Find the 5 companies most likely to buy your product — in under 15 minutes.” Cernio replaces 3-4 hours of manual research (Google, directories, LinkedIn, spreadsheets) with an AI-powered discovery engine that returns ranked, scored buyer candidates for any product-country combination.

​
Value Layers

LayerWhat It SolvesUser Benefit
Discovery”Where are my buyers?”AI-ranked company list for any product + country
Contact Intelligence”Who do I talk to?”Decision maker identification (procurement, import managers)
Lead Workflow”How do I track this?”Light CRM replacing spreadsheets — save, note, follow-up
Trade Fair Capture”I met 50 people, now what?”Business card scan → structured lead (future)
Export Intelligence”Which market should I enter?”Market selection, tariff data, competitive landscape (future)

​
Why Now?

Three structural shifts make this possible today:
  1. Public business data is widely available — Company info exists across websites, directories, LinkedIn, government databases — but it’s fragmented and unstructured.
  2. AI can structure messy web data — LLMs can interpret descriptions, classify industries, infer distributor vs manufacturer roles, extract contacts, and rank relevance.
  3. B2B sales teams still use fragmented workflows — Most SMEs use Excel + Google + email for buyer research. Enterprise tools (Salesforce, ZoomInfo) cost $14,995+/year — too expensive for SMEs and not designed for cross-market buyer discovery.
  4. Global trade is massive — $34.65 trillion in merchandise trade (2025), 1.2M+ exporting companies in US+EU+Turkey alone, yet no AI-native buyer discovery platform exists.

​
2. Customer Segments

​
Primary Segment: B2B Exporters and International Sales Teams

AttributeValue
Company size5-200 employees
Business modelB2B (selling to other businesses)
Sales modelOutbound — actively seeking buyers in new markets
Internal research capacityLow to medium (no dedicated RevOps)
Current toolsExcel, Google, email, trade fairs, LinkedIn
GeographyGlobal — initial focus on Turkey, EU, US exporters

​
Ideal Customer Profile (ICP)

PersonaRolePain PointActivation Trigger
Founder / OwnerStrategic market expansion, trade fair participationLimited time, fragmented research”I spend days finding buyers in new markets”
Export / Sales ManagerFinding potential buyers, sending catalogs, follow-upsRepetitive manual research, scattered info”I Google the same things every week”
Business DevelopmentLead qualification, pipeline, partner relationshipsPoor lead memory, unclear prioritization”I lose track of contacts after fairs”

​
Initial Vertical Markets

Industries with the strongest buyer discovery pain (launch focus):
IndustryWhy It Fits
Industrial chemicalsComplex supply chains, many potential buyers globally
Textile auxiliariesGlobal trade, distributor networks, high fair culture
Machinery & equipmentHigh-value deals, limited known buyers per market
Packaging materialsGlobal demand, fragmented buyer landscape
Food & ingredientsRegulatory-driven, market-specific buyers
These are launch verticals. The engine works for any B2B industry — segments are admin-configurable per tenant.

​
Expansion Segments (Beyond Exporters)

SegmentUse CaseTimeline
Domestic B2B sales teamsFinding buyers within their own countryYear 2+
Procurement teamsFinding suppliers and partnersYear 2+
Trade agencies & associationsMatchmaking as a member serviceYear 3+

​
Market Sizing (Research-Backed)

LevelScopeEstimateSource
TAMAll B2B companies globally needing buyer/lead discovery~5M+ companiesTrade data extrapolation
SAMB2B exporters in US (270K) + EU (750K) + Turkey (180K) + key markets~1.5M companiesCensus Bureau, EU Commission, TUIK
SOMExporters in initial verticals, English/Turkish-speaking, digitally reachable (Year 1)~10K-20K companiesFiltered from SAM
Detailed TAM/SAM/SOM with revenue projections in 05-roi-scoring.md.

