The Problem We Described. The Answer We Built.
In our first post, we made the case that agentic SOC platforms are struggling with a problem nobody wants to talk about. They're fast. They're getting smarter. And they're making autonomous decisions about threats they fundamentally don't understand, because the external intelligence they need to make those decisions well simply isn't there.
That post described the problem. This one introduces what we've been building to solve it.
What Elliot Is
Elliot is VisionHeight's AI-powered adversary analyst. Think of it as the colleague who always has the answer when you need to understand what a piece of infrastructure actually is, who's behind it, and whether you should care.
We built Elliot for anyone who touches adversary infrastructure in their workflow. Whether you're a threat hunter running a complex investigation, an analyst trying to enrich a SIEM alert, or someone who just needs a straight answer about an IP address or a domain without opening six browser tabs and still walking away confused.
That last part matters more than people realize. Today, if you want to understand a suspicious IP, you bounce between half a dozen tools and open-source lookups. Each one gives you a different slice of the picture. Ten minutes later, you're still not sure what you're looking at. Is this a scanner? A proxy? Part of something bigger? You get fragments. You don't get an answer.
Elliot gives you the answer. In one conversation.
What That Looks Like
You give Elliot a suspicious IP. You don't get a score. You don't get a color-coded badge and a link to read more. You get a story.
This IP is part of a seven-node cluster, registered over the past two weeks across two bulletproof hosting providers. The nodes share TLS certificate patterns. Three of them have been probing financial services infrastructure. The cluster is in its build-out phase. Here's the evidence. Here's what it means for you.
That's the difference. Not data. Not a dashboard full of numbers someone still has to interpret. A clear, simple narrative about what you're looking at and what you need to know, built from intelligence that exists nowhere else.
You paste in a domain. Elliot doesn't hand you a WHOIS record and wish you luck. It tells you the story of that domain: when it was registered, how its DNS behavior has changed, which infrastructure it connects to, whether it looks like a freshly staged campaign domain or something that's been parked and harmless for years. The answer. Not the raw material to go find the answer yourself.
You hand Elliot a set of IOCs your team pulled during an incident. It doesn't list them back to you with reputation scores attached. It connects them. Maps the clusters they belong to. Traces the hosting relationships and certificate chains. Assembles the picture your team would need days to build manually, and explains it in language you can hand directly to leadership.
Every interaction follows the same principle. You ask a question. Elliot tells you what's actually going on. No query syntax. No pivoting between six tools. No spending twenty minutes assembling fragments into something that still feels incomplete.
Just the story. Clear, sourced, and actionable.
And once your team sees what Elliot can do in a conversation, the natural next question is always the same: what happens when this runs inside our existing security stack? What happens when our SIEM, our SOAR, our agentic workflows can query this same intelligence programmatically, at machine speed, without a human in the loop?
That's where this is going. And the teams who get in early will shape how it gets there.
The Intelligence Underneath
This is the part that's hard to replicate.
Every AI assistant is only as good as what it has access to. Most security AI tools reason over the data you already own: your logs, your alerts, your internal telemetry. Or they call out to the same recycled threat feeds everyone else uses. The intelligence is shallow because the sources are shared.
Elliot is built on something fundamentally different.
VisionHeight operates a proprietary global sensor network that we designed, deployed, and maintain ourselves. These systems continuously scan, map, and classify internet-facing infrastructure from vantage points across datacenters worldwide. This is primary-source collection. Original intelligence gathered from infrastructure we built over years, producing data that simply doesn't exist anywhere else.
That raw telemetry flows through classification engines we developed in-house, where it gets enriched, correlated, and analyzed. The output is a continuously updated intelligence layer: adversary infrastructure graphs, proxy classifications, hosting profiles, risk assessments, campaign-level pattern detection. All proprietary. All live.
When you ask Elliot a question, it's reasoning over this intelligence in real time. The same data that powers VisionHeight's Adversary Infrastructure Risk Platform already running inside enterprise security stacks. Elliot doesn't guess. It doesn't hallucinate infrastructure relationships. It knows, because the underlying intelligence was built to answer exactly these questions.
We spent years building what's underneath before we built what's on top. That order matters.
Why Practitioners First
The temptation in this market is to go straight to full autonomy. Build the agent that replaces the human. Pitch the 10x efficiency number. Sell the dream.
We've watched that story play out before. SOAR promised end-to-end automation a decade ago. It still requires extensive human engineering to work in production. The autonomous SOC will follow the same path if the intelligence foundation underneath it isn't solid.
So we made a deliberate choice. Elliot today is a force multiplier for the humans doing the work. Whether that's a senior threat hunter mapping a campaign or a junior analyst who just needs to know if an IP is worth escalating. It meets you where you are.
Elliot tomorrow is the intelligence layer inside autonomous security operations. Same data. Same reasoning. Just operating at machine speed within workflows that earned the right to run autonomously because they were built on intelligence worth trusting.
You don't skip the trust-building phase. You earn it one accurate investigation at a time.