Aegis Insight - See What Your AI Cannot

AI Retrieval Is Blind to Structure

Your AI finds relevant content. It doesn't understand the landscape that content comes from.

When you query a RAG system about a contested topic, it returns the top-K most relevant chunks. What it cannot tell you:

That the results come from one of three competing schools of thought that rarely cite each other
Whether the apparent agreement across sources is organic or coordinated
Which credentialed voices have been systematically excluded from the discourse
What claims might bridge the gap between opposing perspectives

This is the difference between retrieval and understanding.

Traditional RAG asks: "What matches this query?"

                        Aegis Insight answers:
                        "What does the knowledge landscape look like?"
                    

What Becomes Possible

When your AI can see the shape of knowledge, new capabilities emerge

Multi-Perspective Awareness

Your AI knows when a topic is contested and presents multiple viewpoints—because it can see they exist, not because you told it to hedge.

Research Synthesis at Scale

See where fields actually disagree, which sources bridge perspectives, and which credentialed voices are being overlooked—across thousands of documents.

Grounded Confidence

Your AI responds with nuance on contested topics and certainty on settled ones—because it knows the difference structurally, not statistically.

Epistemic Risk Visibility

Detect manufactured consensus, coordinated messaging, and systematic exclusion patterns before they compromise your AI's outputs.

How It Works

Rich extraction enables topology analysis that flat embeddings cannot achieve

Traditional RAG	With Aegis Insight
Returns 10 similar chunks	Reveals 3 perspective clusters with relationship mapping
No context on agreement	Shows organic vs. coordinated consensus
Misses marginalized sources	Surfaces overlooked credentialed voices
AI is overly confident	AI responds with appropriate nuance
No bridging insights	Identifies claims that connect opposing views

The Extraction Pipeline

Documents pass through seven-dimensional extraction, creating the rich substrate required for topology analysis:

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Documents

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🔬

7-Dimension Extraction

Claims, entities, temporal, geographic, citations, emotional, authority

→

🕸️

Knowledge Graph

Neo4j + pgvector

→

🗺️

Topology Analysis

Clusters, bridges, detection scores

→

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Your AI

MCP or REST API

Pattern Detection

Once you can see what organic knowledge topology looks like, you can recognize when it looks artificial:

Suppression Detection

Identifies systematic marginalization—high-credential sources with low visibility, primary research ignored by meta-claims, citation network isolation despite topical relevance.

Coordination Detection

Identifies synchronized messaging—temporal clustering of publication, linguistic similarity across sources, network density patterns suggesting organized origin.

Anomaly Detection

Identifies structural outliers—unusual claim type distributions, authority-visibility inversions, citation patterns that suggest non-organic dynamics.

Goldfinger Scoring: Multiple weak signals combine into strong indicators. "Once is happenstance, twice is coincidence, three times is enemy action." Each detector uses non-linear threshold fusion calibrated against historical ground truth.

Architecture

Local-first processing with dual-store architecture for topology and similarity

Data Ingestion Layer

Document processing pipeline supporting PDF, HTML, plaintext, and structured data formats. Chunking strategies optimized for epistemic extraction with configurable overlap and boundary detection.

Extraction Layer

Local LLM processing via Ollama (mistral-nemo, qwen, or custom models). Seven-dimensional extraction with configurable prompts and validation rules. Checkpoint/resume capability for large corpus processing.

Storage Layer

Dual-store architecture: Neo4j knowledge graph for topology and relationships, PostgreSQL with pgvector for semantic embeddings and similarity search. Entity resolution and coreference handling.

Detection Layer

Configurable detection algorithms with domain-specific calibration profiles. Non-linear signal fusion via Goldfinger scoring. Comprehensive result attribution and confidence reporting.

Interface Layer

REST API for programmatic access, MCP endpoints for AI system integration, web interface for interactive analysis and administration. Full OpenAPI specification available.

Integration Options

Add epistemic awareness to your AI without changing your stack

Recommended

MCP Protocol Integration

Model Context Protocol endpoints enabling direct integration with Claude, custom AI assistants, and LLM-powered applications. Query epistemic context mid-conversation.

Protocol MCP 1.0

Latency < 200ms

Auth Token-based

REST API

Full-featured REST API with OpenAPI 3.0 specification. Supports all platform capabilities including search, detection queries, and administrative functions.

Spec OpenAPI 3.0

Format JSON

Auth API Key / OAuth

On-Premises Deployment

Containerized deployment via Docker Compose or Kubernetes. All processing remains within your infrastructure. Suitable for air-gapped environments with sensitive data.

Container Docker / K8s

GPU NVIDIA CUDA

Data Fully Local

Services

Consulting and implementation support tailored to your requirements

Integration Consulting

Expert guidance on integrating Aegis Insight with your existing AI infrastructure. Includes architecture review, integration design, and implementation support.

Architecture assessment
Integration design document
Implementation guidance
Testing and validation support

Custom Detection Profiles

Development of domain-specific detection configurations calibrated to your data set and use case. Includes threshold tuning, validation testing, and documentation.

Domain analysis
Profile development
Calibration and testing
Ongoing refinement

Training & Enablement

Technical training for your team on platform capabilities, administration, and best practices. Available in workshop or ongoing advisory formats.

Technical deep-dives
Hands-on workshops
Best practices documentation
Q&A sessions

AI Security Assessment

Comprehensive review of your AI/ML pipeline architecture with recommendations for epistemic risk management and security hardening.

Current state assessment
Epistemic risk analysis
Integration recommendations
Roadmap development

See What Your AI Cannot