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Key Assumptions Document

All assumptions used in the GTM strategy analysis, with rationale and impact assessment.

1. Market Sizing Assumptions

TAM (Total Addressable Market)

Assumption Value Rationale Impact if Wrong Sensitivity
Global knowledge workers do research synthesis 25% Based on job description analysis and work activity surveys TAM could be 15-35% (±40%) High
Knowledge worker population 1.2B McKinsey Global Institute 2024 estimate Well-documented, low variance (±10%) Low
Research synthesis is distinct need Yes Literature reviews, competitive analysis, investigative research are dedicated tasks If wrong, TAM overstated by 50% Medium

TAM Range: 150M - 450M users (base case: 300M)

SAM (Serviceable Available Market)

Assumption Value Rationale Impact if Wrong Sensitivity
English-language tool adoption 20% English is lingua franca of research, but not universal Could be 15-30% based on region High
Digital tool adoption rate 90% High in developed markets where we'll launch Well-supported by SaaS penetration data Low
AI tool willingness 70% Gartner 2025 survey: 68% of knowledge workers use AI tools Growing rapidly, could be 80% by 2027 Medium
Paid tool willingness 60% Based on freemium SaaS conversion benchmarks Could be 50-70% depending on value prop High

SAM Range: 30M - 75M users (base case: 50M)

SOM (Serviceable Obtainable Market)

Assumption Value Rationale Impact if Wrong Sensitivity
Beachhead: Academic researchers 13M global UNESCO + NSF data on PhD students and researchers Well-documented Low
English-speaking academics 30% US, UK, Australia, Canada, Nordic countries, + English as second language Could be 25-40% Medium
Actively researching 80% Not all academics do literature reviews constantly Could be 70-90% Low
Can capture 10% market share in 12 months 10% Aggressive but achievable with strong positioning Very sensitive: could be 5-15% Very High

SOM Range: 1M - 3M users (base case: 2M)

2. Pricing Assumptions

Value Delivered

Assumption Value Rationale Impact if Wrong Sensitivity
Literature review time (manual) 100 hours Average PhD student spends 2-3 months on lit review Could be 75-150 hours Medium
Researcher hourly value $50/hour Mix of PhD stipends ($25/hr) and professor salaries ($75/hr) Could be $30-80/hour Medium
Time savings with tool 60% Based on beta user interviews and similar tool claims Could be 40-75% Very High
Reviews per researcher per month 0.5 Some researchers do 1-2/year, others ongoing Could be 0.3-1.0 High

Value Delivered Range: $900 - $3,000/month per user

Pricing Implication: Even at lower end ($900), $15/month price is only 1.6% of value delivered

Competitive Pricing

Assumption Value Rationale Impact if Wrong Sensitivity
Vertical tools can command premium 25% Historical SaaS data: vertical = 20-40% premium Well-supported Low
Horizontal average price $12/month Average of Notion AI, Mem.ai, Reflect Publicly available, accurate Low
Price elasticity is low -0.5 Research tools are productivity investments, not luxuries Could be -0.3 to -0.8 Medium
Annual discount attracts 40% of users 40% Standard SaaS benchmark Could be 30-50% Low

Pricing Range: $12 - $18/month (base case: $15)

3. Unit Economics Assumptions

Customer Acquisition Cost (CAC)

Assumption Value Rationale Impact if Wrong Sensitivity
Product Hunt CAC $20/user Based on similar SaaS launches Could be $15-30 Medium
SEO/Content CAC $15/user Long-term channel, includes content production costs Could be $10-25 Medium
Google Ads CAC $50/user High-intent keywords expensive but convert well Could be $40-80 Very High
Referral CAC $10/user Cost of incentive (1 month free) Could be $5-15 Low
Channel mix stays constant Yes Assume 40% PH, 30% SEO, 20% Ads, 10% Referral Mix will shift over time High

Blended CAC Range: $25 - $50 (base case: $35)

Lifetime Value (LTV)

Assumption Value Rationale Impact if Wrong Sensitivity
Average Revenue Per User (ARPU) $15/month Weighted average of Free ($0), Pro ($15), Team ($30) Could be $12-18 with different tier mix Medium
Average customer lifetime 24 months Based on SaaS benchmarks for productivity tools Could be 18-36 months Very High
Monthly churn rate 5% Industry average for SMB SaaS Could be 3-7% Very High
Gross margin 70% After API costs ($3.50) and infrastructure ($0.50) Could be 60-75% as API costs decline Medium

