SponsorCheck

Protect your channel and audience from scams with AI-powered verification

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Inspiration

The inspiration for SponsorCheck came from observing a growing concern within the content creator community: the trustworthiness of sponsors. High-profile cases involving sponsors like PayPal Honey and BetterHelp, which were initially perceived as reliable but later faced scrutiny or were implicated in controversies, highlighted a significant problem. Content creators often lack the resources or expertise to thoroughly vet potential sponsors, leaving them and their audiences vulnerable to scams, misleading partnerships, or association with unethical brands. I wanted to build a tool that empowers creators to make informed decisions, protecting their reputation and their audience's trust.

What it does

SponsorCheck is a platform designed to help content creators verify the legitimacy and trustworthiness of potential sponsors. It provides tools for:

  • Performing risk analysis on sponsor companies.
  • Checking for compliance with relevant regulations (e.g., FTC guidelines).
  • Reviewing historical data and community feedback about sponsors.
  • Generating verification reports to aid in decision-making. The goal is to provide a clear, data-driven assessment of a sponsor's suitability before a creator enters into a partnership.

How we built it

SponsorCheck is a modern web application. The initial design, web app, and core features were developed using *bolt.new which generated the following technologies:

  • Frontend:
    • Framework/Library: React with TypeScript
    • Build Tool: Vite
    • Styling: Tailwind CSS for a utility-first approach to design, with custom CSS for theming and specific component styles.
    • UI Components: Custom-built React components, utilizing libraries like Lucide React for icons.
  • Backend & Database:
    • Platform: Supabase (used for authentication, database, and serverless functions)
  • Payment Integration:
    • Service: Dodo Payments (integrated via Supabase Edge Functions for creating payment links and handling webhooks)
  • Languages:
    • TypeScript (primarily for frontend and Supabase functions)
    • SQL (for Supabase database interactions)
  • Cloud Services:
    • Supabase (for backend-as-a-service, directly through bolt.new)
    • Netlify (for hosting, directly through bolt.new)
  • Data Acquisition & AI:
    • Web Scraping: Firecrawl API (for gathering information from various web sources)
    • AI Language Model: Perplexity AI API (for processing and analyzing textual data)

The application features a component-based architecture for the frontend, with services dedicated to API interactions (Supabase, Dodo Payments), and state management handled within React components and contexts. Supabase Edge Functions are used for secure backend operations like payment link creation.

Challenges we ran into

  • Data Aggregation and Analysis: Designing a system to effectively gather, process, and present verification data in an understandable and actionable format for creators was a complex task. This involved defining what data points are most critical for a "trustworthiness" assessment.
  • User Experience for a Niche Problem: Creating an intuitive user interface that simplifies the potentially complex process of sponsor verification and presents information clearly was a key challenge. We aimed to make it accessible even for creators not deeply familiar with due diligence processes.
  • Ensuring Data Accuracy and Objectivity: Sourcing reliable data for sponsor verification and presenting it without bias is an ongoing challenge. We focused on transparent data sources and clear reporting.

Accomplishments that we're proud of

  • Addressing a Real-World Problem: Developing a tool that directly tackles a significant pain point for the content creator community, aiming to foster more transparency and trust in sponsor partnerships.

What we learned

  • Deep Dive into the Creator Economy: Gained a much deeper understanding of the challenges content creators face, particularly regarding sponsorships and brand partnerships.
  • Bolt prompt Development: The project evolved through iterations, learning from challenges and refining features one prompt at a time.

What's next for SponsorCheck

  • Advanced AI Analysis: Incorporate more sophisticated AI/ML models for deeper analysis of sponsor sentiment, contract terms, and potential red flags.
  • Broader Data Sources: Integrate with more data sources to provide even more comprehensive verification reports.
  • Community Features: Allow creators to share anonymized experiences or ratings about sponsors (with appropriate moderation and privacy controls).

Built With

  • bolt.new
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