Compliance-first enforcement platform versus supplement-native competitive intelligence with human-approved policy workflow.
TrackStreet is a compliance-first platform built around MAP enforcement, with broader market-intelligence modules. It automates violation detection, evidence collection, and enforcement workflows. MarginSonar combines competitive intelligence with a governance-first policy workflow: a deterministic 6-step policy engine, 5 notice lanes with configurable approval chains, cure clocks, customer-owned notice templates, an authorized seller ledger with identity resolution, and a consistency dashboard that flags when similar cases receive different treatment. MarginSonar includes four-fence AI guardrails with audit logging, requires human approval before every notice, and does not provide legal advice or fully automated takedowns.
MarginSonar provides a deterministic 6-step policy engine that evaluates coverage, evidence sufficiency, seller classification, advertised price, policy comparison, and outcome routing. Cases are assigned to one of 5 notice lanes. Draft notices use customer-owned templates with placeholder rendering, pass through configurable approval chains, and require explicit human approval before sending. Cure clocks track business-day deadlines, and a consistency dashboard flags when similar cases received different treatment. MarginSonar does not provide legal advice and is not a fully automated enforcement platform.
TrackStreet is compliance-first, with its core strength in automated MAP enforcement and seller management. It also offers market-intelligence and competitor-intelligence modules, but its primary design is around policy compliance rather than the promo monitoring, stock tracking, category-pattern detection, and weekly executive summaries that MarginSonar focuses on.
Yes. Some brands use an automated enforcement platform like TrackStreet for fully automated MAP compliance and MarginSonar for competitive intelligence, policy-driven review with human approval, and structured evidence packets. The two solve different problems and can complement each other.
MarginSonar believes policy compliance decisions benefit from human judgment. The platform provides structured evidence from public sources, an authorized seller ledger, and customer-approved notice templates — but every notice requires explicit human approval. This governance-first approach avoids automated legal conclusions and keeps your team in control of every action taken.
The consistency dashboard answers the question: "For cases with the same facts, did we respond the same way?" It computes an 8-dimension consistency key for each case (policy version, seller status, channel, surface, price gap bucket, evidence quality, offense history, and exception state), groups cases into cohorts, computes expected outcomes based on historical patterns, and surfaces outliers where the actual response differed from the expected response. Variances can be documented with notes explaining the rationale.
MarginSonar uses a four-fence safety system. The Data Fence classifies all data by sensitivity and restricts what each AI call can access. The Inference Fence requires every AI call to declare a purpose and rejects unrecognized purposes. The Language Fence scans AI outputs against a blocked-vocabulary list to prevent legal conclusions or enforcement strategy language. The Action Fence requires human approval before any notice is sent. Every AI call is audit-logged with SHA-256 hashes of inputs and outputs.
Last updated: 2026-04-02
This comparison is based on publicly available information from each vendor's website. If anything is inaccurate, please let us know.
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