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Overview

Neuradex is a Context as a Service (CaaS) platform that delivers optimal context to AI agents and LLMs. Traditional AI applications lose context when sessions end and cannot leverage past conversations or accumulated knowledge. Neuradex unifies memory, knowledge, and conversation history into context, providing a mechanism for AI to access the right information, at the right time.

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Vision

A world where you don’t have to wonder “Can I trust this?”
We’re building a world where people can make decisions with confidence. Neuradex gives AI organizational memory.
  • Shared knowledge - Your entire team and multiple AI agents access the same memory
  • Compounding asset - The more you use it, the more knowledge accumulates, becoming a competitive advantage
  • Your knowledge assets - “Own” your knowledge base and build AI that leverages it

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Why Neuradex

Context construction, management, and optimization — all as a service.
Achieving this vision requires infrastructure to deliver the right context to AI. But building a RAG system from scratch requires implementing many complex components: vector databases, index management, relevance scoring, context optimization, and more. As a CaaS platform, Neuradex provides all these complex features ready to use:
  • Zero Infrastructure Setup - No need to build or manage vector DBs, graph DBs, or any infrastructure. Start immediately on Neuradex’s cloud
  • Organization-wide Shared Memory - A unified knowledge base accessible by your entire team
  • Intelligent Knowledge Indexing - Automatic indexing optimized for semantic search
  • Automatic Knowledge Linking - Automatically detect and link related knowledge
  • Unanswered Question Tracking - Record unanswered questions to identify knowledge gaps
  • Token Budget Management - Context assembly optimized for LLM context windows
With just a few lines of code, you can integrate all these features into your application through the SDK.
Build once, benefit from improvements forever
If you build RAG yourself, improving accuracy requires continuous research and implementation. Tuning vector search, improving reranking, adopting new retrieval methods—you have to do it all yourself. This takes precious resources away from your core product development. When you build RAG on Neuradex:
  • Latest search technology applied automatically - Platform improvements are automatically reflected in your application
  • No R&D costs - Neuradex handles the research and implementation for accuracy improvements
  • Future innovations included - New technologies become available without additional work
Focus on your core product development and leave RAG accuracy improvement to Neuradex. As long as we continue our research, your application evolves alongside us.

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Context Folding

Neuradex is powered by our proprietary Context Folding technology. Traditional RAG systems have fundamental limitations:
  • Context window ceiling - Passing all search results to the LLM quickly hits the limit
  • Lost in the Middle problem - Important information gets buried in noise
  • Token cost explosion - Wasting tokens on irrelevant information
Context Folding solves these problems at their core. The detailed algorithm is proprietary, but the results speak for themselves:

68% Token Efficiency

Achieve the same quality answers with significantly fewer tokens

2-3x Processing Capacity

Process 2-3x more documents within the same context window

Reduced Hallucinations

Generate answers using only highly relevant information, improving accuracy

Natural Conversation Flow

Maintain context across multiple turns for consistent responses
No configuration required. Simply build RAG applications with Neuradex and automatically receive the benefits of Context Folding. Just call getContext() to receive optimized context.

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Key Features

Chat API

Chat Completions with automatic memory injection. Automatically pass knowledge to LLMs and auto-execute tools. OpenAI SDK-compatible interface

Knowledge Graph

Store and search structured knowledge. Semantic search with automatic relationship graphs that detect connections between knowledge (references, extends, contradicts, supersedes)

Memory

Combines vector search, graph traversal, and episode search to automatically assemble optimal context within token budgets

Agentic Search

Unified search across knowledge, episodes, topics, entities, and relationships. Explore all data layers with a single query

Episodes & Topics

Record events and conversation history chronologically. Related episodes are automatically grouped into topics with summaries

Entity Graph

Automatically extract people, companies, products, and more. Alias resolution and 13 relationship types structure your organization’s knowledge

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Chat API

An OpenAI SDK-style interface where your knowledge base is automatically injected as context into Chat Completions — no RAG pipeline required. Supports automatic tool execution: just define an execute function and the LLM autonomously calls tools to build agents. See the Chat API documentation for usage examples.

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Knowledge Graph

Store and search structured knowledge with automatic semantic indexing and relationship detection. Relationships between knowledge (references / extends / contradicts / supersedes / related / derived_from) are automatically detected, tracking the evolution of your knowledge. See the Knowledge API documentation for details.

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Memory

Combines vector search, graph traversal, and episode search to automatically assemble optimal context for queries with token budget management. Proprietary multi-stage scoring and optimization ensures only the most relevant information is selected. See the Memory API documentation for details.

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Episodes & Topics

Record events and conversation history chronologically for change tracking and session management. When multiple episodes are related, they’re automatically grouped into topics with generated summaries. See the Episodes API documentation for details.

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Multi-Provider LLM

Neuradex supports multiple LLM providers, letting you choose the optimal model for each use case.

OpenAI

GPT-4o, GPT-4o-mini

Anthropic

Claude 4 Opus, Sonnet

Google

Gemini Pro, Flash

Groq

Optimized for fast inference

Fireworks

Optimized inference

xAI

Grok
Configure models per project, and use different models for different actions — chat, embeddings, knowledge extraction, and more.

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Integrations

SDK

TypeScript / JavaScript SDK. Install from npm and start building immediately

MCP

Model Context Protocol support. Operate knowledge directly from Claude, VS Code, and other AI tools

Slack

Connect your Slack workspace. Automatically extract and accumulate knowledge from conversations

Widget

Embeddable chat widget for websites. Deploy with just a few lines of code

React

Build rich chat UIs instantly with the useChat hook

REST API

Full-featured REST API. Accessible from any platform

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Use Cases

Simply enable the Chat API’s memory option to build a complete RAG system powered by your knowledge base.The SDK handles context retrieval, prompt construction, and LLM calls in one step — no pipeline assembly required.

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Next Steps

Quickstart

Register your first knowledge in 5 minutes

Chat API

Chat Completions with memory

API Reference

Detailed SDK API documentation