Trust cannot be declared — it must be enforced mechanically. When you force real payment provider connections, verification becomes mechanical and cannot be faked.
You cannot pretend to have Stripe revenue. You cannot forge Lemon Squeezy subscriber data. When verification connection is mandatory, data authenticity is embedded in the system.
When This Works
- Your platform involves buyers evaluating sellers or projects
- Data quality determines whether users trust your system
- You’re willing to raise the barrier to entry rather than letting anyone self-declare data
When This Doesn’t
- Verification cost is too high and users won’t complete the connection
- Data sources aren’t rich enough to make verification valuable
- You’re building a trust claim rather than a trust system
Key Levers
- Mandatory connections — No manual data entry. All data must come from API connections.
- Transparent mechanics — Explain what data you access, how often it syncs, and what the limitations are. Don’t pretend data is perfect.
- Disclose limitations — TrustMRR discloses a possible 30% deviation. That transparency actually increases trust.
- Self-selection — Only founders who genuinely believe in their data will complete the connection.
Execution Sequence
- Define the trust promise — What does your platform claim to verify? What data gets verified?
- Select data sources — Which payment providers are most relevant to your audience? Prioritize those with mature APIs.
- Design the connection flow — How do users connect accounts? What permission level is needed?
- Build the sync mechanism — How often does data refresh? How do you handle discrepancies?
- Public documentation — Explain the verification mechanism, limitations, and access requirements in a public FAQ.
Supported Payment Providers
Base combination: Stripe + Lemon Squeezy + Polar (covers most indie hacker audiences)
Expansion candidates: RevenueCat (mobile apps), Paddle (developer SaaS), Creem (creator economy)
Risks
- API changes — Payment providers may change API access permissions
- Data latency — Gap between real-time data and periodic syncs
- Privacy sensitivity — Even aggregated data makes some users nervous about privacy