TrendGuard provides platform-neutral verification signals: structured scores, evidence, and audit trails—that commerce systems can query before recommendation, routing, or checkout.
Built for marketplaces, brands, and agentic commerce platforms.
Modular verification signals designed for machine decisioning and enterprise governance.
Explainable authenticity scoring using multi-modal analysis, metadata consistency, and provenance indicators.
Signal + evidence payloadKYB/KYC orchestration hooks, reputation patterns, and impersonation risk to reduce platform exposure.
Entity graph + risk flagsManipulation detection, purchase-proof weighting, and anomaly patterns to suppress synthetic influence.
Integrity scoringStructured flags for known regulatory and platform policy patterns (e.g., identity, disclosure, restricted claims).
Governance-readyEvent-level decision trace: inputs, signals, rationale, and references for internal review and regulator requests.
Explainability + traceabilityLow-latency endpoints returning structured trust outputs designed for autonomous commerce decisions.
API v1TrendGuard is designed to plug into existing commerce stacks and agentic workflows, supporting pre-transaction decisions, enforcement actions, and governance review.
APIs and SDKs designed for seamless integration into marketplace backends and AI-driven commerce systems. TrendGuard enables trust-scoring, verification, and enforcement directly at the transaction or listing layer.
Enterprise-grade tools for brands, compliance teams, and AI agent developers who rely on verified data to make autonomous commerce decisions and maintain regulatory compliance.
TrendGuard trust signals are delivered in a structured, machine-readable schema designed for automated decision systems, human review, and regulatory audit.
Normalized numeric scores representing confidence and risk across distinct trust dimensions.
Each signal includes an explainability payload suitable for agent reasoning and human inspection.
Signals are enriched with governance metadata to support enforcement, review, and compliance workflows.
This schema allows platforms to separate trust assessment from enforcement logic while preserving transparency, accountability, and composability.
Structured outputs that both humans and AI agents can act on instantly.
Designed for low-latency decisioning with clearly defined performance targets as the network scales.
Everything needed to integrate quickly and safely.
Operational targets for enterprise evaluation and planning:
Metrics shown reflect design targets and roadmap objectives, not production guarantees.
An example of how TrendGuard trust signals are structured to support automated decisioning, governance review, and regulatory audit. This example is illustrative and does not represent a finalized API contract.
The structure below demonstrates how trust assessments, evidence references, and governance metadata are packaged into a single, decision-grade signal.
/*
PSEUDO-STRUCTURE (ILLUSTRATIVE)
This example is non-binding and does not represent a finalized API contract.
*/
TrustSignal {
subject: {
type: "listing | seller | product | review | transaction",
reference: "partner-defined identifier (e.g., listing_id)",
context: "marketplace | region | category"
},
assessments: [
{
domain: "authenticity | identity | review_integrity | compliance",
outcome: "pass | flag | fail | unknown",
confidence: "low | medium | high",
risk_tier: "low | medium | high",
rationale: [
"brief, human-readable reasons suitable for agent summaries"
]
}
],
evidence: [
{
class: "metadata | image | provenance | behavioral | network",
note: "summarized or redacted evidence reference (no raw PII by default)"
}
],
policy: {
recommended_action: "allow | warn | hold | require_review | block",
notes: "partner policy mapping and governance alignment (optional)"
},
audit: {
trace_reference: "tamper-evident reference for accountability (conceptual)",
retention_guidance: "data-minimization and retention guidance (conceptual)"
}
}
Field names, scoring models, and enforcement behavior are partner-specific and expected to evolve through pilot integrations.
Trust infrastructure only works when outputs are consistent, explainable, and reviewable. TrendGuard is designed as a repeatable lifecycle from ingestion to decision trace.
Listing, seller, and content inputs are normalized into a consistent evaluation schema.
Signals are computed and packaged with rationale and evidence references suitable for humans and agents.
Partners use outputs for decisions, enforcement, and governance review—supported by traceability.
Request enterprise access to review signal taxonomy, integration patterns, and governance requirements.
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