​
3. Channels

​
Acquisition Channels (Ordered by Priority)

#ChannelStageCostDescription
1Personal networkFirst 10 usersFreeFounder’s existing business contacts, trade fair connections
2Trade fairsFirst 30 usersTravel costLive demos at ITMA, Texworld, Heimtextil. Pitch: “Tell me your product + country, I’ll show your top 5 buyers in 30 seconds”
3LinkedInFirst 100 usersFree/lowContent marketing: “How I find distributors in Germany in 15 minutes using AI”
4Referrals30-100+ usersFreePilot users introduce colleagues and partners
5Export associations100+ usersPartnershipIndustry groups, exporter communities, chambers of commerce
6Product-led growthScaleBuilt-inSelf-serve signup → run discovery → see results → convert

​
GTM Flywheel

Exporter discovers buyers (Cernio)
        |
Saves leads, contacts decision makers
        |
Closes distributor deal
        |
Shares experience with peers
        |
More exporters join
        |
More data improves discovery accuracy
        |
(Repeat — network effects compound)

​
Activation Metric

User runs first discovery search — must happen in the first session.

​
4. Revenue Streams

​
Primary: Hybrid SaaS + Credit Model

Revenue = Subscription (predictable) + Credit Packs (usage-based)
StreamMechanismTarget
SubscriptionMonthly/annual plans (Free, Pro, Team, Enterprise)Predictable MRR
Credit packsOn-demand credit purchases for exceeding plan limitsPower user monetization
Seat expansionAdditional team members on Team/Enterprise plansARPU growth

​
Plan Pricing

PlanPriceTarget UserKey Limits
Free$0Trial — deliver WOW moment3 searches/mo, 10 companies/search, 1 contact reveal
Pro$39-79/moIndividual exporter50 searches/mo, 25 companies/search, 100 reveals
Team$99-199/moExport teams (2-10 people)200 searches/mo, 500 reveals, shared workspace
EnterpriseCustomLarge firms, multi-orgUnlimited, API access, custom integrations

​
Credit Pack Pricing

PackCreditsPricePer-Credit
Small50~$15$0.30
Medium200~$50$0.25
Large1000~$200$0.20

​
Credit Costs per Action

ActionCreditsActual AI Cost
Buyer discovery search1~$0.06
Contact reveal1~$0.03
Deep company analysis2~$0.10
Market intelligence report3~$0.15
Batch operation (per company)0.5~$0.03

​
Future Revenue Streams

StreamTimelineDescription
Market intelligence reportsStage 4Premium country/industry analysis
Tariff intelligenceStage 4Export-specific regulatory data
HubSpot/CRM integrationStage 3Connector premium
API accessEnterpriseProgrammatic buyer discovery

​
5. Key Resources

​
Technology

ResourceRole
AI Discovery PipelineCore product — product analysis, query generation, web retrieval, scoring, ranking
FitScore AlgorithmProprietary company scoring: industryMatch * 0.5 + distributorProbability * 0.5
Multi-provider AI ClientProvider-agnostic (Gemini, Claude, OpenAI, Perplexity) — cost optimization
Supabase (PostgreSQL)Multi-tenant database with RLS, 18 tables
Next.js 16 ApplicationFull-stack web app (React 19, App Router)

​
Data Assets (Growing Over Time)

AssetCurrent StateFuture Value
Company database~thousands of companiesBuyer signal graph
Contact databaseGrowing via headhuntContact intelligence network
Search history30-day cacheQuery pattern analysis
User feedbackCollected (not yet active in ranking)Discovery accuracy improvement
Outcome dataNot yet tracked”Which leads actually converted?” — strongest moat

​
Human

ResourceRole
Founder (Alex)Product vision, development, GTM, customer relationships
AI coding assistantsDevelopment acceleration (Claude Code, etc.)

​
6. Key Activities

ActivityDescriptionStage
Product developmentBuilding and shipping features (Ring 1-4 roadmap)Ongoing
AI pipeline optimizationImproving discovery accuracy, reducing hallucinationOngoing
Customer developmentPilot user feedback loops, validationNow → Beta
Content marketingLinkedIn posts, trade fair presencePre-launch
Data enrichmentGrowing company + contact databaseOngoing
Infrastructure managementHetzner VPS deployment, monitoringPost-beta

​
7. Key Partners

Partner TypeExamplesValue
AI providersGoogle (Gemini), Anthropic (Claude), OpenAI, PerplexityLLM + search capabilities
InfrastructureHetzner (VPS), Supabase (DB), Cloudflare (CDN)Hosting + data
Trade fair organizersITMA, Texworld, HeimtextilUser acquisition channel
Export associationsTurkish Exporters Assembly, industry chambersDistribution + credibility
Payment processorStripeBilling infrastructure

