competitive-landscape-research/METHODOLOGY.md

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)