DossierAudit (Data Portability Verifier)

Who it's for

Privacy-conscious consumers and consumer advocacy journalists who currently use generic spreadsheets or wait weeks for legal teams to explain raw JSON/CSV dumps.

The problem

Time and legal complexity. Reading raw multi-gigabyte loyalty dumps (like the 500+ page dossiers from McDonald's or Starbucks) is impossible for non-technical users due to obfuscated database keys, hidden location coordinate histories, and unmapped behavioral segment IDs.

How to build it

An offline-first web application where users drag-and-drop their exported ZIP archives, processing everything locally in the browser to maintain strict privacy, producing a structured PDF and interactive dashboard.

How it makes money

Consumer advocacy groups and independent journalists pay $29/month for the premium tier to batch-process dossiers and export white-labeled reports, while casual users pay a $10 one-time fee to unlock their personal dashboard. Free alternatives like generic JSON viewers do not translate proprietary schemas into human-readable timelines.

Why it doesn't exist yet

Incumbent privacy tools focus on requesting deletion or raw exports (like Mine or Incogni), but they do not process or translate the raw files once delivered because schemas change rapidly. An indie developer can ship and maintain specialized parsers for the top 10 retail/fast-food loyalty apps within days.

First users

The first 10 privacy journalists and power-users will discover it on Hacker News and Reddit (r/privacy) following viral investigative pieces on fast-food loyalty tracking.

Build size

1 developer x 8 weeks. Includes building local browser-based zip-parsing logic and visual timeline rendering for the top 5 global fast-food and retail loyalty exports (McDonald's, Starbucks, Target, Sephora, Walgreens). Excludes automatic legal filing systems.

Biggest risk

The major retail brands could change their schema formats, which would require updating the parsing templates; however, they cannot stop exporting the underlying data without violating CCPA/GDPR.

Conditions for a hit (all 3 required)

• Accepts a raw zip export from McDonald's or Starbucks and extracts a chronological map of coordinates and purchases in under 15 seconds.
• Produces a local dashboard that translates proprietary segment flags (e.g. churn-risk, high-frequency) into plain-English behavioral profiles.
• Generates a downloadable, encrypted PDF summary of the surveillance dossier with one-click sharing configurations.

How it's judged (in 6 months)

GitHub repository of the parsing engine achieving over 500 stars, or Product Hunt launch reaching the Top 5 Daily products.(judgment date 2027-02-14)

AI self-confidence 75/100 — self-reported likelihood of meeting the criterion, not a business success rate