An enterprise-grade verification architecture designed to identify misinformation, authenticate sources, and quantify trust across digital media ecosystems in real time.
Content passes through five specialized stages before receiving a final Trust Score and editorial recommendation.
Multi-format input parsing (text, image, video, metadata) with schema validation.
Historical credibility scoring, domain reputation analysis, and ownership tracing.
NLP sentiment mapping, linguistic fingerprinting, and generative content detection.
Flagged items route to human specialists for contextual validation and nuance checks.
Standardized score (0-100), confidence intervals, and actionable publication flags.
Modular architecture enables targeted analysis across media types and threat vectors.
Frequency-domain analysis, CNN-based artifact detection, and cryptographic provenance verification (C2PA compliant).
Vision PipelineGraph-based tracking of content amplification patterns, bot cluster identification, and coordinated inorganic behavior.
Graph AnalyticsStylometric analysis, perplexity scoring, and cross-lingual consistency checks to flag synthetic text.
NLP EngineUnified tracking across social, news, and forum ecosystems with normalized entity resolution.
Data LayerStreaming ingestion with sub-second latency alerts for breaking events and viral misinformation spikes.
Streaming CoreFull traceability logs, GDPR/CCPA alignment, and automated redaction pipelines for sensitive data.
GovernanceDecoupled microservices architecture optimized for scalability, accuracy, and editorial workflow integration.
Distributed inference cluster • Vector DB • Graph Store • Rule Evaluator
Independent audits and internal validation metrics across the last 12 months.
Sample output from the framework's CLI analysis tool.
Access the Detection Framework programmatically via REST or GraphQL. Rate-limited free tier available for journalists and researchers.