What does AI say about you —
and your competitors?
Five AI models score how you and up to three rivals show up in AI answers. 15 seconds. One scoreboard. The gap is your roadmap.
Beta closing soon Free while in beta. No login. No credit card.
Three ways to see what you're missing
AIPM Audit
What do GPT-4o, Claude, Gemini, Mistral, and DeepSeek say about your brand? Score, compare, fix. Free.
Audit Now →STIX Feed
1M+ indicators of compromise. Splunk ES, OPNsense, TAXII compatible. Real-time threat intelligence. From $45/mo.
View Pricing →Edge Shield
Cloudflare Worker. Blocks threats, trolls scanners, enriches every request with city-level geo. Deploy in 30 seconds. Open source.
GitHub →SEO is dead. Welcome to GEO.
Harvard Business Review calls it Generative Engine Optimization. We call it AI Presence Management. The shift is the same: AI models are replacing search engines as the front door to the internet.
The Old World: SEO
You optimized for Google's crawler. Keywords, backlinks, meta tags. Google ranked pages. Users clicked links. You controlled the narrative through your own website.
The New World: AI Answers
Users ask ChatGPT "What laptop should I buy?" and get an answer — synthesized from training data, third-party reviews, and whatever the model scraped. Your website might not even factor in. Perplexity cited a 1.6-star Sitejabber review to describe Dell. Not Dell's own content. Sitejabber.
The Gap
Most companies have no idea what AI models say about them. No way to measure it. No way to improve it. The models form opinions from whatever they find — and whoever controls that narrative controls your brand.
AIPM: The Fix
We query the models directly. We score your technical structure. We show you exactly what they think, why they think it, and what you need to change. Audit, optimize, monitor, correct.
Five steps to AI visibility
Submit a domain. We do the rest.
Query AI Models
We ask GPT-4o, Claude, Gemini, Mistral, and DeepSeek five questions about your brand: awareness, accuracy, sentiment, recommendation, and NPS confidence.
Score Structure
We crawl your domain and score seven technical factors: robots.txt AI permissions, LD-JSON schema, semantic HTML, sitemap, meta tags, llms.txt, and NLWeb protocol.
Scan for Contamination
We check every surface AI models read — robots.txt, JSON-LD, HTML comments, llms.txt, NLWeb — for hidden prompt injection payloads. 30+ attack signatures. If your site has been weaponized against AI, we find it.
Detect Threats
We cross-reference your brand against our threat intelligence database — 1M+ indicators — to find impersonation, typosquatting, and reputation attacks.
Generate Fixes
Prioritized recommendations with exact code snippets. What to add to robots.txt. What LD-JSON schema to implement. What contamination to remove. What the models need to hear.
Two dimensions. One score.
Combined Score = (AI Perception × 0.6) + (Structure × 0.4), capped at 95. We guarantee 5% bullshit exists.
Does your robots.txt allow AI crawlers? GPTBot, ClaudeBot, PerplexityBot, Google-Extended — blocking them means the models can't learn from your content.
Structured data that tells AI models exactly what your organization is, what you do, and how to describe you. The difference between "some company" and a precise identity.
Proper heading hierarchy, ARIA landmarks, and semantic elements. AI models parse structure — a well-organized page is easier to understand than a div soup.
An XML sitemap helps every crawler — traditional and AI — discover your content. Dell and NVIDIA don't have one. You should.
OpenGraph, Twitter Cards, and description meta tags. These are the first thing AI models read when they visit your page.
Who else does this — and where the gap is
"Who are your competitors?" is a fair question. Here's the honest map — and the one thing none of them do.
AI-visibility monitors
Profound, Peec AI, and Scrunch AI track how models mention your brand and chart sentiment over time. Real category, real tools. What they don't carry: a live threat-intelligence backend, a contamination scanner that reads the surfaces AI crawlers actually parse, or a security posture behind the score. They watch. We watch and defend.
The pipeline incumbents — Cribl
Cribl is the loudest of the telemetry-routing vendors, priced by the gigabyte of hay it shovels. It's the analogy people reach for — "a Cribl for AI presence" — so we ran our own five-model audit on Cribl itself. See exactly where a category incumbent wins and loses in AI answers.
See the Cribl audit →Run yourself against them
The audit tool compares up to three domains side by side — you, Cribl, and anyone else you name — across all five models, live, in about 15 seconds. No hardcoded scoreboard to distrust: you run it, you read the gap. That gap is the roadmap nobody else is measuring.
Run a side-by-side →Why the incumbents can't just do this
The fair follow-up: what stops CrowdStrike or Microsoft Defender from coming downmarket and doing it cheaper? The answer is structural, not promotional.
Cost asymmetry
Our per-detection cost sits below the incumbents' per-attack cost — $0.0056 to publish an indicator, 416× more efficient than Shadowserver on a per-operational-dollar basis. An incumbent coming downmarket has to defend a cost structure built for six-figure contracts. We don't. That gap doesn't close by discounting; it's baked into the operating model.
AI-native, human-reviewed
"You depend on Claude" reads as a weakness only if you assume the alternative is cheaper. It isn't. We're AI-native from the metal: every published call is human-reviewed, and the pipeline is instrumented to catch its own mistakes. That's not a dependency — it's the reason two people can run a Tier-2 CERT's worth of output.
One expert × AI execution
The operating model is one expert's judgment multiplied by AI execution. That is the scale answer, not a weakness to hide — 25+ years of infrastructure judgment deciding what matters, AI doing the volume. It's why the novelty is real (~75% of our indicators aren't in ThreatFox) and why nobody replicates it by hiring.
Everything you need for AI visibility
From one-time audits to continuous monitoring.
Multi-Model Querying
GPT-4o, Claude, Gemini, Mistral, and DeepSeek queried in parallel. Five dimensions scored per model: awareness, accuracy, sentiment, recommendation, and NPS.
Structure Analysis
Full crawl of robots.txt, LD-JSON, semantic HTML, sitemap, and meta tags. Scored against what AI crawlers actually need to understand your brand.
Brand Threat Detection
Cross-referenced against 1M+ threat indicators. Find impersonation domains, typosquats, and reputation attacks before the AI models learn from them.
AI Contamination Detection
Scans robots.txt, JSON-LD, HTML comments, llms.txt, and NLWeb for prompt injection payloads hidden in your AI-facing content. 30+ attack signatures including CamoLeak-style exfiltration, role hijacking, and SEO poisoning for AI. No one else does this.
AIPM-NPS
Net Promoter Score calculated from AI model recommendation confidence. Would GPT-4o recommend you? Would Perplexity? Now you know.
Fix Generation
Prioritized recommendations with exact code. robots.txt rules, LD-JSON templates, semantic HTML fixes. Copy, paste, improve.
Audit History
Every audit persisted to Azure Table Storage. Track your score over time. See the before and after. Prove ROI to your board.
McKinsey scored 35/95 on AIPM. Two weeks later, an AI agent breached their internal platform in 2 hours.
What's your score?
Free. No login. 15 seconds.