​
8. Cost Structure

​
Fixed Costs (Monthly)

ItemCostNotes
Hetzner VPS (2 servers)~$19/mo (€17-18)Galata (production) + Kadikoy (worker)
Supabase (self-hosted)$0Included in VPS
Domain + DNS~$2/moCloudflare
Founder salary$0 (bootstrap)Self-funded initially
Total fixed: ~$21/mo

​
Variable Costs (Per-Use)

ItemCostTrigger
AI API calls (LLM)~$0.02/searchEvery discovery query
Web search APIs~$0.03/searchEvery discovery query
Data processing~$0.01/searchParsing, scoring
Total per search~$0.06—

​
Cost at Scale (Projections)

UsersSearches/moAI Cost/moInfra/moTotal/mo
10300$18$21$39
501,500$90$21$111
2006,000$360$40$400
1,00030,000$1,800$80$1,880
Detailed projections in 03-financial-projections.md and 07-production-simulation.md.

​
9. Competitive Positioning

​
Category Intersection

Cernio sits at the intersection of four existing categories — none of which solve the full exporter workflow:
CapabilityTraditional ToolTheir WeaknessCernio Advantage
Buyer discoveryTrade directories (Kompass, Europages)Static data, no ranking, manual filteringAI-ranked results in seconds
Contact discoverySales intelligence (Apollo, ZoomInfo)Built for SaaS sales, weak in industrial sectorsExport-native contact finding
Lead workflowCRM (HubSpot, Salesforce)Assumes leads already exist, not designed for exportDiscovery + workflow in one tool
Market intelligenceImport data (ImportGenius, Panjiva)Incomplete coverage, no contact dataAI-structured market insights

​
Category Creation

Cernio is not a CRM, not a directory, not a sales tool. It’s creating a new category: B2B Buyer Intelligence Platform — AI-native buyer discovery + workflow + market intelligence

​
4 Strategic Advantages

#AdvantageMoat Depth
1Export specialization — Competitors are generic; Cernio is exporter-specificMedium
2Workflow ownership — Discovery + leads + follow-ups = high switching costsHigh
3Data moat — Buyer signals, contact signals, outcome signals compound over timeHigh (grows)
4AI leverage — 3-4 hours manual → <15 min AI = 10x improvementMedium (replicable)

​
Strategic Risks

RiskSeverityMitigation
AI hallucination (wrong classifications)HighScoring models + deterministic logic + human feedback loop
Data freshness (companies change)MediumRegular re-enrichment, user-reported changes
Contact accuracy (email discovery)MediumMulti-source verification, confidence scoring
Big player pivot (Apollo adds export focus)Low-MediumFirst-mover advantage, data moat, vertical depth

​
10. Product Evolution Roadmap

Stage 1: AI Buyer Discovery         ← CURRENT (MVP built, v0.74)
    |
Stage 2: Contact Intelligence       ← NEXT (headhunt exists, needs polish)
    |
Stage 3: Export Workflow             ← Lead workspace, follow-ups, trade fair
    |
Stage 4: Export Intelligence         ← Market reports, tariff data, competitor intel
    |
Stage 5: B2B Buyer Intelligence Platform  ← System of record for buyer relationships
Each stage increases switching costs and deepens the data moat.

​
Summary: Business Model in One Paragraph

Cernio is an AI-powered B2B buyer intelligence platform that helps companies discover buyers, identify decision makers, and manage leads — replacing 3-4 hours of manual research with 15-minute AI-powered discovery. It monetizes through a hybrid SaaS + credit model (Free/0,Pro/0, Pro/39-79, Team/99−199)withstronguniteconomics( 99-199) with strong unit economics (~0.06 AI cost per $0.20-0.30 credit value, 70-80% gross margin). Initial GTM targets Turkey-based manufacturing exporters as beachhead, expanding globally through personal network, trade fairs, and LinkedIn, with product-led growth at scale. The platform creates compounding value through a data moat (buyer signals, contact signals, outcome data) that improves discovery accuracy over time, making it increasingly difficult for generic tools to compete.
Next: 02-revenue-cost-structure.md — Detailed revenue model and cost analysis