Initial: competitive landscape research pipeline — methodology, tracking, Batch 001 dispatched
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# Changelog
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## 2026-08-10 — Initial Pipeline Setup
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### Added
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- Project folder, README, METHODOLOGY.md, COST-TRACKING.md created
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- Gitea repo: git.itpropartner.com/ippadmin/competitive-landscape-research
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- Research Batch 001 dispatched: 20 sites across 3 parallel teams
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- Products analyzed: IntelSight, TransitPin, VentureBuilt, VerdictTank, HotNow, DRE
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# Research Methodology
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## Dispatch Architecture
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### Team Composition
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Each batch is split across 3 parallel research teams, each covering 3-8 sites:
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- **Team Alpha:** Marketing/landing optimization + scraping/monitoring tools
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- **Team Bravo:** Micro-SaaS platforms + social/content tools
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- **Team Charlie:** Career/local discovery + document tools (typically highest priority due to HotNow competitive overlap)
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### Model Selection
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- Default: deepseek-v4-pro (cost-effective, strong at structured analysis)
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- Premium: Claude Sonnet 5 for mission-critical competitive analysis (e.g., Locale-NYC vs HotNow)
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- Fallback: deepseek-v4-flash for high-volume scraping passes
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### Dispatch Pattern
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```
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delegate_task(tasks=[
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{goal: "...", context: "...", role: "orchestrator"},
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{goal: "...", context: "...", role: "orchestrator"},
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{goal: "...", context: "...", role: "orchestrator"}
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])
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```
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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.
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## Site Analysis Protocol
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### Per-Site Mandatory Extraction
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1. **Core product/service** — What do they actually sell? (not what their tagline says)
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2. **Pricing model** — Free tier? Monthly? Per-seat? Enterprise?
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3. **Key features** — The 3-5 things that define the product
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4. **UI/UX patterns** — Layout decisions, card designs, search patterns, filter approaches
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5. **USP (Unique Selling Proposition)** — What's their "one thing"?
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6. **Target audience** — Who is this for? (evidence from copy, not assumption)
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7. **Tech stack indicators** — Framework hints, JS libraries, hosting clues
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8. **Polish rating (1-5)** — Landing page quality, copy clarity, conversion strategy
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### Competitive Comparison Dimensions
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When comparing a target site to an ITPP product, evaluate across:
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- Feature completeness
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- UX/UI polish
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- Monetization strategy
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- Data pipeline (how do they get their content?)
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- Mobile readiness
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- Conversion funnel
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- Brand voice and positioning
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## Cross-Reference Protocol
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### Feature Mapping
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Every extracted feature is mapped to at least one ITPP product with:
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- **What:** The specific feature (e.g., "their tiered pricing table with hover comparison")
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- **From:** Source site
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- **To:** Target ITPP product
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- **Why:** Reason this feature would improve the product
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- **Effort:** Low / Medium / High implementation estimate
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- **Impact:** 1-5 rating of expected user impact
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### Priority Tiers
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- **P0 (Ship This Week):** 1-2 day features that close obvious gaps
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- **P1 (Next Sprint):** 3-7 day features that add significant value
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- **P2 (Roadmap):** Features requiring refactoring or new infrastructure
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- **P3 (Nice to Have):** "Eventually" features with marginal impact
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## New Product Identification
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### Signal Detection
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A new product opportunity exists when:
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- 3+ sites in a batch cluster around the same problem
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- A site solves a problem in one vertical that doesn't exist in another
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- A "pick-and-shovel" opportunity emerges (tools for the toolmakers)
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- A local version of a city-specific app is viable (e.g., Locale-NYC → HotNow Savannah)
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### Evaluation Criteria
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- Market size estimate (rough)
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- Competition density
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- Our unique advantage
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- MVP scope and timeline
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- AI cost for ongoing operation
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- Domain availability
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## Cost Tracking
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### Per-Batch Cost Attribution
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```
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Research Batch 001:
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Team Alpha: $X.XX (deepseek-v4-pro, N tool calls)
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Team Bravo: $X.XX (deepseek-v4-pro, N tool calls)
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Team Charlie: $X.XX (deepseek-v4-pro, N tool calls)
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Integration: $X.XX (deepseek-v4-pro, N tool calls)
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Total: $X.XX
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```
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Tracked via dedicated LiteLLM virtual key `competitive-research-prod` on admin-ai.
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### Historical Cost Baseline
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| Batch | Date | Total Cost | Sites | Cost/Site | Notes |
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| 001 | 2026-08-10 | TBD | 20 | TBD | Initial batch |
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## Report Template
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Each batch produces a master report at `research/NNN-slug.md`:
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1. Executive Summary (1 paragraph)
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2. Site-by-Site Breakdown (summary table)
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3. Feature Extraction Matrix (sortable table)
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4. Product Enhancement Recommendations (grouped by product, prioritized)
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5. New Product Opportunities (concept cards)
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6. Risk Flags & Warnings
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7. Cost Summary
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8. Next Batch Queue (sites flagged for future analysis)
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# Competitive Landscape Research Pipeline
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Systematic competitive analysis pipeline — scrape target sites, extract features, cross-reference against ITPP products, and identify new product opportunities. Designed as a repeatable research operation.
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**Status:** LIVE
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**Owner:** Germaine Brown / Sho'Nuff
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**Created:** 2026-08-10
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## Access
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| Resource | URL | Access |
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| Git Repo | https://git.itpropartner.com/ippadmin/competitive-landscape-research | ippadmin (read/write) |
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| AI Costs | admin-ai key: `competitive-research-prod` | TBD |
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## Purpose
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When Germaine identifies a batch of competitor/adjacent sites, this pipeline:
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1. Scrapes and deeply analyzes every site
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2. Extracts features, UI patterns, pricing models, and UX decisions
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3. Cross-references findings against ITPP products (IntelSight, HotNow, VerdictTank, TransitPin, VentureBuilt, DRE)
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4. Identifies new product opportunities from common themes
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5. Produces a structured, searchable report
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## Pipeline Architecture
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```
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Research Batch (user provides URLs)
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│
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├─ Team Alpha (3-7 sites) ──┐
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├─ Team Bravo (3-7 sites) ──┤ Parallel scraping + analysis
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└─ Team Charlie (3-7 sites)─┘
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│
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▼
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Integration & Synthesis (master report)
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│
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├─ Feature Extraction Matrix
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├─ Product Enhancement Recommendations
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├─ New Product Opportunities
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└─ Risk Flags & Cost Summary
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```
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## Research Batches
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| Batch ID | Date | Sites | Researchers | Products Analyzed | Report |
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| 001 | 2026-08-10 | 20 sites | Alpha, Bravo, Charlie | 6 products | research/001-aug10.md |
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## Methodology
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See [METHODOLOGY.md](METHODOLOGY.md) for the full research protocol.
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Key principles:
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- Every site gets a full page extraction + browser inspection
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- Features are rated on replicability and mapped to specific ITPP products
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- New product ideas must cite which sites inspired them
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- AI costs tracked per-research-batch via dedicated LiteLLM key
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