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Mission Control / Domain Intelligence
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Domain Alpha
Domain Beta
▶ DEMO
Fingerprint Detection
HTML, header, script, and URL signal analysis across 200+ technology patterns.
Graph Intelligence
Co-occurrence mapping, relationship weighting, and ecosystem centrality scoring.
Cluster Classification
Ecosystem feature vectors, similarity scoring, and adjacent cluster mapping.
Security Analysis
CSP, HSTS, WAF detection, risk scoring, and anomaly classification.
Adaptive Learning
Confidence evolution, drift detection, and temporal pattern reinforcement.
AI Interpretation
Deterministic stack analysis, maturity inference, and architecture classification.

Website Technology Intelligence

Enter any domain to run a full-spectrum stack analysis across 30+ detection dimensions.

Initializing intelligence engine...
Resolving DNS infrastructure
Acquiring HTTP signals
Fingerprinting technology stack
Computing graph relationships
Classifying ecosystem cluster
Running AI interpretation layer
Finalizing intelligence output
ERR_UNKNOWN
Intelligence Engine Error
An unexpected error occurred.
TARGET
LATENCY
CACHE
TECHNOLOGIES
TIMESTAMP
Technologies
detected
Architecture
classification
Infrastructure
provider
Cluster
ecosystem
Risk Score
security posture
Maturity
eng. profile
▸ AI Intelligence Summary
Analyzing...
Technology Detection Matrix
0 detected
SORT:
Min Confidence: 0%
Graph Relationship Engine
0 edges
Signal Evidence Explorer
No signals loaded.
AI Interpretation Layer
No analysis loaded.
Infrastructure
Security Intelligence
SEO Intelligence
Cluster Classification
Performance Diagnostics
Raw JSON Inspector
engine_response.json
// Run a scan to view raw output
Domain Alpha
— technologies
—%
stack similarity
Domain Beta
— technologies
Technology Stack Comparison Matrix
⬤ Shared Stack (Overlap)
Infrastructure Comparison
Raw JSON
// Run compare to view output
▸ System Telemetry
Live runtime state snapshot from EIGE v10 intelligence engine.
Global Graph Intelligence Explorer
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Detection Logic

EIGE v10 cross-references HTTP response headers, inline DOM signals, and external script fingerprints against a multi-category pattern library to assign each detected technology a normalized confidence score across 200+ named technology patterns.

Methodology

Each technology match is independently scored across header evidence, HTML markers, and script fingerprint channels, then combined into a single normalized confidence value between 0 and 1, with technologies below the minimum threshold suppressed from primary output.

References

Sources informing EIGE v10's detection methodology.