AutoIntelligence: An End-to-End Agentic Platform for Software Security Intelligence

A customized agentic AI solution that draws input from:

A large multi-source evidence model learned from historical disclosures and exploit chatter
Feedback from downstream stages (extraction confidence, deduplication/conflict outcomes, SBOM mapping success)
Provenance and source-trust signals to suppress misinformation and poisoning

A customized agentic AI solution that generates:

Adaptive discovery actions to surface high-yield weak signals earlier
Normalized, provenance-linked structured records (e.g., CVE/CWE/STIX + package/version constraints)
Deduplicated, credibility-scored outputs mapped to SBOM/dependency graphs for prioritized alerts

Fine-grained, end-to-end intelligence, from weak early signals to SBOM-mapped alerts, covering the full spectrum from benign noise to genuinely malicious or exploitable activity.
AI-driven autonomous discovery and extraction, continuously ingesting multi-source signals while automatically adjusting source focus, parsing strategy, and confidence thresholds based on feedback.
AI-driven interpretation of evidence, reconciling duplicates/contradictions and highlighting suspicious or high-impact items with transparent provenance and credibility scoring.

Enterprises / Corporates: Deep inspection of internally deployed and third‑party (outsourced) software stacks—validate SBOM exposure and prioritize fixes before production rollout.
Security vendors: Continuous “pre‑advisory” intelligence enrichment for pentesting, exposure management, and customer alerting with lower noise and faster lead time.
Government / Regulators: Support certification and assurance for software used in finance and critical infrastructure by providing provenance‑rich, auditable supply‑chain risk intelligence.
Academia: Research

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