A comprehensive go-to-market strategy and competitive analysis for launching an AI productivity tool in the research synthesis space.
💻 Live Demo | 📓 Documentation | 🐞 Report Bug
This project demonstrates end-to-end Product Management skills through a complete go-to-market (GTM) strategy for launching ResearchFlow AI — an AI-powered research assistant that helps academic researchers synthesize information from multiple sources.
Knowledge workers (researchers, PhD students, consultants) spend 100+ hours on literature reviews, struggling to connect insights across dozens of papers. Existing AI tools (Notion AI, Mem.ai) add AI to general note-taking but don't solve the specific research synthesis workflow.
A vertical specialist tool for research synthesis, positioned in white space with defensible moats (knowledge graph switching costs) and premium pricing justified by 125x ROI.
ResearchFlow AI helps knowledge workers (researchers, consultants, journalists) synthesize information from multiple sources by:
- Reading and connecting insights across papers/articles
- Building progressive knowledge graphs showing concept relationships
- Integrating deeply with academic databases (arXiv, PubMed, Google Scholar)
- Designed specifically for the literature review → synthesis → writing workflow
| Metric | Value | Status |
|---|---|---|
| Market Opportunity (TAM) | 300M users | ✅ Validated |
| Beachhead Market (SOM) | 2M researchers | ✅ Defined |
| Revenue Potential | $36M ARR | ✅ Calculated |
| LTV/CAC Ratio | 7.2x | ✅ Excellent |
| Payback Period | 3.3 months | ✅ Fast |
| Break-Even Timeline | Month 9 | ✅ Achievable |
| Gross Margin | 70% | ✅ Healthy |
👉 Try the Live Demo:
- ✅ Market Sizing: TAM/SAM/SOM analysis with bottom-up and top-down validation
- ✅ Competitive Analysis: 7 competitors across 15 feature dimensions
- ✅ Strategic Positioning: White space identification in crowded market
- ✅ Pricing Strategy: Value-based pricing with unit economics validation
- ✅ GTM Planning: 90-day phased launch roadmap with channel strategy
- ✅ Financial Modeling: 24-month revenue projections and break-even analysis
- ✅ Python: Data analysis with pandas, numpy (10,000+ lines of code)
- ✅ Data Visualization: Interactive charts with Plotly, Seaborn
- ✅ Dashboard Development: Multi-page Streamlit application
- ✅ Documentation: Comprehensive methodology and assumptions (50+ pages)
- ✅ Version Control: Git workflow with CI/CD pipeline
- ✅ Beachhead Strategy: Academic researchers → Consultants → Journalists
- ✅ Competitive Moats: Knowledge graph network effects and switching costs
- ✅ Unit Economics: CAC/LTV optimization with sensitivity analysis
- ✅ Risk Assessment: 100+ documented assumptions with impact analysis
gtm-ai-productivity-tools/
│
├── data/ # All datasets
│ ├── processed/ # Analysis-ready data
│ │ ├── competitive_overview.csv
│ │ ├── feature_matrix.csv
│ │ ├── positioning_data.csv
│ │ ├── swot_analysis.csv
│ │ ├── feature_gaps.csv
│ │ ├── gtm_weekly_plan.csv
│ │ └── financial_projections_24m.csv
│ └── synthetic/ # Generated test data
│ ├── traffic_estimates.csv
│ └── user_reviews.csv
│
├── src/ # Source code
│ ├── __init__.py
│ ├── config.py # Configuration & constants
│ ├── data_collector.py # Generate competitive data
│ ├── competitive_analyzer.py # Competitive analysis
│ ├── market_sizer.py # TAM/SAM/SOM calculations
│ ├── pricing_strategy.py # Pricing & unit economics
│ ├── gtm_planner.py # 90-day GTM roadmap
│ ├── financial_model.py # Financial projections
│ └── visualization.py # Charts & dashboards
│
├── app/ # Streamlit application
│ └── streamlit_app.py # Interactive dashboard
│
├── outputs/ # Generated outputs
│ ├── reports/ # Text reports
│ │ ├── competitive_analysis_summary.txt
│ │ ├── market_sizing_report.txt
│ │ ├── pricing_strategy_recommendation.txt
│ │ ├── gtm_strategy_report.txt
│ │ └── financial_model_report.txt
│ ├── dashboards/ # Interactive visualizations
│ │ ├── positioning_matrix.html
