Entidex
EntidexAI Narrative Intelligence
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EntidexEntidex

AI narrative intelligence platform. Monitor how ChatGPT, Gemini, and Claude describe your brand.

Powered by the Milo Says Observatory

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© 2026 Entidex. All rights reserved. AI narrative signals are observational, not definitive.

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  2. Pipeline

Entity Intelligence Pipeline

From raw signals to actionable intelligence — 6 stages, 5 learning loops, and continuous feedback powering the Entidex entity observatory.

847M+12%
Entities profiled
2.3B+8%
Relationships mapped
156+4
Sources integrated
23ms−15%
Avg latency
How Entidex and Milo Says work together
Entidex

The private intelligence engine. Entidex collects signals from 15+ sources, resolves entities to canonical identities, builds evidence graphs, and runs 5 continuous learning loops to produce deep entity intelligence.

CollectionIdentificationResolutionEnrichmentGraphLearning
powers
Milo Says

The public AI visibility observatory. Milo Says surfaces visibility scores, sentiment signals, recommendation presence, and narrative themes — all derived from Entidex intelligence.

ScoresSentimentRecommendationsNarrativesProfilesTrends
Entidex orchestrates 15+ collectors across structured & unstructured sources
Entidex's recognition engine classifies and delineates entities at scale
Entidex resolves surface variations to canonical identities with KB linking
Entidex fuses knowledge from multiple sources and tracks attribute evolution
Entidex maintains the entity graph — co-mentions, recommendations & influence edges
Entidex powers the intelligence APIs — Milo Says surfaces scores, sentiment & narratives to the public

Continuous Learning Engine

Entidex

5 autonomous loops driving self-improving intelligence — Entidex learns from every observation cycle

Cross-Stage Feedback Loops
User interaction patterns feed back to improve recognition accuracy. Click-through rates and dwell time as implicit labels.
Unresolved entities trigger targeted data collection. Priority queue based on business value scoring.
Graph structure reveals missing attributes. Neighbour-based imputation with confidence bounds.
Enriched attributes improve matching precision. Iterative refinement until convergence.
New data triggers re-scoring and re-interpretation. Staleness decay with configurable half-life.
End-to-End Data Flow
Data Collection
Entity Identification
Entity Resolution
Entity Enrichment
Relationship Graph
Intelligence Layer
5 learning loops feed continuously back across stages
Entidex
Milo Says