The World Before NexusIntel
Every organization sits on a mountain of unstructured documents. PDFs, emails, web pages, research papers — each containing valuable knowledge, yet isolated from one another. Teams search for answers across silos, never finding what they need.
THE INDUSTRY FRICTION
The Problem
NexusIntel bridges the gap between neural embeddings and symbolic logic. It creates a multi-layered relational vector space where AI agents can traverse entity linkages and execute multi-hop logical deductions. Unlike traditional vector search that retrieves flat documents, NexusIntel understands connections between concepts, enabling deep factual reasoning without hallucination.
THE METHODOLOGY
The Solution
Multi-hop traversals execute in under 8ms p99 across billion-node graphs. The hybrid Neo4j+Pinecone architecture ensures both relational precision and semantic recall. Horizontal scaling is achieved via graph partitioning shards.
"What if AI could think in connections, not just keywords? What if every fact existed within a web of verified relationships?"
That single question led to years of engineering, countless iterations, and a fundamental rethinking of how ai knowledge graph core should work.
Why NexusIntel Exists
From Idea to Execution
NexusIntel ingests documents through multi-format parsers, extracts entities and their relationships using NLP models, and stores both dense vectors (for similarity) and graph links (for traversal) in a hybrid Neo4j-Pinecone architecture.
Queries traverse both systems simultaneously.
Experience Features
Receiving query
Enables complex relational queries that would take traditional databases minutes — completed in milliseconds.
Interactive Architecture
Click a node to see details
Architecture Graph for Overview
Documents
PDFs, web pages, emails, markdown, and structured data sources ingested through multi-format parsers.
Entity Extraction
NLP models extract entities, relationships, and metadata from unstructured text content.
Knowledge Graph
Neo4j graph database stores typed entity relationships for multi-hop traversal queries.
Vector Store
Pinecone vector database indexes dense semantic embeddings for similarity search.
Hybrid Query
Unified query engine that combines graph traversal and vector similarity in a single request.
Engineered with purpose.
Every technology has been selected for a specific reason. Nothing exists by accident. Our stack is built on longevity, maintainability, performance, and developer experience — not trends.
Click a technology to explore its engineering rationale
Performance
Security & Compliance
All graph data is encrypted at rest and in transit. Access is controlled via API key authentication with granular role-based permissions. Query logs are anonymized after 30 days. SOC 2 Type II compliant.
Product Roadmap
Neo4j integration with Pinecone indexes
Sub-graph retrieval querying models
Multi-modal entity parsing (images & charts)
Real-time graph streaming updates
Frequently Asked Questions
Related Products
Launch NexusIntel
NexusIntel parses complex files, maps relational entities, and indexes them into a unified knowledge graph.