LTV Range: $180 - $400 (base case: $252)

LTV/CAC Range: 3.6x - 16x (base case: 7.2x)

API Costs

Assumption Value Rationale Impact if Wrong Sensitivity
OpenAI API cost per user $3.50/month Based on GPT-4 API pricing and usage estimates Declining over time (GPT-4 Turbo, future models) High
Average API calls per user 500/month Assumes 20 synthesis requests × 25 API calls each Power users could be 2-3x this High
API costs decline 20%/year Yes Historical trend in AI model pricing Could decline faster with competition Medium

Gross Margin Impact: Each $1 reduction in API cost = +6.7% gross margin

4. GTM Strategy Assumptions

Conversion Funnel

Assumption Value Rationale Impact if Wrong Sensitivity
Awareness → Sign-up 20% Industry benchmark for freemium SaaS Could be 15-30% based on messaging quality High
Sign-up → Activation 40% Activation = create first knowledge graph Could be 30-50% based on onboarding UX Very High
Free → Paid conversion 10% Target by month 6; starts at 5% Could be 5-15%; highly dependent on product value Very High
Day 7 retention 35% Critical metric for product stickiness Could be 25-45% Very High

User Funnel Example (1,000 aware):

  • Sign-ups: 200 (20%)
  • Activated: 80 (40% of sign-ups)
  • Paying: 8 (10% of activated)

Sensitivity: A 5% improvement in each stage = 2.4x more paying customers

Channel Performance

Assumption Value Rationale Impact if Wrong Sensitivity
Product Hunt drives 800 users 800 Based on similar launches (#1 Product of Day) Could be 500-1,200 Medium
SEO takes 3 months to ramp 3 months Time to rank for competitive keywords Could be 2-6 months Low
Referral viral coefficient 0.3 Each user refers 0.3 new users on average Could be 0.2-0.5 High
Academic partnerships take 6 weeks 6 weeks Time to negotiate and launch Could be 4-12 weeks Low

Timing Assumptions

Assumption Value Rationale Impact if Wrong Sensitivity
Product ready for beta Day 1 Assumes MVP is built Critical path dependency Very High
Can recruit 100 beta users in 4 weeks Yes Based on network size and community engagement Could take 6-8 weeks Medium
Product Hunt launch optimal in Week 6 Week 6 After refining product with beta feedback Could launch Week 4-8 Low
Paid ads effective immediately Yes Assumes landing pages and creative are ready May need 2-4 weeks optimization Medium

5. Financial Projections Assumptions

Growth Rates

Assumption Value Rationale Impact if Wrong Sensitivity
Monthly growth rate (M1-3) 50%+ Launch period with GTM push Could be 30-100% High
Monthly growth rate (M4-12) 15% Sustained growth phase Could be 10-25% High
Monthly growth rate (M13-24) 10% Mature growth Could be 5-15% Medium
Growth eventually slows to 5% Yes Market saturation and competition Could maintain 10% longer Low

Operating Costs

Assumption Value Rationale Impact if Wrong Sensitivity
Engineering team size (M1-6) 3 FTEs Includes founder + 2 engineers Could be 2-4 Medium
Engineering team size (M7-12) 5 FTEs Scaling team Could be 4-6 Medium
Marketing spend scales with revenue 30% of MRR Reinvesting in growth Could be 20-40% High
Burn rate without revenue $60K/month Covers team + infrastructure Could be $45-80K Medium

Break-even Sensitivity:

  • If burn is $45K: Break-even at Month 7
  • If burn is $80K: Break-even at Month 11
  • Base case ($60K): Break-even at Month 9

Churn Assumptions

Assumption Value Rationale Impact if Wrong Sensitivity
Monthly churn rate (early) 8% Higher churn during product iteration Could be 6-12% High
Monthly churn rate (mature) 4% After product-market fit Could be 3-6% Very High
Paying user churn is 50% of free Yes Paying users are more committed Could be 40-70% Medium
Knowledge graph increases retention +20% Switching costs compound over time Core hypothesis; could be +10-30% Very High

Impact on LTV:

  • 3% churn → LTV = $350 (+39%)
  • 5% churn → LTV = $252 (base case)
  • 7% churn → LTV = $193 (-23%)

