AI-native analysis that understands what code means—
revealing threat DNA, family lineage, and campaign relationships
the instant you scan.
Every binary has DNA. We decode it.
For decades, the industry has been forced to choose between speed, depth, and trust—a fundamental compromise. This is the broken reality.
SemanticsAV analyzes the architectural DNA of code—what it fundamentally is at the design level. Not what it superficially appears to be through syntax. Not what it does in a controlled sandbox. We understand the intrinsic semantic blueprint that defines purpose and capability, regardless of obfuscation or evasion tactics.
This is not feature engineering. This is end-to-end AI learning directly from structural and relational properties that persist through any transformation.
We resolve the explainability crisis by inverting the paradigm. Instead of asking you to trust an opaque model, our AI produces objective, verifiable evidence that explains its verdict. You see a genetic comparison matrix showing your sample's DNA against its closest relatives from our intelligence database.
The analyst is liberated from fighting the black box. You're presented with data—similarity scores, family attributions, architectural matches. The trust is placed in verifiable facts, not inscrutable algorithms.
Real intelligence reports demonstrating genetic analysis and threat attribution
Heavy obfuscation, non-standard packing, and anti-analysis structures create unique high-dimensional patterns. For semantic AI trained on millions of samples, these aren't obstacles—they're distinctive genetic markers that correlate strongly with malicious intent. The very act of hiding becomes a signal.
The inscrutability of AI, once a liability for defenders, is now repurposed as a weapon against attackers. The analyst receives deterministic, data-driven outputs they can verify and act upon. Meanwhile, the attacker faces an unpredictable opponent whose decision logic cannot be reverse-engineered or gamed. They're now the ones fighting the black box.
Every novel attack analyzed becomes training data. The system learns not just from a single sample, but generalizes the underlying architectural patterns. A simple syntactic modification is no longer sufficient. To evade detection, an attacker must now create a fundamentally new semantic architecture—a far more difficult and expensive endeavor, creating exponentially rising costs.
Ultra-lightweight AI engine delivering production-grade verdicts without network dependency. Complete analysis happens on-device—no cloud required during scanning.
Open-source interface handling all network communication. Audit exactly what data leaves your system—architectural proof of privacy, not promises.
Optional real-time threat attribution and genetic positioning. Transforms verdicts into actionable forensics—family identification, campaign attribution, similarity clustering.
Packing (Themida, UPX, custom) and encryption become evidence rather than obstacles—semantic patterns persist through any transformation.
Understand where samples cluster in malware landscape. Identify family relationships, campaign attribution, and evolutionary patterns.
No signature database required. AI-native understanding detects threats never seen before based on semantic characteristics.
Same file always produces identical analysis payload. Enables independent verification of privacy guarantees through open-source code.
Full detection capability with zero network dependency. Intelligence API completely optional—offline operation is first-class.
Daemon mode, REST/Unix socket APIs, multi-format output (JSON, HTML, Markdown). Built for enterprise integration.
The SDK remains free for all commercial uses on Linux.
Intelligence API tiers scale from personal research to enterprise distribution.
* Personal, academic, and research use only. Commercial service delivery requires Pro tier or higher.
** Windows, macOS, and other platforms require Enterprise licensing. Build customer-facing services that incorporate and redistribute Intelligence analysis.
Traditional enterprise-grade malware detection remains locked behind commercial licensing, creating a security divide. Well-funded organizations deploy sophisticated AI-driven defenses while open-source projects and Linux-native environments rely on decades-old signature-based approaches.
By making the SemanticsAV SDK freely available on Linux for all commercial uses, we enable security teams, researchers, and the open-source community to deploy the same AI-native detection technology that protects enterprise environments—without licensing barriers.
Ship with zero-day detection capabilities built-in, making semantic analysis a standard component of system security rather than an expensive add-on.
Integrate production-grade AI detection into security tools, research platforms, and defensive systems without concerns about licensing costs or restrictions.
Deliver AI-powered protection to underserved markets and enable new business models around advanced threat detection capabilities.
We believe security technology evolves fastest when foundational tools are accessible. Commercial licensing for cross-platform deployment and premium Intelligence services sustains innovation while keeping Linux deployments perpetually free.