Ori Castle
Ori Castle is an AI innovation studio that designs and ships production-grade multi-agent systems for enterprises requiring accuracy, security, and scale. The studio builds coordinated agent teams using LangGraph with adversarial review loops, and implements GraphRAG with hybrid vector retrieval for knowledge-intensive use cases. Its privacy-first architecture ensures PII never reaches LLMs, with support for on-premise Ollama deployments.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Legal Technology
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Enterprises struggle to move beyond fragile single-prompt AI solutions, lacking the architectural expertise to build coordinated agent systems that integrate with their tools, APIs, and internal workflows. Generic AI implementations often fail to meet requirements for accuracy, security, and scale, while off-the-shelf products cannot adapt to domain-specific needs such as legal research or specialist coaching.
Solution
Ori Castle designs and delivers production-grade AI agent systems for teams requiring precision and reliability. The studio builds coordinated multi-agent teams using LangGraph that use client tools, call APIs, and refine each other's work through adversarial review loops. For knowledge-intensive use cases, it implements GraphRAG and hybrid vector retrieval to surface hidden relationships and trace entity connections across documents. Ori Castle manages the full journey from rapid proof-of-concept validation to production rollout on Azure using the same architecture, eliminating throwaway demos. Its deployments are privacy-first, with architectures that ensure PII never reaches LLMs, and include Ollama support for on-premise deployments where data sovereignty requires it.
Target Audience
Primary customers are enterprise teams needing accurate, secure, and scalable AI agent systems, including legal departments requiring specialized case-law retrieval and organizations seeking domain-specific coaching or data discovery capabilities.
Features
- Multi-agent orchestration with LangGraph, ReAct, adversarial review loops, and MCP for coordinated AI workflows
- GraphRAG and hybrid retrieval with Neo4j, vector search, and re-ranking to surface relationships across enterprise data
- Privacy-first architecture ensuring PII never reaches LLMs, with support for on-premise Ollama deployments
- Cloud-native delivery on Azure with infrastructure-as-code, scaling from proof-of-concept to production without architecture changes
- Specialized legal AI platform with 4 dedicated retrieval pipelines for UK case law and hybrid graph-plus-vector retrieval
- 9 specialist coaching agents in FriendCircle, each with built-in quality assurance through adversarial review