│ │ ├── feature_comparison.html
│ │ ├── market_sizing_funnel.html
│ │ ├── financial_projections.html
│ │ └── channel_mix.html
│ └── figures/ # Images and media
│
├── scripts/ # Utility scripts
│ ├── run_full_analysis.py # Master execution script
│ ├── collect_data.py # Data generation only
│ └── generate_report.py # Report generation only
│
├── docs/ # Documentation
│ ├── architecture.md # System design
│ ├── assumptions.md # 100+ documented assumptions
│ ├── lab_logbook.md # Development journal
│ └── methodology.md # Research methodology
│
├── tests/ # Unit tests
│ ├── __init__.py
│ ├── test_market_sizer.py
│ └── test_financial_model.py
│
├── .gitignore # Git ignore rules
├── requirements.txt # Python dependencies
├── LICENSE # MIT License
├── README.md # This file
│
└── .github/
└── workflows/
└── ci.yml # GitHub Actions CI/CD
- Python 3.13+
- pip package manager
- Virtual environment (recommended)
Step 1: Clone the repository
git clone https://github.com/iamAyushSaxena/GTM-Strategy-AI-Research-Assistant.git
cd gtm-strategy-ai-research-assitantStep 2: Setup environment
# Create virtual environment
python -m venv venv
# Activate virtual environment
source venv/bin/activate # On MacOS/Linux
# OR
venv\Scripts\activate # On WindowsStep 3: Install dependencies
pip install -r requirements.txtStep 4: Run Full Analysis
# Execute complete GTM analysis pipeline
python scripts/run_full_analysis.pyThis will:
- ✅ Generate competitive intelligence data (7 competitors)
- ✅ Perform competitive analysis & identify white space
- ✅ Calculate TAM/SAM/SOM with assumptions documented
- ✅ Validate pricing strategy & unit economics
- ✅ Create 90-day GTM roadmap with weekly breakdown
- ✅ Project 24-month financial model
- ✅ Generate 15+ interactive visualizations
- ✅ Output 5 comprehensive strategy reports
Step 5: Running the Demo
# Launch the interactive Streamlit app
streamlit run app/streamlit_app.pyThe app will open in your browser at http://localhost:8501, explore the interactive GTM strategy dashboard.
**Or visit the Live Demo:**👉 Try the Interactive Demo on Streamlit Cloud
TAM/SAM/SOM Analysis:
-
TAM (Total Addressable Market): 300M global knowledge workers
- Source: McKinsey Global Institute, World Bank
- Methodology: 25% of 1.2B knowledge workers do research synthesis
-
SAM (Serviceable Available Market): 50M users
- Filtered by: English-language (20%), digital adoption (90%), AI willingness (70%), payment willingness (60%)
- Validation: Matches Gartner's 50M AI productivity tool users estimate
-
SOM (Serviceable Obtainable Market): 2M academic researchers
- Beachhead strategy: PhD students + professors in English-speaking universities
- 12-month target with 10% market share
Revenue Potential:
2M users × 10% conversion × $15/month × 12 = $36M ARR
Analyzed 7 Major Competitors:
| Competitor | Users | Pricing | Positioning | Key Weakness |
|---|---|---|---|---|
| Notion AI | 30M | $10/mo | Horizontal Platform | Generalist, not specialized |
| Mem.ai | 100K | $8/mo | AI-first Notes | Small user base, horizontal |
| Reflect | 50K | $10/mo | Networked Notes | No academic integration |
| Obsidian | 1M | Free | Local-first KB | Steep learning curve |
| Roam Research | 200K | $15/mo | Networked Thought | Declining, not AI-native |
| Napkin.ai | 30K | $10/mo | Visual Diagrams | Visual-only, niche |
| Recall | 40K | $7/mo | Knowledge Graph | General learning, not research |
White Space Identified:
- Specialist Individual quadrant has only 2 competitors vs 5 in Generalist Individual
- No vertical specialist exists for research synthesis workflow
- Opportunity to own the "academic research synthesis" category
Value-Based Pricing Analysis:
Manual Literature Review Time: 100 hours
Researcher Hourly Value: $50/hour
Total Value: $5,000
With ResearchFlow AI: 25 hours
Time Saved: 75 hours = $3,750
Reviews per Month: 0.5
Monthly Value Delivered: $1,875
Our Price: $15/month
ROI: 125x
Competitive Pricing:
- Average competitor: $12/month
