The reasoning engine above your tools
Scanners produce findings. Antrixis produces understanding — correlating evidence across the graph into ranked hypotheses, then narrating each one in plain language.
- LLM reasoning grounded in observed evidence
- A guardrail drops any path it can’t cite
- Posture, business impact, and choke points in one view
cites 3 findings · 4 hops · grounded
cites 2 findings · 2 hops · grounded
cites 2 findings · needs review
cites 1 finding · 3 hops · grounded
AI that hallucinates exploits is worse than no AI
An assistant that invents a plausible-sounding attack path you can’t reproduce destroys trust on the first false positive. Intelligence is only useful if every claim is anchored to something the platform actually observed.
Reasoning you can trust
Agents correlate, hypothesize, and prioritize — over a deterministic, grounded substrate.
Cross-graph correlation
Findings, assets, technologies, and relationships are connected into coherent attack stories.
Attack hypotheses
The planner proposes how weaknesses chain together, ranked by likelihood and impact.
Grounding guardrail
Any path that can’t cite real evidence is dropped before it reaches you — and the count is shown.
Threat-intel fusion
EPSS and CISA KEV fold exploitation likelihood directly into the ranking.
Plain-language narratives
Every path reads like a sentence a human would write, not a CVE identifier.
Pluggable reasoners
Bring your own model. The deterministic template reasoner runs with no LLM configured at all.
The Attack Planner, step by step
An agent doesn't just emit a path — it hypothesizes, gathers evidence, scores plausibility, and critiques its own work. Anything it can't ground in observed evidence is dropped before it reaches you.
Intelligence, not another inbox
Get ranked hypotheses with the evidence behind every claim — and a guardrail you can audit.