Snapshot
Status
MVP
Type
trust infrastructure
Timeframe
2025–present
What I Owned
Product
incident flows, trust snapshots, public profile structure
System Design
behavioral signals, pattern detection, evidence logic
UX
trust/risk visualization, incident pages, response states
GTM
creator-led reporting wedge, grievance/resolution positioning
Narrative
behavioral trust infrastructure for humans and AI agents
Problem
Trust infrastructure exists for companies, products, and platforms, but not for human behavior across high-stakes interactions. Across hiring, lending, and partnerships, the strongest signal is usually missing, unverifiable, or siloed.
Journey
Started from repeated first-hand experiences where trust broke across hiring, lending, and collaborations, without any reliable way to reference past behavior.
Validation came from consistent stories across offline interactions, recurring patterns across communities, and strong signals from platforms like Reddit where people expressed similar pain points.
Mapped how trust currently works: LinkedIn for identity, references for selective narratives, and reviews that are platform-bound and biased. None of these capture consistent behavioral patterns over time.
Shifted the core lens from identity to behavior.
Early exploration focused on structuring incidents, extracting signals, and enabling pattern recognition.
Built an early demo system to test how this interaction model could feel and function in practice.
Designed TrustCircle as a behavior registry, not a review platform: incidents as structured inputs, corroboration for signal strength, and summaries as neutral interpretation.
Iterated on core constraints: how to maintain neutrality, prevent misuse or false reporting, and balance privacy with accountability.
The system evolved into a coordination layer for trust where decisions are informed by patterns rather than isolated opinions.
Wins & Failures
Wins
✓Clear reframing: trust as behavior, not reputation.
✓Strong validation through real-world experiences and community signals.
✓Built an early demo to test interaction and system design.
✓Resonance across high-stakes use cases such as hiring, lending, and partnerships.
Failures
○Early perception risk: seen as a review or complaint platform.
○Cold-start problem: no initial data layer to generate signals.
○Trust density is hard to bootstrap without a focused wedge and strong reporting incentives.
○Complex trade-offs between neutrality, privacy, and usability.
Where it stands
Early-stage system with strong thesis and an initial prototype.
What it needs:
- •Real-world incident data to validate and refine signal structures.
- •Early users willing to contribute and test reporting flows.
- •Tighter design of evidence, corroboration, and dispute layers.
- •A clear wedge use case to drive initial adoption.
Collaboration
Open to collaborating with builders working on trust, identity, or coordination systems, early users willing to test reporting and validation flows, researchers exploring behavioral data systems, and platforms interested in embedding trust signals into decision-making.
Reach out →