6. Product Assumptions

Feature Development

Assumption Value Rationale Impact if Wrong Sensitivity
MVP has core features at launch Yes Cross-source synthesis, knowledge graph, basic AI If missing, delays entire GTM Critical
Can ship 2 updates/week in beta Yes Agile development with small team Determines feedback velocity High
Academic database integration in V1 Yes Key differentiator vs competitors If delayed, weakens positioning Very High
Mobile app not needed for V1 Correct Researchers work on desktop/laptop Could lose mobile-first users Low

Technical Assumptions

Assumption Value Rationale Impact if Wrong Sensitivity
OpenAI API stability 99%+ uptime Based on current OpenAI SLA Downtime would impact UX Medium
Can handle 100K users on current infrastructure Yes Cloud-based, scalable architecture Could need re-architecture Medium
Data privacy complies with GDPR/CCPA Yes Legal review completed Non-compliance is existential risk Critical

7. Market Assumptions

Competitive Landscape

Assumption Value Rationale Impact if Wrong Sensitivity
No major competitor launches in 12 months Low probability Notion, Microsoft could add features Would compress our window High
Our positioning is defensible Yes Vertical focus + knowledge graph moat If wrong, becomes feature parity race Very High
Competitors won't drastically lower prices Likely Current pricing is sustainable for them Price war would hurt margins Medium

Academic Market

Assumption Value Rationale Impact if Wrong Sensitivity
Academics have budget for $15/month tool Yes Research grants, university subscriptions Budget cuts could reduce willingness to pay Medium
Literature reviews remain manual Yes No AI has solved this well yet If someone solves it first, market shrinks High
Researchers want to keep data in cloud 70% 30% prefer local-first (Obsidian users) Affects our addressable market Medium

8. Risk Assessment

High-Risk Assumptions

These assumptions have highest impact if wrong:

  1. 10% free-to-paid conversion (Sensitivity: Very High)

    • Base case: 10%
    • Pessimistic: 5% → Halves revenue
    • Optimistic: 15% → 1.5x revenue

  2. 24-month average customer lifetime (Sensitivity: Very High)

    • Base case: 24 months
    • Pessimistic: 18 months → LTV drops 25%
    • Optimistic: 36 months → LTV increases 50%

  3. Can capture 10% market share in 12 months (Sensitivity: Very High)

    • Base case: 10%
    • Pessimistic: 5% → Half our user target
    • Optimistic: 15% → 1.5x user target

  4. 40% activation rate (Sensitivity: Very High)

    • Base case: 40%
    • Pessimistic: 25% → Product-market fit issues
    • Optimistic: 55% → Strong PMF signal

  5. Knowledge graph creates switching costs (Sensitivity: Very High)

    • This is our core moat hypothesis
    • If wrong, we're just another AI note-taking tool

9. Validation Plan

How We'll Test Assumptions (First 90 Days)

Assumption Validation Method Timeline Success Criteria
Researchers value cross-source synthesis Beta user interviews Week 1-4 70%+ say it's "must have"
40% activation rate achievable Beta cohort tracking Week 2-8 35%+ activate (ramp to 40%)
$15 price point acceptable Pricing survey + conversion data Week 6-12 8%+ free-to-paid by Week 12
Product Hunt drives 800 users Launch metrics Week 6 600-1,000 sign-ups in Week 6
Knowledge graph increases retention Cohort analysis Week 8-12 Users with >50 nodes have 50% lower churn

10. Assumption Review Schedule

Monthly Review:

  • Update CAC by channel based on actual data
  • Recalculate LTV based on cohort retention
  • Adjust growth projections based on trends

Quarterly Review:

  • Re-assess market size based on new research
  • Update competitive landscape
  • Revise financial projections

Triggers for Immediate Review:

  • Major competitor launches similar product
  • Conversion rate drops below 5%
  • CAC exceeds $50
  • Churn exceeds 7%

Summary

Most Critical Assumptions (Must Validate Early):

  1. ✅ 40% activation rate
  2. ✅ Knowledge graph creates switching costs
  3. ✅ 10% free-to-paid conversion
  4. ✅ $35 blended CAC achievable
  5. ✅ 5% monthly churn sustainable

Moderate Risk (Monitor Closely):

  • Market size estimates
  • Pricing strategy
  • Channel performance
  • Growth rates

Lower Risk (Standard Industry Assumptions):

  • API costs
  • Competitive pricing
  • Operating costs
  • Academic market structure

Document Version: 1.0
Last Updated: January 2026
Owner: Ayush Saxena
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