Initial: competitive landscape research pipeline — methodology, tracking, Batch 001 dispatched

2026-08-10 23:16:59 -04:00
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+# Research Methodology
+
+## Dispatch Architecture
+
+### Team Composition
+Each batch is split across 3 parallel research teams, each covering 3-8 sites:
+- **Team Alpha:** Marketing/landing optimization + scraping/monitoring tools
+- **Team Bravo:** Micro-SaaS platforms + social/content tools  
+- **Team Charlie:** Career/local discovery + document tools (typically highest priority due to HotNow competitive overlap)
+
+### Model Selection
+- Default: deepseek-v4-pro (cost-effective, strong at structured analysis)
+- Premium: Claude Sonnet 5 for mission-critical competitive analysis (e.g., Locale-NYC vs HotNow)
+- Fallback: deepseek-v4-flash for high-volume scraping passes
+
+### Dispatch Pattern
+```
+delegate_task(tasks=[
+  {goal: "...", context: "...", role: "orchestrator"},
+  {goal: "...", context: "...", role: "orchestrator"},  
+  {goal: "...", context: "...", role: "orchestrator"}
+])
+```
+
+Each team subagent is an orchestrator — they can spawn their own leaf subagents for individual site scraping. This keeps context windows manageable and enables parallel site analysis.
+
+## Site Analysis Protocol
+
+### Per-Site Mandatory Extraction
+1. **Core product/service** — What do they actually sell? (not what their tagline says)
+2. **Pricing model** — Free tier? Monthly? Per-seat? Enterprise?
+3. **Key features** — The 3-5 things that define the product
+4. **UI/UX patterns** — Layout decisions, card designs, search patterns, filter approaches
+5. **USP (Unique Selling Proposition)** — What's their "one thing"?
+6. **Target audience** — Who is this for? (evidence from copy, not assumption)
+7. **Tech stack indicators** — Framework hints, JS libraries, hosting clues
+8. **Polish rating (1-5)** — Landing page quality, copy clarity, conversion strategy
+
+### Competitive Comparison Dimensions
+When comparing a target site to an ITPP product, evaluate across:
+- Feature completeness
+- UX/UI polish
+- Monetization strategy
+- Data pipeline (how do they get their content?)
+- Mobile readiness
+- Conversion funnel
+- Brand voice and positioning
+
+## Cross-Reference Protocol
+
+### Feature Mapping
+Every extracted feature is mapped to at least one ITPP product with:
+- **What:** The specific feature (e.g., "their tiered pricing table with hover comparison")
+- **From:** Source site
+- **To:** Target ITPP product
+- **Why:** Reason this feature would improve the product
+- **Effort:** Low / Medium / High implementation estimate
+- **Impact:** 1-5 rating of expected user impact
+
+### Priority Tiers
+- **P0 (Ship This Week):** 1-2 day features that close obvious gaps
+- **P1 (Next Sprint):** 3-7 day features that add significant value
+- **P2 (Roadmap):** Features requiring refactoring or new infrastructure
+- **P3 (Nice to Have):** "Eventually" features with marginal impact
+
+## New Product Identification
+
+### Signal Detection
+A new product opportunity exists when:
+- 3+ sites in a batch cluster around the same problem
+- A site solves a problem in one vertical that doesn't exist in another
+- A "pick-and-shovel" opportunity emerges (tools for the toolmakers)
+- A local version of a city-specific app is viable (e.g., Locale-NYC → HotNow Savannah)
+
+### Evaluation Criteria
+- Market size estimate (rough)
+- Competition density
+- Our unique advantage
+- MVP scope and timeline
+- AI cost for ongoing operation
+- Domain availability
+
+## Cost Tracking
+
+### Per-Batch Cost Attribution
+```
+Research Batch 001:
+  Team Alpha:  $X.XX (deepseek-v4-pro, N tool calls)
+  Team Bravo:  $X.XX (deepseek-v4-pro, N tool calls)
+  Team Charlie: $X.XX (deepseek-v4-pro, N tool calls)
+  Integration: $X.XX (deepseek-v4-pro, N tool calls)
+  Total:       $X.XX
+```
+
+Tracked via dedicated LiteLLM virtual key `competitive-research-prod` on admin-ai.
+
+### Historical Cost Baseline
+| Batch | Date | Total Cost | Sites | Cost/Site | Notes |
+|---|---|---|---|---|---|
+| 001 | 2026-08-10 | TBD | 20 | TBD | Initial batch |
+
+## Report Template
+
+Each batch produces a master report at `research/NNN-slug.md`:
+1. Executive Summary (1 paragraph)
+2. Site-by-Site Breakdown (summary table)
+3. Feature Extraction Matrix (sortable table)
+4. Product Enhancement Recommendations (grouped by product, prioritized)
+5. New Product Opportunities (concept cards)
+6. Risk Flags & Warnings
+7. Cost Summary
+8. Next Batch Queue (sites flagged for future analysis)