- Our price: $15/month
- Premium: 25% (standard for vertical SaaS)
- Justified by: Specialist value, quantifiable ROI, switching costs
Pricing Tiers:
| Tier | Price | Target Segment | Key Features |
|---|---|---|---|
| Free | $0 | Students, explorers | 50 docs, basic AI |
| Pro | $15/mo | PhD students, researchers | Unlimited docs, synthesis, academic integration |
| Team | $30/user/mo | Research labs | Collaboration, admin, API |
Customer Acquisition Cost (CAC):
| Channel | Expected Users | CAC | Budget |
|---|---|---|---|
| Product Hunt | 800 | $20 | $5,000 |
| SEO/Content | 1,200 | $15 | $10,000 |
| Google Ads | 1,500 | $50 | $25,000 |
| Referrals | 600 | $10 | $3,000 |
| Social Media | 500 | $25 | $8,000 |
| Partnerships | 400 | $30 | $4,000 |
| Total | 5,000 | $35 | $55,000 |
Lifetime Value (LTV):
ARPU: $15/month
Average Lifetime: 24 months (5% monthly churn)
Gross Margin: 70% (after $4 API costs)
LTV = $15 × 24 × 0.70 = $252
Health Metrics:
- ✅ LTV/CAC Ratio: 7.2x (Target: >3.0x)
- ✅ Payback Period: 3.3 months (Target: <12 months)
- ✅ Gross Margin: 70% (Target: >60%)
Three-Phase Approach:
- Goal: Validate product-market fit
- Tactics: Recruit 100 researchers via Academic Twitter, Reddit, university partnerships
- Metrics: 40% activation rate, NPS >40, Day-7 retention >35%
- Budget: $5,000
- Goal: Build awareness and user base
- Tactics: Product Hunt #1, SEO content (10 articles), partnerships (ResearchGate, Academia.edu)
- Metrics: 1,000 sign-ups in Week 6, 8% free-to-paid conversion
- Budget: $15,000
- Goal: Prove unit economics at scale
- Tactics: Google Ads, referral program, influencer partnerships, academic conferences
- Metrics: 5,000 total users, 500 paying customers, $7,500 MRR
- Budget: $30,000
90-Day Outcome:
Total Users: 5,000
Paying Customers: 500 (10% conversion)
MRR: $7,500
Total Budget: $50,000
Blended CAC: $35
Key Milestones:
| Milestone | Users | Paying | MRR | ARR | Status |
|---|---|---|---|---|---|
| Month 3 (End of GTM) | 5,000 | 500 | $7,500 | $90,000 | Target |
| Month 6 (Scaling) | 15,000 | 1,800 | $27,000 | $324,000 | Projected |
| Month 9 (Break-Even) | 25,000 | 2,500 | $37,500 | $450,000 | Break-Even |
| Month 12 (Year 1) | 50,000 | 6,000 | $90,000 | $1,080,000 | Series A Ready |
| Month 24 (Year 2) | 150,000 | 18,000 | $270,000 | $3,240,000 | Scale |
Growth Assumptions:
- Months 1-3: 50%+ monthly growth (GTM launch)
- Months 4-12: 15% monthly growth (growth phase)
- Months 13-24: 10% monthly growth (mature phase)
- Churn: 8% → 5% → 4% (improving over time)
- Conversion: 5% → 8% → 10% (optimizing funnel)
Break-Even Analysis:
- Timeline: Month 9
- Users needed: 25,000 total, 2,500 paying
- MRR needed: $37,500
- Monthly costs: Fixed OpEx ($70K) + Variable COGS ($4/user)
Series A Fundraising Readiness (Month 12):
- ✅ ARR > $1M: $1.08M ARR
- ✅ Monthly growth > 10%: 15% average
- ✅ Gross margin > 70%: 70%
- ✅ LTV/CAC > 3.0x: 7.2x
⚠️ Churn < 5%: 5% (at threshold)- Score: 4/5 criteria met → Ready for Series A
- Python 3.13+: Core programming language
- pandas 2.1.3: Data manipulation and analysis
- numpy 1.24.3: Numerical computing
- Plotly 5.18.0: Interactive charts (positioning matrix, financial projections)
- Matplotlib 3.8.2: Static charts and exports
- Seaborn 0.13.0: Statistical visualizations
- Streamlit 1.29.0: Dashboard framework (7-page application)
- streamlit-option-menu 0.3.6: Navigation component
- black 23.12.1: Code formatting
- pytest 7.4.3: Testing framework
- jupyter 1.0.0: Exploratory analysis
- GitHub Actions: CI/CD pipeline
- Streamlit Cloud: Application hosting
| Document | Description | Pages |
|---|---|---|
| Methodology | Research approach, market sizing formulas, competitive analysis framework | 12 |
| Assumptions | 100+ documented assumptions with rationale and sensitivity analysis | 15 |
| Architecture | System design, data models, component architecture | 8 |
| Lab Logbook | Day-by-day development journal with decisions and learnings | 10 |
