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NEXUS — AI-Powered Cyber Simulation
## Overview NEXUS helps organizations test high-stakes decisions in simulated environments before real-world deployment. ## The Problem Cybersecurity teams must balance threat containment, service availability, and limited response resources. Choosing the wrong strategy can worsen an incident or disrupt critical services. ## The Solution NEXUS combines a deterministic simulation engine with AI-assisted decision-making. The engine maintains the authoritative world state, validates actions, applies transitions, and records events. AI proposes decisions, but the simulation determines their actual consequences. ## Key Features - Compare doing nothing, scripted defense, and AI-assisted strategies. - Simulate cyber containment and active-attack scenarios. - Evaluate compromised hosts, containment, service availability, and resource consumption. - Run repeatable experiments across multiple random seeds. - Generate structured results for objective strategy comparison. ## Innovation Unlike a chatbot that merely recommends actions, NEXUS evaluates decisions through their consequences in a controlled simulation. This reveals trade-offs and weaknesses using measurable outcomes rather than AI-generated claims. ## Vision NEXUS aims to help organizations evaluate high-stakes strategies before deploying them in reality, with potential applications in infrastructure resilience, healthcare operations, and supply-chain management. **NEXUS — Simulate before you deploy.**
18 Oct 2026
Protocol Archaeologist
Every enterprise developer knows the golden rule of IT support: "If it works, don't touch it." Trillion-dollar industries run on legacy systems where the original developers left years ago, leaving behind documentation that is pure fiction. When official API docs say a parameter is "optional," but a patch from 2022 quietly made it mandatory, engineers who trust the docs end up breaking production. This fear of touching the unknown creates "bus factor zero." It stalls modernization, hides critical vulnerabilities, and costs enterprises millions in outages. We built Protocol Archaeologist to solve this. Instead of forcing engineers to manually untangle spaghetti code, our platform acts as an autonomous forensic investigator. Targeted at Site Reliability Engineers (SREs), DevOps teams, and enterprise architects, users simply connect their repository to our platform. Instead of generating a generic summary, the system hunts for contradictions. When it finds a discrepancy between the documentation and the actual system, it builds a verified case file. It highlights the exact line of source code causing the issue, pulls the live production error log proving the failure, and surfaces the exact Git commit explaining why the hidden rule was introduced. What makes this unique is that we are not building another AI chatbot that guesses how code works. We built an evidence-correlation engine. It replaces the fear of legacy code with hard, cited evidence, outputting a ground-truth API specification so engineering teams can finally modernize their systems with confidence.
27 Sep 2026
