Spellbreaker is a deepfake detection platform that identifies AI-generated, manipulated, and synthetic media across video, audio, and images. It's built for organizations where a wrong call on media authenticity carries real consequences — courts and legal teams, insurance claims and SIU units, financial institutions, and enterprise security and trust & safety teams.
Unlike single-model detectors that degrade on real-world content, Spellbreaker uses an ensemble of multimodal AI models combined with neuro-symbolic reasoning, cross-referencing signals between modalities to catch inconsistencies in compressed, degraded, or low-quality files — the conditions where deepfakes actually circulate.
Every analysis goes beyond a binary real/fake verdict. Spellbreaker generates explainable forensic reports that pinpoint where manipulation occurs and document how each conclusion was reached, giving investigators, claims adjusters, and analysts defensible findings they can present in court, regulatory reviews, or internal investigations.
Security and privacy are built into the architecture: files are analyzed statelessly in memory and purged immediately after processing, with no retention. Your systems remain the authoritative record.
Key capabilities:
Multimodal detection across video, audio, and image files
Cross-modal analysis that surfaces multi-layered manipulations single-modality tools miss
Explainable forensic reports with traceable reasoning, suitable for legal and investigative use
Robust performance on compressed and degraded real-world media
Continuously updated detection ensemble to keep pace with new generative techniques
Stateless, zero-retention processing for sensitive workflows
Flexible deployment: cloud API, analyst web portal, or on-premise/air-gapped for data residency requirements
Common use cases: digital evidence verification, insurance claims fraud investigation, threat intelligence and disinformation analysis, executive impersonation investigations, and enterprise incident response.
