Strategic product research and GTM documentation for Spear, an AI-native outbound pipeline engine that books meetings for technical B2B SaaS founders — so they can focus on building product, not doing sales.
This repository contains the complete product strategy, technical architecture, business model, and go-to-market plan for Spear — packaged as a professionally structured documentation site built with Astro Starlight.
The research covers the full product lifecycle from problem validation through 90-day execution, including:
- 10 discounted product ideas with specific kill criteria and competitive analysis
- Deep market landscape research across 15+ AI SDR startups, CRM incumbents, and DIY tool stacks
- Technical architecture design with stack choices, cost modeling, and AI engine specifications
- Business model with unit economics at 100, 1,000, and 10,000 customer scale
- Go-to-market strategy from first 10 customers to 8ドル.5M ARR — no ads, no sales team
- Moat analysis covering data network effects, incumbent response theory, and compounding intelligence
- Risk matrix with 5 ranked risks, mitigations, and explicit kill criteria
| Section | Pages | Covers |
|---|---|---|
| Vision & Problem | 3 | The hair-on-fire problem, market timing, competitive landscape |
| Validation | 2 | 10 rejected ideas with kill reasons, why Spear passes every filter |
| Product | 5 | Feature specs, ICP definition, magic moment, deliberate exclusions |
| Architecture | 3 | Stack choices with costs, system design diagrams, AI engine pipeline |
| Moat & Defensibility | 3 | Data flywheel, institutional memory, incumbent response analysis |
| Business Model | 3 | Pricing tiers, unit economics at scale, revenue projections |
| Go-to-Market | 3 | First 10 customers playbook, scaling to 100, distribution channels |
| Expansion | 3 | V2/V3 roadmap, bowling pin strategy, HubSpot collision timeline |
| Risks | 2 | Risk matrix visualization, all mitigations with kill criteria |
| Execution | 3 | Week-by-week 90-day plan, decision gates, day-one infrastructure |
33 pages total with 11+ Mermaid diagrams, rich Starlight components (Cards, Tabs, Steps, Badges, Asides), and full cross-linking.
| Layer | Technology |
|---|---|
| Framework | Astro + Starlight |
| Diagrams | Mermaid via @pasqal-io/starlight-client-mermaid |
| Plugins | Image zoom, links validator, blog, scroll-to-top |
| Analytics | Google Analytics 4 (Consent Mode v2), Cloudflare Web Analytics, Yandex Metrica |
| Privacy | GDPR cookie consent with EU region detection, IP anonymization |
| SEO | Open Graph, Twitter Cards, JSON-LD structured data (Product + SoftwareApplication + Person) |
| AI Discoverability | LLM meta tags (ai-indexable, ai-purpose, ai-audience), AI crawler rules in robots.txt |
| Hosting | GitHub Pages via Actions workflow |
# Clone the repo git clone git@github.com:javajack/spear-gtm.git cd spear-gtm # One-command build + serve (stateless — checks all prerequisites) ./local.sh
Or manually:
npm install npm run dev # Dev server at http://localhost:4321/spear-gtm/ npm run build # Production build to ./dist/ npm run preview # Preview production build
- Node.js >= 18
- npm
Pushes to main automatically deploy to GitHub Pages via the included workflow at .github/workflows/deploy.yml.
First-time setup: Go to Settings → Pages → Source → GitHub Actions in the repo.
The documentation includes rich Mermaid visualizations:
- System Architecture — Full stack diagram (frontend, backend, AI, data, email, job queue)
- Data Flow Sequence — Signup → prospect research → email generation → reply handling
- AI Processing Pipeline — ICP analysis → prospect scoring → email generation → reply classification
- Bowling Pin Strategy — Segment expansion from SaaS founders to mid-market
- Risk Matrix — Quadrant chart of likelihood vs. impact
- 90-Day Gantt Chart — Week-by-week execution timeline
- Revenue Growth — MRR bar chart from Month 1 to Month 24
- Competitive Positioning — Quadrant map of automation level vs. target segment
- Data Flywheel — Cross-customer intelligence compounding loop
- Market Landscape — How Spear fits between AI SDRs, tool stacks, and CRM giants
This strategy was developed through structured product discovery:
- Opportunity scanning — Evaluated 10 product ideas in the AI-native GTM space against kill criteria (moat durability, capital requirements, sales complexity, market crowding)
- Competitive analysis — Mapped 15+ funded AI SDR startups, analyzed pricing/positioning/funding, identified unserved niches
- Segment validation — Defined hyper-specific ICP with demographic, psychographic, and behavioral attributes; validated willingness-to-pay assumptions
- Technical feasibility — Designed architecture optimized for solo-founder operability at <200ドル/mo infrastructure cost
- Unit economics modeling — Built bottom-up cost models at 100/1K/10K customer scale with margin and LTV:CAC analysis
- Risk assessment — Ranked 5 risks by likelihood ×ばつ impact with specific mitigations and quantitative kill criteria
Rakesh Waghela — Technical architect and product research specialist with deep expertise in translating complex market opportunities into structured, executable product strategies. Combines hands-on technical architecture (system design, AI/LLM integration, infrastructure cost modeling) with rigorous business analysis (unit economics, competitive positioning, go-to-market planning).
Built with Astro Starlight | Deployed on GitHub Pages