Research Station

Research Station is the factory's opportunity discovery module — a specialized agent system that continuously scans Reddit and the App Store for product opportunities, scores potential niches against viability criteria, and feeds validated opportunities into the factory pipeline. Where Design Station handles visual execution, Research Station handles strategic input: what should we build, and why is there demand?

The module emerged from recognizing that the factory's bottleneck wasn't building — it was finding good ideas to build. A system that can produce iOS apps quickly is only as valuable as the opportunities it pursues. Research Station solves the opportunity identification problem by automating the market research function: scraping Reddit for unmet needs, analyzing App Store competitor density, and scoring opportunities on a consistent rubric.

The Research Station operates by running multiple scraping and analysis agents in parallel. Reddit agents scan relevant subreddits for posts expressing frustrations, unmet needs, feature requests, and complaints about existing solutions. App Store agents analyze top charts, recent releases, and review sentiment for apps in candidate categories. The output is a structured opportunity report with a score: does this opportunity clear the discovery gate (see discovery-gate)?

The scoring rubric is derived from factory learnings — accumulated intelligence about what makes an iOS app opportunity viable. Factors include: market saturation (red ocean vs blue ocean), category seasonality (some niches spike during certain times of year), competitive weakness (apps with poor ratings, outdated UI, or missing features represent opportunity), and demand signals (Reddit posts with high engagement, repeated complaints, no good existing solution).

Research Station also runs competitive analysis on apps that are already solving the identified problem. This analysis identifies where existing solutions fall short — missing features, poor UI, performance issues, subscription fatigue — so the factory can build a differentiated offering rather than a me-too product. The factory learns from its own competitive analysis: each app built adds data to the scoring model about what works and what doesn't.

The output of Research Station feeds directly into the factory pipeline's DISCOVER phase. When Research Station identifies an opportunity clearing the discovery gate, the Router routes it to the next stage (VALIDATE, then DESIGN). This closes the loop between market research and product execution — the factory doesn't just build whatever is requested, it actively searches for opportunities and validates them before committing build resources.

The strategic value of an automated Research Station is that it makes the factory's output predictable and directional. Rather than waiting for a human to identify an opportunity, the factory is continuously scanning, scoring, and queuing opportunities. This creates a pipeline of validated ideas ready to be built, so when build capacity is available there's no delay from research — the opportunity is already vetted and scored.

  • discovery-gate — the 28/40 gate that Research Station opportunity scores must clear
  • ios-factory-pipeline — where Research Station output enters the factory pipeline
  • design-station — the sister module that executes design for Research Station's opportunities
  • sub-agent-parallelism — the parallel execution pattern Research Station uses for scraping