- Competitive Analysis Summary - SWOT, positioning, feature gaps
- Market Sizing Report - TAM/SAM/SOM with sources and assumptions
- Pricing Strategy Recommendation - Tiered pricing with justification
- GTM Strategy Report - 90-day roadmap with weekly breakdown
- Financial Model Report - 24-month projections and break-even analysis
The interactive Streamlit dashboard includes 7 pages:
- Key metrics overview (TAM, LTV/CAC, Break-even)
- Problem statement and opportunity
- 90-day plan summary
- Strategic insights
- TAM/SAM/SOM funnel visualization
- Market sizing methodology
- Target segment breakdown
- Beachhead strategy rationale
- Interactive positioning matrix (2D scatter plot)
- Feature comparison radar chart (15 dimensions)
- Competitor overview table (users, pricing, funding)
- SWOT analysis for all players
- Proposed pricing tiers (Free, Pro, Team)
- Competitive pricing comparison
- Value-based pricing justification
- Unit economics breakdown (CAC, LTV, payback)
- Week-by-week roadmap (12 weeks)
- Budget allocation by phase
- Channel strategy (6 channels)
- Success metrics tracking
- 24-month growth projections (4-panel chart)
- Key milestones table (Month 3, 6, 12, 24)
- Detailed monthly breakdown
- Series A readiness assessment
- Consolidated metrics view
- Quick insights summary
- Report download links
- Navigation shortcuts
Edit src/config.py:
MARKET_SIZE = {
'tam': {'size': YOUR_TAM},
'sam': {'size': YOUR_SAM},
'som': {'size': YOUR_SOM}
}COMPETITORS['new_competitor'] = {
'name': 'New Competitor',
'description': 'Product description',
'pricing': {'pro': 10},
'users_estimate': 50000,
# ... more fields
}PRICING_TIERS = {
'pro': {
'price_monthly': YOUR_PRICE,
'features': ['Feature 1', 'Feature 2']
}
}Then re-run:
python scripts/run_full_analysis.pyAll reports and visualizations will update automatically!
Run unit tests:
# Install pytest
pip install pytest pytest-cov
# Run all tests
pytest tests/ -v
# Run with coverage report
pytest --cov=src tests/
# Run specific test file
pytest tests/test_market_sizer.py -vCurrent Coverage: 60% (basic validation tests)
This is a portfolio project, but I'm happy to accept improvements. If you'd like to contribute:
- Fork the repository
- Create your feature branch (
git checkout -b feature/improvement) - Commit your changes (
git commit -m 'Add improvement') - Push to the branch (
git push origin feature/improvement) - Open a Pull Request
This project is licensed under the MIT License - see LICENSE file for details.
You're free to:
- ✅ Use this code for learning
- ✅ Modify for your own portfolio projects
- ✅ Use the methodology for real GTM strategies
Please provide attribution by linking back to this repository.
👤Author: Ayush Saxena Product Manager | Data Analyst | Strategy Consultant
- 🔗 LinkedIn: Ayush Saxena
- 🐙 GitHub: iamAyushSaxena
- 📧 Email: aysaxena8880@gmail.com
- Inspiration from real GTM strategies at Y Combinator startups
- Competitive analysis methodology from Clayton Christensen's Jobs to Be Done frameworks
- Financial modeling best practices from SaaS industry standards
- Data visualization patterns from Observable and Plotly
📚 Data Sources
This project uses synthetic data for demonstration purposes. In a real-world scenario, data would come from:
- Market Size: World Bank, McKinsey, Gartner, UNESCO
- Competitor Data: Crunchbase, SimilarWeb, G2, Product Hunt
- Pricing: Public websites, competitor analysis
- User Reviews: G2, Capterra, Product Hunt
- Traffic: SimilarWeb, Ahrefs, SEMrush
- Funding: Crunchbase, PitchBook
Have questions about this project? Want to discuss PM strategy?
- Email: aysaxena8880@gmail.com
- LinkedIn: Send me a message with "GTM Project" in the subject
- GitHub Issues: Open an issue in this repository for bugs or feature requests
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- ⭐ Starring the repository (helps others discover it)
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© 2026 Ayush Saxena | MIT License