Understand the exposure
Connect vulnerabilities to asset context and business impact so teams can focus their attention where it matters.
INDUSTRY · Cybersecurity
Give your security team the power to act.
We build the AI systems that turn vulnerability detection into bulk remediation — multimodal LLM agents, fast-track execution, and zero-impact guardrails for OT/IoT environments.
Scoped permissionsEvaluated playbooksHuman oversight
6 billion
Asset diversity Armis platform handles (the scale problem we built for)
40-day MVP
From assessment to a defined Kubernetes-orchestrated MVP plan (Armis)
Multimodal
Agents that read screenshots + DOM to learn device-specific workflows without pre-programmed recipes
From another alert to a clear next action.
Learn a safe response
Use agents to research device workflows and prepare tested playbooks, with checks and approvals before execution.
Remediate at scale
Run validated workflows repeatedly, monitor the result and retain rollback paths for critical environments.
The people behind the work
Carlos Leite
AI for cybersecurityBuild with someone who understands both the opportunity and the responsibility. Carlos helps security teams turn AI into useful workflows, with careful evaluation, clear permissions and people in control of critical decisions.
Experience in AI for cybersecurity at Microsoft and AI at Meta.
Challenge → Solution
What's broken, and what we do about it.
This is your problem
This is our solution
Problem
Your platform identifies millions of vulnerabilities, but remediation is manual and unscalable — incumbent tools rely on slow, device-specific "recipes."
Our solution
A "Learn-and-Use" agentic architecture: a Researcher Agent uses multimodal LLMs and browser automation to create validated playbooks once, then a "Fast-Track" engine executes in bulk. (Armis)
Problem
Frontier models are too expensive to run on every action.
Our solution
Dual-path execution: expensive LLM agents learn the flow once, then efficient non-LLM Playwright scripts execute the task at scale. Costs collapse without sacrificing accuracy. (Armis)
Problem
Your AI agent lives in the cloud but needs to control devices on private IP ranges (192.168.x.x).
Our solution
Integration with TCP brokers as transparent forwarders to reach non-routable on-prem assets. (Armis Broker pattern)
Problem
One bad action takes a critical OT device offline. The system can’t crash the device, ever.
Our solution
"Zero-Impact" guardrails: pre-flight connectivity checks, "Page Looks Good" UI validation, hard rollback paths. (Armis)
Practice Areas
What we build for security teams.
Agentic systems that turn the "scale problem" of vulnerability remediation into something a small team can actually own.
Agentic Remediation OS
The pattern we proved with Armis: autonomous agents that research devices once, then execute remediation playbooks in bulk — safely, cheaply, and at scale.
Agentic Remediation
Researcher + Writer agents, multimodal analysis (screenshots + DOM), and validated playbook generation.
Fast-Track Execution
LLM-learned-then-script-executed pattern. Frontier models run rarely; cheap scripts run at scale.
Safe Orchestration
Kubernetes-based agent orchestration, zero-impact guardrails, rollback-first design — built for OT environments where downtime is unacceptable.
Connectivity to non-routable assets
TCP-broker integrations and forwarder patterns to reach on-prem and air-gapped devices from the cloud.
Engagements
How we deliver in security environments.
Anchor reference: the Armis pattern — 1-month fixed-price Assessment, then MVP squad on T&M (1 EM + 1 SME + 2 AI Engineers).
Rapid-Impact Intervention
SWAT Team
A high-impact strike team that diagnoses, architects, and ships. We bring the ML engineers, infra, and domain expertise needed to deliver measurable lift within weeks.
- End-to-end diagnostic and solution delivery
- Cross-functional team: ML engineers, infra, domain SMEs
- Measurable KPI improvement with defined timelines
Applied Research Partnership
The ML Lab
A dedicated research partnership where we co-develop proprietary models alongside your team — from initial hypothesis through production deployment.
- Joint model development and full knowledge transfer
- Custom algorithms built on your data and objectives
- Structured engagement from discovery to production scale
Risk-Free Experimentation
Simulator
A controlled experimentation environment for validating strategies before they touch production. Test against realistic system dynamics and quantify impact upfront.
- Realistic simulation with historical data replay
- A/B scenario testing for system-level decisions
- Quantified impact forecasting before production rollout
Proof
How security teams ship with us.
Ready to build?
No pitch decks, no generic demos — just a technical conversation about your data and your goals.