Audit Architecture
Methodology & Data
Our scoring engine processes bilingual community signals through a three-stage verification pipeline. All implementation parameters are proprietary and not disclosed publicly.
Verification Pipeline — 3 Stages
STAGE 01
Multi-Modal Sentiment Audit
Raw community text is ingested across forums, social channels, and native-language review ecosystems. Sentiment vectors are constructed per-language before cross-lingual normalization. Duplicate and near-duplicate submissions are collapsed at ingestion.
STAGE 02
Anti-Manipulation Heat Wash
Statistical anomaly detection isolates coordinated voting bursts, bot-pattern velocity spikes, and publisher-adjacent manipulation signals. Flagged data enters a quarantine buffer pending secondary re-audit before any score contribution is applied.
STAGE 03
Bilingual Feature Mapping
Verified signals from EN and ES corpora are unified into a single canonical semantic score. The normalization model is retrained quarterly against validated ground-truth datasets to correct for language-population bias.
Integrity Principles
Zero Publisher Access
Ranking pipelines are network-isolated. No external vendor API can influence computed scores at any stage.
Quarterly Blind Audits
An independent statistical review checks for model drift, score inflation, and demographic skew every 90 days.
Immutable Score Ledger
Score history is append-only. Retroactive manipulation of published rankings is architecturally impossible.
CONFIDENTIALITY NOTICE: Specific ranking weights, mathematical formulas, model architectures, and training datasets are classified as proprietary commercial assets. They are not disclosed in any public documentation. Researchers may apply for academic partnership access through the editorial board.