About

Trust for the AI Commerce Era

TrendGuard is the independent trust and verification layer for AI-mediated commerce. We provide pre-transaction decision signals that marketplaces, AI platforms, and enterprise commerce operators can query before recommendation, routing, or checkout.

Why Now

Commerce is entering an era where automated systems increasingly mediate discovery and purchase flows. As more decisions move to software, verification must operate at machine speed, with explainability and audit trails that support governance and regulatory review.

59%

Leaders say AI-based risks are outpacing their organization’s ability to govern and control them
Source: Vanta / Sepio Research, 2024

$1.1T

Annual global economic activity displaced by counterfeiting and illicit trade
Source: Corsearch, 2024

Up to 6%

Maximum global revenue penalty for systemic non-compliance under the EU Digital Services Act
Source: European Union, Digital Services Act

Defining the foundation for trusted AI commerce

Mission & Vision

Mission

Deliver independent, real-time verification signals at scale so commerce platforms and AI systems can make defensible, auditable decisions before transactions occur, supported by explainability and auditability.

Vision

A world where automated commerce decisions are governed by verifiable trust signals. TrendGuard becomes the platform-neutral trust layer that enterprises can evaluate, govern, and rely on across marketplaces, agents, and commerce surfaces.

Our Development Philosophy

TrendGuard is built by FindNext Corp as part of a B2B infrastructure ecosystem for AI-mediated commerce. We prioritize neutrality, governance readiness, and partner-driven integration to ensure trust signals can be adopted across platforms without competitive bias.

  • Neutrality by design: trust assessment separated from commerce outcomes
  • Governance first: explainability, audit trails, and policy alignment
  • Partner-driven integration: built with marketplaces and AI commerce teams
  • Enterprise readiness: integration patterns that support scale and review

Part of the FindNext Ecosystem

TrendGuard operates as the trust and verification component within the FindNext ecosystem. TrendGraph provides agent-readable demand intelligence, and FindNext.co provides validation and distribution surfaces that help partners operationalize these outputs in real environments.

  • TrendGuard: trust and verification signals for decision systems
  • TrendGraph: structured demand intelligence for forecasting and planning
  • FindNext.co: B2B distribution and validation surfaces for partner workflows
  • Composability: components can be evaluated and adopted independently

The Market Opportunity

AI-driven shopping, identity verification, and counterfeit detection are converging into a new market category: AI Commerce Trust Infrastructure. TrendGuard is positioned as a neutral standard in this emerging space.

Agentic Commerce Enablement

As agents move into shopping and purchasing workflows, platforms require external trust signals to govern automated decisions.

Trust, Risk, and Compliance Infrastructure

Verification must be explainable and auditable to support enterprise policy enforcement and regulatory scrutiny.

Platform-Neutral Verification

Independent verification enables trust assessment without relying on marketplace incentives or internal bias.

Our Strategic Advantages

Early Market Entry

Building trust infrastructure as AI commerce matures

Platform Agnostic

Enables partnerships across competing marketplaces

AI-Native Architecture

Engineered from the ground up for agent-driven decisions

Network Effects

Each verification improves the system’s precision and value

Regulatory Alignment

Built for emerging AI commerce compliance frameworks

Global Scalability

Designed to operate across borders and trust jurisdictions

Join the Trust Infrastructure Development

TrendGuard is in active development with phased enterprise access. Early access is prioritized for organizations that can evaluate integration requirements, governance expectations, and decision-signal taxonomy.